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# Directory Structure
```
├── .devcontainer
│ ├── .vscode
│ │ └── settings.json
│ ├── devcontainer.json
│ ├── OPTIMIZING_FOR_CODESPACES.md
│ ├── POST_SETUP_README.md
│ └── README.md
├── .dockerignore
├── .gitattributes
├── .github
│ ├── policheck.yml
│ └── workflows
│ ├── assistants-codespace-assistant.yml
│ ├── assistants-document-assistant.yml
│ ├── assistants-explorer-assistant.yml
│ ├── assistants-guided-conversation-assistant.yml
│ ├── assistants-knowledge-transfer-assistant.yml
│ ├── assistants-navigator-assistant.yml
│ ├── assistants-project-assistant.yml
│ ├── assistants-prospector-assistant.yml
│ ├── assistants-skill-assistant.yml
│ ├── libraries.yml
│ ├── mcp-server-giphy.yml
│ ├── mcp-server-memory-filesystem-edit.yml
│ ├── mcp-server-memory-user-bio.yml
│ ├── mcp-server-memory-whiteboard.yml
│ ├── mcp-server-open-deep-research-clone.yml
│ ├── mcp-server-web-research.yml
│ ├── workbench-app.yml
│ └── workbench-service.yml
├── .gitignore
├── .multi-root-tools
│ ├── Makefile
│ └── README.md
├── .vscode
│ ├── extensions.json
│ ├── launch.json
│ └── settings.json
├── ai_context
│ └── generated
│ ├── ASPIRE_ORCHESTRATOR.md
│ ├── ASSISTANT_CODESPACE.md
│ ├── ASSISTANT_DOCUMENT.md
│ ├── ASSISTANT_NAVIGATOR.md
│ ├── ASSISTANT_PROJECT.md
│ ├── ASSISTANT_PROSPECTOR.md
│ ├── ASSISTANTS_OTHER.md
│ ├── ASSISTANTS_OVERVIEW.md
│ ├── CONFIGURATION.md
│ ├── DOTNET_LIBRARIES.md
│ ├── EXAMPLES.md
│ ├── MCP_SERVERS.md
│ ├── PYTHON_LIBRARIES_AI_CLIENTS.md
│ ├── PYTHON_LIBRARIES_CORE.md
│ ├── PYTHON_LIBRARIES_EXTENSIONS.md
│ ├── PYTHON_LIBRARIES_SKILLS.md
│ ├── PYTHON_LIBRARIES_SPECIALIZED.md
│ ├── TOOLS.md
│ ├── WORKBENCH_FRONTEND.md
│ └── WORKBENCH_SERVICE.md
├── aspire-orchestrator
│ ├── .editorconfig
│ ├── Aspire.AppHost
│ │ ├── .gitignore
│ │ ├── appsettings.json
│ │ ├── Aspire.AppHost.csproj
│ │ ├── Program.cs
│ │ └── Properties
│ │ └── launchSettings.json
│ ├── Aspire.Extensions
│ │ ├── Aspire.Extensions.csproj
│ │ ├── Dashboard.cs
│ │ ├── DockerFileExtensions.cs
│ │ ├── PathNormalizer.cs
│ │ ├── UvAppHostingExtensions.cs
│ │ ├── UvAppResource.cs
│ │ ├── VirtualEnvironment.cs
│ │ └── WorkbenchServiceHostingExtensions.cs
│ ├── Aspire.ServiceDefaults
│ │ ├── Aspire.ServiceDefaults.csproj
│ │ └── Extensions.cs
│ ├── README.md
│ ├── run.sh
│ ├── SemanticWorkbench.Aspire.sln
│ └── SemanticWorkbench.Aspire.sln.DotSettings
├── assistants
│ ├── codespace-assistant
│ │ ├── .claude
│ │ │ └── settings.local.json
│ │ ├── .env.example
│ │ ├── .vscode
│ │ │ ├── extensions.json
│ │ │ ├── launch.json
│ │ │ └── settings.json
│ │ ├── assistant
│ │ │ ├── __init__.py
│ │ │ ├── assets
│ │ │ │ ├── icon_context_transfer.svg
│ │ │ │ └── icon.svg
│ │ │ ├── chat.py
│ │ │ ├── config.py
│ │ │ ├── helpers.py
│ │ │ ├── response
│ │ │ │ ├── __init__.py
│ │ │ │ ├── completion_handler.py
│ │ │ │ ├── models.py
│ │ │ │ ├── request_builder.py
│ │ │ │ ├── response.py
│ │ │ │ ├── step_handler.py
│ │ │ │ └── utils
│ │ │ │ ├── __init__.py
│ │ │ │ ├── abbreviations.py
│ │ │ │ ├── formatting_utils.py
│ │ │ │ ├── message_utils.py
│ │ │ │ └── openai_utils.py
│ │ │ ├── text_includes
│ │ │ │ ├── card_content_context_transfer.md
│ │ │ │ ├── card_content.md
│ │ │ │ ├── codespace_assistant_info.md
│ │ │ │ ├── context_transfer_assistant_info.md
│ │ │ │ ├── guardrails_prompt.txt
│ │ │ │ ├── guidance_prompt_context_transfer.txt
│ │ │ │ ├── guidance_prompt.txt
│ │ │ │ ├── instruction_prompt_context_transfer.txt
│ │ │ │ └── instruction_prompt.txt
│ │ │ └── whiteboard
│ │ │ ├── __init__.py
│ │ │ ├── _inspector.py
│ │ │ └── _whiteboard.py
│ │ ├── assistant.code-workspace
│ │ ├── Makefile
│ │ ├── pyproject.toml
│ │ ├── README.md
│ │ └── uv.lock
│ ├── document-assistant
│ │ ├── .env.example
│ │ ├── .vscode
│ │ │ ├── launch.json
│ │ │ └── settings.json
│ │ ├── assistant
│ │ │ ├── __init__.py
│ │ │ ├── assets
│ │ │ │ └── icon.svg
│ │ │ ├── chat.py
│ │ │ ├── config.py
│ │ │ ├── context_management
│ │ │ │ ├── __init__.py
│ │ │ │ └── inspector.py
│ │ │ ├── filesystem
│ │ │ │ ├── __init__.py
│ │ │ │ ├── _convert.py
│ │ │ │ ├── _file_sources.py
│ │ │ │ ├── _filesystem.py
│ │ │ │ ├── _inspector.py
│ │ │ │ ├── _model.py
│ │ │ │ ├── _prompts.py
│ │ │ │ └── _tasks.py
│ │ │ ├── guidance
│ │ │ │ ├── __init__.py
│ │ │ │ ├── dynamic_ui_inspector.py
│ │ │ │ ├── guidance_config.py
│ │ │ │ ├── guidance_prompts.py
│ │ │ │ └── README.md
│ │ │ ├── response
│ │ │ │ ├── __init__.py
│ │ │ │ ├── completion_handler.py
│ │ │ │ ├── models.py
│ │ │ │ ├── prompts.py
│ │ │ │ ├── responder.py
│ │ │ │ └── utils
│ │ │ │ ├── __init__.py
│ │ │ │ ├── formatting_utils.py
│ │ │ │ ├── message_utils.py
│ │ │ │ ├── openai_utils.py
│ │ │ │ ├── tokens_tiktoken.py
│ │ │ │ └── workbench_messages.py
│ │ │ ├── text_includes
│ │ │ │ └── document_assistant_info.md
│ │ │ ├── types.py
│ │ │ └── whiteboard
│ │ │ ├── __init__.py
│ │ │ ├── _inspector.py
│ │ │ └── _whiteboard.py
│ │ ├── assistant.code-workspace
│ │ ├── CLAUDE.md
│ │ ├── Makefile
│ │ ├── pyproject.toml
│ │ ├── README.md
│ │ ├── tests
│ │ │ ├── __init__.py
│ │ │ ├── test_convert.py
│ │ │ └── test_data
│ │ │ ├── blank_image.png
│ │ │ ├── Formatting Test.docx
│ │ │ ├── sample_data.csv
│ │ │ ├── sample_data.xlsx
│ │ │ ├── sample_page.html
│ │ │ ├── sample_presentation.pptx
│ │ │ └── simple_pdf.pdf
│ │ └── uv.lock
│ ├── explorer-assistant
│ │ ├── .env.example
│ │ ├── .vscode
│ │ │ ├── launch.json
│ │ │ └── settings.json
│ │ ├── assistant
│ │ │ ├── __init__.py
│ │ │ ├── chat.py
│ │ │ ├── config.py
│ │ │ ├── helpers.py
│ │ │ ├── response
│ │ │ │ ├── __init__.py
│ │ │ │ ├── model.py
│ │ │ │ ├── response_anthropic.py
│ │ │ │ ├── response_openai.py
│ │ │ │ └── response.py
│ │ │ └── text_includes
│ │ │ └── guardrails_prompt.txt
│ │ ├── assistant.code-workspace
│ │ ├── Makefile
│ │ ├── pyproject.toml
│ │ ├── README.md
│ │ └── uv.lock
│ ├── guided-conversation-assistant
│ │ ├── .env.example
│ │ ├── .vscode
│ │ │ ├── launch.json
│ │ │ └── settings.json
│ │ ├── assistant
│ │ │ ├── __init__.py
│ │ │ ├── agents
│ │ │ │ ├── guided_conversation
│ │ │ │ │ ├── config.py
│ │ │ │ │ ├── definition.py
│ │ │ │ │ └── definitions
│ │ │ │ │ ├── er_triage.py
│ │ │ │ │ ├── interview.py
│ │ │ │ │ ├── patient_intake.py
│ │ │ │ │ └── poem_feedback.py
│ │ │ │ └── guided_conversation_agent.py
│ │ │ ├── chat.py
│ │ │ ├── config.py
│ │ │ └── text_includes
│ │ │ └── guardrails_prompt.txt
│ │ ├── assistant.code-workspace
│ │ ├── Makefile
│ │ ├── pyproject.toml
│ │ ├── README.md
│ │ └── uv.lock
│ ├── knowledge-transfer-assistant
│ │ ├── .claude
│ │ │ └── settings.local.json
│ │ ├── .env.example
│ │ ├── .vscode
│ │ │ ├── launch.json
│ │ │ └── settings.json
│ │ ├── assistant
│ │ │ ├── __init__.py
│ │ │ ├── agentic
│ │ │ │ ├── __init__.py
│ │ │ │ ├── analysis.py
│ │ │ │ ├── coordinator_support.py
│ │ │ │ └── team_welcome.py
│ │ │ ├── assets
│ │ │ │ ├── icon-knowledge-transfer.svg
│ │ │ │ └── icon.svg
│ │ │ ├── assistant.py
│ │ │ ├── common.py
│ │ │ ├── config.py
│ │ │ ├── conversation_clients.py
│ │ │ ├── conversation_share_link.py
│ │ │ ├── data.py
│ │ │ ├── domain
│ │ │ │ ├── __init__.py
│ │ │ │ ├── audience_manager.py
│ │ │ │ ├── information_request_manager.py
│ │ │ │ ├── knowledge_brief_manager.py
│ │ │ │ ├── knowledge_digest_manager.py
│ │ │ │ ├── learning_objectives_manager.py
│ │ │ │ └── share_manager.py
│ │ │ ├── files.py
│ │ │ ├── logging.py
│ │ │ ├── notifications.py
│ │ │ ├── respond.py
│ │ │ ├── storage_models.py
│ │ │ ├── storage.py
│ │ │ ├── string_utils.py
│ │ │ ├── text_includes
│ │ │ │ ├── assistant_info.md
│ │ │ │ ├── card_content.md
│ │ │ │ ├── coordinator_instructions.txt
│ │ │ │ ├── coordinator_role.txt
│ │ │ │ ├── knowledge_digest_instructions.txt
│ │ │ │ ├── knowledge_digest_prompt.txt
│ │ │ │ ├── share_information_request_detection.txt
│ │ │ │ ├── team_instructions.txt
│ │ │ │ ├── team_role.txt
│ │ │ │ └── welcome_message_generation.txt
│ │ │ ├── tools
│ │ │ │ ├── __init__.py
│ │ │ │ ├── base.py
│ │ │ │ ├── information_requests.py
│ │ │ │ ├── learning_objectives.py
│ │ │ │ ├── learning_outcomes.py
│ │ │ │ ├── progress_tracking.py
│ │ │ │ └── share_setup.py
│ │ │ ├── ui_tabs
│ │ │ │ ├── __init__.py
│ │ │ │ ├── brief.py
│ │ │ │ ├── common.py
│ │ │ │ ├── debug.py
│ │ │ │ ├── learning.py
│ │ │ │ └── sharing.py
│ │ │ └── utils.py
│ │ ├── CLAUDE.md
│ │ ├── docs
│ │ │ ├── design
│ │ │ │ ├── actions.md
│ │ │ │ └── inference.md
│ │ │ ├── DEV_GUIDE.md
│ │ │ ├── how-kta-works.md
│ │ │ ├── JTBD.md
│ │ │ ├── knowledge-transfer-goals.md
│ │ │ ├── learning_assistance.md
│ │ │ ├── notable_claude_conversations
│ │ │ │ ├── clarifying_quad_modal_design.md
│ │ │ │ ├── CLAUDE_PROMPTS.md
│ │ │ │ ├── transfer_state.md
│ │ │ │ └── trying_the_context_agent.md
│ │ │ └── opportunities-of-knowledge-transfer.md
│ │ ├── knowledge-transfer-assistant.code-workspace
│ │ ├── Makefile
│ │ ├── pyproject.toml
│ │ ├── README.md
│ │ ├── tests
│ │ │ ├── __init__.py
│ │ │ ├── test_artifact_loading.py
│ │ │ ├── test_inspector.py
│ │ │ ├── test_share_manager.py
│ │ │ ├── test_share_storage.py
│ │ │ ├── test_share_tools.py
│ │ │ └── test_team_mode.py
│ │ └── uv.lock
│ ├── Makefile
│ ├── navigator-assistant
│ │ ├── .env.example
│ │ ├── .vscode
│ │ │ ├── launch.json
│ │ │ └── settings.json
│ │ ├── assistant
│ │ │ ├── __init__.py
│ │ │ ├── assets
│ │ │ │ ├── card_content.md
│ │ │ │ └── icon.svg
│ │ │ ├── chat.py
│ │ │ ├── config.py
│ │ │ ├── helpers.py
│ │ │ ├── response
│ │ │ │ ├── __init__.py
│ │ │ │ ├── completion_handler.py
│ │ │ │ ├── completion_requestor.py
│ │ │ │ ├── local_tool
│ │ │ │ │ ├── __init__.py
│ │ │ │ │ ├── add_assistant_to_conversation.py
│ │ │ │ │ ├── list_assistant_services.py
│ │ │ │ │ └── model.py
│ │ │ │ ├── models.py
│ │ │ │ ├── prompt.py
│ │ │ │ ├── request_builder.py
│ │ │ │ ├── response.py
│ │ │ │ ├── step_handler.py
│ │ │ │ └── utils
│ │ │ │ ├── __init__.py
│ │ │ │ ├── formatting_utils.py
│ │ │ │ ├── message_utils.py
│ │ │ │ ├── openai_utils.py
│ │ │ │ └── tools.py
│ │ │ ├── text_includes
│ │ │ │ ├── guardrails_prompt.md
│ │ │ │ ├── guidance_prompt.md
│ │ │ │ ├── instruction_prompt.md
│ │ │ │ ├── navigator_assistant_info.md
│ │ │ │ └── semantic_workbench_features.md
│ │ │ └── whiteboard
│ │ │ ├── __init__.py
│ │ │ ├── _inspector.py
│ │ │ └── _whiteboard.py
│ │ ├── Makefile
│ │ ├── pyproject.toml
│ │ ├── README.md
│ │ └── uv.lock
│ ├── project-assistant
│ │ ├── .cspell
│ │ │ └── custom-dictionary-workspace.txt
│ │ ├── .env.example
│ │ ├── .vscode
│ │ │ ├── launch.json
│ │ │ └── settings.json
│ │ ├── assistant
│ │ │ ├── __init__.py
│ │ │ ├── agentic
│ │ │ │ ├── __init__.py
│ │ │ │ ├── act.py
│ │ │ │ ├── coordinator_next_action.py
│ │ │ │ ├── create_invitation.py
│ │ │ │ ├── detect_audience_and_takeaways.py
│ │ │ │ ├── detect_coordinator_actions.py
│ │ │ │ ├── detect_information_request_needs.py
│ │ │ │ ├── detect_knowledge_package_gaps.py
│ │ │ │ ├── focus.py
│ │ │ │ ├── respond.py
│ │ │ │ ├── team_welcome.py
│ │ │ │ └── update_digest.py
│ │ │ ├── assets
│ │ │ │ ├── icon-knowledge-transfer.svg
│ │ │ │ └── icon.svg
│ │ │ ├── assistant.py
│ │ │ ├── common.py
│ │ │ ├── config.py
│ │ │ ├── conversation_clients.py
│ │ │ ├── data.py
│ │ │ ├── domain
│ │ │ │ ├── __init__.py
│ │ │ │ ├── audience_manager.py
│ │ │ │ ├── conversation_preferences_manager.py
│ │ │ │ ├── information_request_manager.py
│ │ │ │ ├── knowledge_brief_manager.py
│ │ │ │ ├── knowledge_digest_manager.py
│ │ │ │ ├── learning_objectives_manager.py
│ │ │ │ ├── share_manager.py
│ │ │ │ ├── tasks_manager.py
│ │ │ │ └── transfer_manager.py
│ │ │ ├── errors.py
│ │ │ ├── files.py
│ │ │ ├── logging.py
│ │ │ ├── notifications.py
│ │ │ ├── prompt_utils.py
│ │ │ ├── storage.py
│ │ │ ├── string_utils.py
│ │ │ ├── text_includes
│ │ │ │ ├── actor_instructions.md
│ │ │ │ ├── assistant_info.md
│ │ │ │ ├── card_content.md
│ │ │ │ ├── coordinator_instructions copy.md
│ │ │ │ ├── coordinator_instructions.md
│ │ │ │ ├── create_invitation.md
│ │ │ │ ├── detect_audience.md
│ │ │ │ ├── detect_coordinator_actions.md
│ │ │ │ ├── detect_information_request_needs.md
│ │ │ │ ├── detect_knowledge_package_gaps.md
│ │ │ │ ├── focus.md
│ │ │ │ ├── knowledge_digest_instructions.txt
│ │ │ │ ├── team_instructions.txt
│ │ │ │ ├── to_do.md
│ │ │ │ ├── update_knowledge_brief.md
│ │ │ │ ├── update_knowledge_digest.md
│ │ │ │ └── welcome_message_generation.txt
│ │ │ ├── tools
│ │ │ │ ├── __init__.py
│ │ │ │ ├── base.py
│ │ │ │ ├── conversation_preferences.py
│ │ │ │ ├── information_requests.py
│ │ │ │ ├── learning_objectives.py
│ │ │ │ ├── learning_outcomes.py
│ │ │ │ ├── progress_tracking.py
│ │ │ │ ├── share_setup.py
│ │ │ │ ├── system_reminders.py
│ │ │ │ ├── tasks.py
│ │ │ │ └── todo.py
│ │ │ ├── ui_tabs
│ │ │ │ ├── __init__.py
│ │ │ │ ├── brief.py
│ │ │ │ ├── common.py
│ │ │ │ ├── debug.py
│ │ │ │ ├── learning.py
│ │ │ │ └── sharing.py
│ │ │ └── utils.py
│ │ ├── CLAUDE.md
│ │ ├── docs
│ │ │ ├── design
│ │ │ │ ├── actions.md
│ │ │ │ ├── control_options.md
│ │ │ │ ├── design.md
│ │ │ │ ├── inference.md
│ │ │ │ └── PXL_20250814_190140267.jpg
│ │ │ ├── DEV_GUIDE.md
│ │ │ ├── how-kta-works.md
│ │ │ ├── JTBD.md
│ │ │ ├── knowledge-transfer-goals.md
│ │ │ ├── learning_assistance.md
│ │ │ ├── notable_claude_conversations
│ │ │ │ ├── clarifying_quad_modal_design.md
│ │ │ │ ├── CLAUDE_PROMPTS.md
│ │ │ │ ├── transfer_state.md
│ │ │ │ └── trying_the_context_agent.md
│ │ │ └── opportunities-of-knowledge-transfer.md
│ │ ├── knowledge-transfer-assistant.code-workspace
│ │ ├── Makefile
│ │ ├── pyproject.toml
│ │ ├── README.md
│ │ ├── tests
│ │ │ ├── __init__.py
│ │ │ ├── test_artifact_loading.py
│ │ │ ├── test_inspector.py
│ │ │ ├── test_share_manager.py
│ │ │ ├── test_share_storage.py
│ │ │ └── test_team_mode.py
│ │ └── uv.lock
│ ├── prospector-assistant
│ │ ├── .env.example
│ │ ├── .vscode
│ │ │ ├── launch.json
│ │ │ └── settings.json
│ │ ├── assistant
│ │ │ ├── __init__.py
│ │ │ ├── agents
│ │ │ │ ├── artifact_agent.py
│ │ │ │ ├── document
│ │ │ │ │ ├── config.py
│ │ │ │ │ ├── gc_draft_content_feedback_config.py
│ │ │ │ │ ├── gc_draft_outline_feedback_config.py
│ │ │ │ │ ├── guided_conversation.py
│ │ │ │ │ └── state.py
│ │ │ │ └── document_agent.py
│ │ │ ├── artifact_creation_extension
│ │ │ │ ├── __init__.py
│ │ │ │ ├── _llm.py
│ │ │ │ ├── config.py
│ │ │ │ ├── document.py
│ │ │ │ ├── extension.py
│ │ │ │ ├── store.py
│ │ │ │ ├── test
│ │ │ │ │ ├── conftest.py
│ │ │ │ │ ├── evaluation.py
│ │ │ │ │ ├── test_completion_with_tools.py
│ │ │ │ │ └── test_extension.py
│ │ │ │ └── tools.py
│ │ │ ├── chat.py
│ │ │ ├── config.py
│ │ │ ├── form_fill_extension
│ │ │ │ ├── __init__.py
│ │ │ │ ├── config.py
│ │ │ │ ├── extension.py
│ │ │ │ ├── inspector.py
│ │ │ │ ├── state.py
│ │ │ │ └── steps
│ │ │ │ ├── __init__.py
│ │ │ │ ├── _guided_conversation.py
│ │ │ │ ├── _llm.py
│ │ │ │ ├── acquire_form_step.py
│ │ │ │ ├── extract_form_fields_step.py
│ │ │ │ ├── fill_form_step.py
│ │ │ │ └── types.py
│ │ │ ├── helpers.py
│ │ │ ├── legacy.py
│ │ │ └── text_includes
│ │ │ ├── artifact_agent_enabled.md
│ │ │ ├── guardrails_prompt.txt
│ │ │ ├── guided_conversation_agent_enabled.md
│ │ │ └── skills_agent_enabled.md
│ │ ├── assistant.code-workspace
│ │ ├── gc_learnings
│ │ │ ├── gc_learnings.md
│ │ │ └── images
│ │ │ ├── gc_conversation_plan_fcn.png
│ │ │ ├── gc_conversation_plan_template.png
│ │ │ ├── gc_execute_plan_callstack.png
│ │ │ ├── gc_functions.png
│ │ │ ├── gc_generate_plan_callstack.png
│ │ │ ├── gc_get_resource_instructions.png
│ │ │ ├── gc_get_termination_instructions.png
│ │ │ ├── gc_kernel_arguments.png
│ │ │ ├── gc_plan_calls.png
│ │ │ ├── gc_termination_instructions.png
│ │ │ ├── sk_get_chat_message_contents.png
│ │ │ ├── sk_inner_get_chat_message_contents.png
│ │ │ ├── sk_send_request_prep.png
│ │ │ └── sk_send_request.png
│ │ ├── Makefile
│ │ ├── pyproject.toml
│ │ ├── README.md
│ │ └── uv.lock
│ └── skill-assistant
│ ├── .env.example
│ ├── .vscode
│ │ ├── launch.json
│ │ └── settings.json
│ ├── assistant
│ │ ├── __init__.py
│ │ ├── config.py
│ │ ├── logging.py
│ │ ├── skill_assistant.py
│ │ ├── skill_engine_registry.py
│ │ ├── skill_event_mapper.py
│ │ ├── text_includes
│ │ │ └── guardrails_prompt.txt
│ │ └── workbench_helpers.py
│ ├── assistant.code-workspace
│ ├── Makefile
│ ├── pyproject.toml
│ ├── README.md
│ ├── tests
│ │ └── test_setup.py
│ └── uv.lock
├── CLAUDE.md
├── CODE_OF_CONDUCT.md
├── CONTRIBUTING.md
├── docs
│ ├── .vscode
│ │ └── settings.json
│ ├── ASSISTANT_CONFIG.md
│ ├── ASSISTANT_DEVELOPMENT_GUIDE.md
│ ├── CUSTOM_APP_REGISTRATION.md
│ ├── HOSTED_ASSISTANT_WITH_LOCAL_MCP_SERVERS.md
│ ├── images
│ │ ├── architecture-animation.gif
│ │ ├── configure_assistant.png
│ │ ├── conversation_canvas_open.png
│ │ ├── conversation_duplicate.png
│ │ ├── conversation_export.png
│ │ ├── conversation_share_dialog.png
│ │ ├── conversation_share_link.png
│ │ ├── dashboard_configured_view.png
│ │ ├── dashboard_view.png
│ │ ├── license_agreement.png
│ │ ├── message_bar.png
│ │ ├── message_inspection.png
│ │ ├── message_link.png
│ │ ├── new_prospector_assistant_dialog.png
│ │ ├── open_conversation_canvas.png
│ │ ├── prospector_example.png
│ │ ├── readme1.png
│ │ ├── readme2.png
│ │ ├── readme3.png
│ │ ├── rewind.png
│ │ ├── signin_page.png
│ │ └── splash_screen.png
│ ├── LOCAL_ASSISTANT_WITH_REMOTE_WORKBENCH.md
│ ├── SETUP_DEV_ENVIRONMENT.md
│ └── WORKBENCH_APP.md
├── examples
│ ├── dotnet
│ │ ├── .editorconfig
│ │ ├── dotnet-01-echo-bot
│ │ │ ├── appsettings.json
│ │ │ ├── dotnet-01-echo-bot.csproj
│ │ │ ├── MyAgent.cs
│ │ │ ├── MyAgentConfig.cs
│ │ │ ├── MyWorkbenchConnector.cs
│ │ │ ├── Program.cs
│ │ │ └── README.md
│ │ ├── dotnet-02-message-types-demo
│ │ │ ├── appsettings.json
│ │ │ ├── ConnectorExtensions.cs
│ │ │ ├── docs
│ │ │ │ ├── abc.png
│ │ │ │ ├── code.png
│ │ │ │ ├── config.png
│ │ │ │ ├── echo.png
│ │ │ │ ├── markdown.png
│ │ │ │ ├── mermaid.png
│ │ │ │ ├── reverse.png
│ │ │ │ └── safety-check.png
│ │ │ ├── dotnet-02-message-types-demo.csproj
│ │ │ ├── MyAgent.cs
│ │ │ ├── MyAgentConfig.cs
│ │ │ ├── MyWorkbenchConnector.cs
│ │ │ ├── Program.cs
│ │ │ └── README.md
│ │ └── dotnet-03-simple-chatbot
│ │ ├── appsettings.json
│ │ ├── ConnectorExtensions.cs
│ │ ├── dotnet-03-simple-chatbot.csproj
│ │ ├── MyAgent.cs
│ │ ├── MyAgentConfig.cs
│ │ ├── MyWorkbenchConnector.cs
│ │ ├── Program.cs
│ │ └── README.md
│ ├── Makefile
│ └── python
│ ├── python-01-echo-bot
│ │ ├── .env.example
│ │ ├── .vscode
│ │ │ ├── launch.json
│ │ │ └── settings.json
│ │ ├── assistant
│ │ │ ├── __init__.py
│ │ │ ├── chat.py
│ │ │ └── config.py
│ │ ├── assistant.code-workspace
│ │ ├── Makefile
│ │ ├── pyproject.toml
│ │ ├── README.md
│ │ └── uv.lock
│ ├── python-02-simple-chatbot
│ │ ├── .env.example
│ │ ├── .vscode
│ │ │ ├── launch.json
│ │ │ └── settings.json
│ │ ├── assistant
│ │ │ ├── __init__.py
│ │ │ ├── chat.py
│ │ │ ├── config.py
│ │ │ └── text_includes
│ │ │ └── guardrails_prompt.txt
│ │ ├── assistant.code-workspace
│ │ ├── Makefile
│ │ ├── pyproject.toml
│ │ ├── README.md
│ │ └── uv.lock
│ └── python-03-multimodel-chatbot
│ ├── .env.example
│ ├── .vscode
│ │ ├── launch.json
│ │ └── settings.json
│ ├── assistant
│ │ ├── __init__.py
│ │ ├── chat.py
│ │ ├── config.py
│ │ ├── model_adapters.py
│ │ └── text_includes
│ │ └── guardrails_prompt.txt
│ ├── assistant.code-workspace
│ ├── Makefile
│ ├── pyproject.toml
│ ├── README.md
│ └── uv.lock
├── KNOWN_ISSUES.md
├── libraries
│ ├── dotnet
│ │ ├── .editorconfig
│ │ ├── pack.sh
│ │ ├── README.md
│ │ ├── SemanticWorkbench.sln
│ │ ├── SemanticWorkbench.sln.DotSettings
│ │ └── WorkbenchConnector
│ │ ├── AgentBase.cs
│ │ ├── AgentConfig
│ │ │ ├── AgentConfigBase.cs
│ │ │ ├── AgentConfigPropertyAttribute.cs
│ │ │ └── ConfigUtils.cs
│ │ ├── Constants.cs
│ │ ├── IAgentBase.cs
│ │ ├── icon.png
│ │ ├── Models
│ │ │ ├── Command.cs
│ │ │ ├── Conversation.cs
│ │ │ ├── ConversationEvent.cs
│ │ │ ├── DebugInfo.cs
│ │ │ ├── Insight.cs
│ │ │ ├── Message.cs
│ │ │ ├── MessageMetadata.cs
│ │ │ ├── Participant.cs
│ │ │ ├── Sender.cs
│ │ │ └── ServiceInfo.cs
│ │ ├── Storage
│ │ │ ├── AgentInfo.cs
│ │ │ ├── AgentServiceStorage.cs
│ │ │ └── IAgentServiceStorage.cs
│ │ ├── StringLoggingExtensions.cs
│ │ ├── Webservice.cs
│ │ ├── WorkbenchConfig.cs
│ │ ├── WorkbenchConnector.cs
│ │ └── WorkbenchConnector.csproj
│ ├── Makefile
│ └── python
│ ├── anthropic-client
│ │ ├── .vscode
│ │ │ └── settings.json
│ │ ├── anthropic_client
│ │ │ ├── __init__.py
│ │ │ ├── client.py
│ │ │ ├── config.py
│ │ │ └── messages.py
│ │ ├── Makefile
│ │ ├── pyproject.toml
│ │ ├── README.md
│ │ └── uv.lock
│ ├── assistant-data-gen
│ │ ├── .vscode
│ │ │ ├── launch.json
│ │ │ └── settings.json
│ │ ├── assistant_data_gen
│ │ │ ├── __init__.py
│ │ │ ├── assistant_api.py
│ │ │ ├── config.py
│ │ │ ├── gce
│ │ │ │ ├── __init__.py
│ │ │ │ ├── gce_agent.py
│ │ │ │ └── prompts.py
│ │ │ └── pydantic_ai_utils.py
│ │ ├── configs
│ │ │ └── document_assistant_example_config.yaml
│ │ ├── Makefile
│ │ ├── pyproject.toml
│ │ ├── README.md
│ │ ├── scripts
│ │ │ ├── gce_simulation.py
│ │ │ └── generate_scenario.py
│ │ └── uv.lock
│ ├── assistant-drive
│ │ ├── .gitignore
│ │ ├── .vscode
│ │ │ ├── extensions.json
│ │ │ └── settings.json
│ │ ├── assistant_drive
│ │ │ ├── __init__.py
│ │ │ ├── drive.py
│ │ │ └── tests
│ │ │ └── test_basic.py
│ │ ├── Makefile
│ │ ├── pyproject.toml
│ │ ├── pytest.ini
│ │ ├── README.md
│ │ ├── usage.ipynb
│ │ └── uv.lock
│ ├── assistant-extensions
│ │ ├── .vscode
│ │ │ └── settings.json
│ │ ├── assistant_extensions
│ │ │ ├── __init__.py
│ │ │ ├── ai_clients
│ │ │ │ └── config.py
│ │ │ ├── artifacts
│ │ │ │ ├── __init__.py
│ │ │ │ ├── _artifacts.py
│ │ │ │ ├── _inspector.py
│ │ │ │ └── _model.py
│ │ │ ├── attachments
│ │ │ │ ├── __init__.py
│ │ │ │ ├── _attachments.py
│ │ │ │ ├── _convert.py
│ │ │ │ ├── _model.py
│ │ │ │ ├── _shared.py
│ │ │ │ └── _summarizer.py
│ │ │ ├── chat_context_toolkit
│ │ │ │ ├── __init__.py
│ │ │ │ ├── _config.py
│ │ │ │ ├── archive
│ │ │ │ │ ├── __init__.py
│ │ │ │ │ ├── _archive.py
│ │ │ │ │ └── _summarizer.py
│ │ │ │ ├── message_history
│ │ │ │ │ ├── __init__.py
│ │ │ │ │ ├── _history.py
│ │ │ │ │ └── _message.py
│ │ │ │ └── virtual_filesystem
│ │ │ │ ├── __init__.py
│ │ │ │ ├── _archive_file_source.py
│ │ │ │ └── _attachments_file_source.py
│ │ │ ├── dashboard_card
│ │ │ │ ├── __init__.py
│ │ │ │ └── _dashboard_card.py
│ │ │ ├── document_editor
│ │ │ │ ├── __init__.py
│ │ │ │ ├── _extension.py
│ │ │ │ ├── _inspector.py
│ │ │ │ └── _model.py
│ │ │ ├── mcp
│ │ │ │ ├── __init__.py
│ │ │ │ ├── _assistant_file_resource_handler.py
│ │ │ │ ├── _client_utils.py
│ │ │ │ ├── _devtunnel.py
│ │ │ │ ├── _model.py
│ │ │ │ ├── _openai_utils.py
│ │ │ │ ├── _sampling_handler.py
│ │ │ │ ├── _tool_utils.py
│ │ │ │ └── _workbench_file_resource_handler.py
│ │ │ ├── navigator
│ │ │ │ ├── __init__.py
│ │ │ │ └── _navigator.py
│ │ │ └── workflows
│ │ │ ├── __init__.py
│ │ │ ├── _model.py
│ │ │ ├── _workflows.py
│ │ │ └── runners
│ │ │ └── _user_proxy.py
│ │ ├── Makefile
│ │ ├── pyproject.toml
│ │ ├── README.md
│ │ ├── test
│ │ │ └── attachments
│ │ │ └── test_attachments.py
│ │ └── uv.lock
│ ├── chat-context-toolkit
│ │ ├── .claude
│ │ │ └── settings.local.json
│ │ ├── .env.sample
│ │ ├── .vscode
│ │ │ └── settings.json
│ │ ├── assets
│ │ │ ├── archive_v1.png
│ │ │ ├── history_v1.png
│ │ │ └── vfs_v1.png
│ │ ├── chat_context_toolkit
│ │ │ ├── __init__.py
│ │ │ ├── archive
│ │ │ │ ├── __init__.py
│ │ │ │ ├── _archive_reader.py
│ │ │ │ ├── _archive_task_queue.py
│ │ │ │ ├── _state.py
│ │ │ │ ├── _types.py
│ │ │ │ └── summarization
│ │ │ │ ├── __init__.py
│ │ │ │ └── _summarizer.py
│ │ │ ├── history
│ │ │ │ ├── __init__.py
│ │ │ │ ├── _budget.py
│ │ │ │ ├── _decorators.py
│ │ │ │ ├── _history.py
│ │ │ │ ├── _prioritize.py
│ │ │ │ ├── _types.py
│ │ │ │ └── tool_abbreviations
│ │ │ │ ├── __init__.py
│ │ │ │ └── _tool_abbreviations.py
│ │ │ └── virtual_filesystem
│ │ │ ├── __init__.py
│ │ │ ├── _types.py
│ │ │ ├── _virtual_filesystem.py
│ │ │ ├── README.md
│ │ │ └── tools
│ │ │ ├── __init__.py
│ │ │ ├── _ls_tool.py
│ │ │ ├── _tools.py
│ │ │ └── _view_tool.py
│ │ ├── CLAUDE.md
│ │ ├── Makefile
│ │ ├── pyproject.toml
│ │ ├── README.md
│ │ ├── test
│ │ │ ├── archive
│ │ │ │ └── test_archive_reader.py
│ │ │ ├── history
│ │ │ │ ├── test_abbreviate_messages.py
│ │ │ │ ├── test_history.py
│ │ │ │ ├── test_pair_and_order_tool_messages.py
│ │ │ │ ├── test_prioritize.py
│ │ │ │ └── test_truncate_messages.py
│ │ │ └── virtual_filesystem
│ │ │ ├── test_virtual_filesystem.py
│ │ │ └── tools
│ │ │ ├── test_ls_tool.py
│ │ │ ├── test_tools.py
│ │ │ └── test_view_tool.py
│ │ └── uv.lock
│ ├── content-safety
│ │ ├── .vscode
│ │ │ └── settings.json
│ │ ├── content_safety
│ │ │ ├── __init__.py
│ │ │ ├── evaluators
│ │ │ │ ├── __init__.py
│ │ │ │ ├── azure_content_safety
│ │ │ │ │ ├── __init__.py
│ │ │ │ │ ├── config.py
│ │ │ │ │ └── evaluator.py
│ │ │ │ ├── config.py
│ │ │ │ ├── evaluator.py
│ │ │ │ └── openai_moderations
│ │ │ │ ├── __init__.py
│ │ │ │ ├── config.py
│ │ │ │ └── evaluator.py
│ │ │ └── README.md
│ │ ├── Makefile
│ │ ├── pyproject.toml
│ │ ├── README.md
│ │ └── uv.lock
│ ├── events
│ │ ├── .vscode
│ │ │ └── settings.json
│ │ ├── events
│ │ │ ├── __init__.py
│ │ │ └── events.py
│ │ ├── Makefile
│ │ ├── pyproject.toml
│ │ ├── README.md
│ │ └── uv.lock
│ ├── guided-conversation
│ │ ├── .vscode
│ │ │ └── settings.json
│ │ ├── guided_conversation
│ │ │ ├── __init__.py
│ │ │ ├── functions
│ │ │ │ ├── __init__.py
│ │ │ │ ├── conversation_plan.py
│ │ │ │ ├── execution.py
│ │ │ │ └── final_update_plan.py
│ │ │ ├── guided_conversation_agent.py
│ │ │ ├── plugins
│ │ │ │ ├── __init__.py
│ │ │ │ ├── agenda.py
│ │ │ │ └── artifact.py
│ │ │ └── utils
│ │ │ ├── __init__.py
│ │ │ ├── base_model_llm.py
│ │ │ ├── conversation_helpers.py
│ │ │ ├── openai_tool_calling.py
│ │ │ ├── plugin_helpers.py
│ │ │ └── resources.py
│ │ ├── Makefile
│ │ ├── pyproject.toml
│ │ ├── README.md
│ │ └── uv.lock
│ ├── llm-client
│ │ ├── .vscode
│ │ │ └── settings.json
│ │ ├── llm_client
│ │ │ ├── __init__.py
│ │ │ └── model.py
│ │ ├── Makefile
│ │ ├── pyproject.toml
│ │ ├── README.md
│ │ └── uv.lock
│ ├── Makefile
│ ├── mcp-extensions
│ │ ├── .vscode
│ │ │ └── settings.json
│ │ ├── Makefile
│ │ ├── mcp_extensions
│ │ │ ├── __init__.py
│ │ │ ├── _client_session.py
│ │ │ ├── _model.py
│ │ │ ├── _sampling.py
│ │ │ ├── _server_extensions.py
│ │ │ ├── _tool_utils.py
│ │ │ ├── llm
│ │ │ │ ├── __init__.py
│ │ │ │ ├── chat_completion.py
│ │ │ │ ├── helpers.py
│ │ │ │ ├── llm_types.py
│ │ │ │ ├── mcp_chat_completion.py
│ │ │ │ └── openai_chat_completion.py
│ │ │ └── server
│ │ │ ├── __init__.py
│ │ │ └── storage.py
│ │ ├── pyproject.toml
│ │ ├── README.md
│ │ ├── tests
│ │ │ └── test_tool_utils.py
│ │ └── uv.lock
│ ├── mcp-tunnel
│ │ ├── .vscode
│ │ │ └── settings.json
│ │ ├── Makefile
│ │ ├── mcp_tunnel
│ │ │ ├── __init__.py
│ │ │ ├── _devtunnel.py
│ │ │ ├── _dir.py
│ │ │ └── _main.py
│ │ ├── pyproject.toml
│ │ ├── README.md
│ │ └── uv.lock
│ ├── openai-client
│ │ ├── .vscode
│ │ │ └── settings.json
│ │ ├── Makefile
│ │ ├── openai_client
│ │ │ ├── __init__.py
│ │ │ ├── chat_driver
│ │ │ │ ├── __init__.py
│ │ │ │ ├── chat_driver.ipynb
│ │ │ │ ├── chat_driver.py
│ │ │ │ ├── message_history_providers
│ │ │ │ │ ├── __init__.py
│ │ │ │ │ ├── in_memory_message_history_provider.py
│ │ │ │ │ ├── local_message_history_provider.py
│ │ │ │ │ ├── message_history_provider.py
│ │ │ │ │ └── tests
│ │ │ │ │ └── formatted_instructions_test.py
│ │ │ │ └── README.md
│ │ │ ├── client.py
│ │ │ ├── completion.py
│ │ │ ├── config.py
│ │ │ ├── errors.py
│ │ │ ├── logging.py
│ │ │ ├── messages.py
│ │ │ ├── tokens.py
│ │ │ └── tools.py
│ │ ├── pyproject.toml
│ │ ├── README.md
│ │ ├── tests
│ │ │ ├── test_command_parsing.py
│ │ │ ├── test_formatted_messages.py
│ │ │ ├── test_messages.py
│ │ │ └── test_tokens.py
│ │ └── uv.lock
│ ├── semantic-workbench-api-model
│ │ ├── .vscode
│ │ │ └── settings.json
│ │ ├── Makefile
│ │ ├── pyproject.toml
│ │ ├── README.md
│ │ ├── semantic_workbench_api_model
│ │ │ ├── __init__.py
│ │ │ ├── assistant_model.py
│ │ │ ├── assistant_service_client.py
│ │ │ ├── workbench_model.py
│ │ │ └── workbench_service_client.py
│ │ └── uv.lock
│ ├── semantic-workbench-assistant
│ │ ├── .vscode
│ │ │ ├── launch.json
│ │ │ └── settings.json
│ │ ├── Makefile
│ │ ├── pyproject.toml
│ │ ├── README.md
│ │ ├── semantic_workbench_assistant
│ │ │ ├── __init__.py
│ │ │ ├── assistant_app
│ │ │ │ ├── __init__.py
│ │ │ │ ├── assistant.py
│ │ │ │ ├── config.py
│ │ │ │ ├── content_safety.py
│ │ │ │ ├── context.py
│ │ │ │ ├── error.py
│ │ │ │ ├── export_import.py
│ │ │ │ ├── protocol.py
│ │ │ │ └── service.py
│ │ │ ├── assistant_service.py
│ │ │ ├── auth.py
│ │ │ ├── canonical.py
│ │ │ ├── command.py
│ │ │ ├── config.py
│ │ │ ├── logging_config.py
│ │ │ ├── settings.py
│ │ │ ├── start.py
│ │ │ └── storage.py
│ │ ├── tests
│ │ │ ├── conftest.py
│ │ │ ├── test_assistant_app.py
│ │ │ ├── test_canonical.py
│ │ │ ├── test_config.py
│ │ │ └── test_storage.py
│ │ └── uv.lock
│ └── skills
│ ├── .vscode
│ │ └── settings.json
│ ├── Makefile
│ ├── README.md
│ └── skill-library
│ ├── .vscode
│ │ └── settings.json
│ ├── docs
│ │ └── vs-recipe-tool.md
│ ├── Makefile
│ ├── pyproject.toml
│ ├── README.md
│ ├── skill_library
│ │ ├── __init__.py
│ │ ├── chat_driver_helpers.py
│ │ ├── cli
│ │ │ ├── azure_openai.py
│ │ │ ├── conversation_history.py
│ │ │ ├── README.md
│ │ │ ├── run_routine.py
│ │ │ ├── settings.py
│ │ │ └── skill_logger.py
│ │ ├── engine.py
│ │ ├── llm_info.txt
│ │ ├── logging.py
│ │ ├── README.md
│ │ ├── routine_stack.py
│ │ ├── skill.py
│ │ ├── skills
│ │ │ ├── common
│ │ │ │ ├── __init__.py
│ │ │ │ ├── common_skill.py
│ │ │ │ └── routines
│ │ │ │ ├── bing_search.py
│ │ │ │ ├── consolidate.py
│ │ │ │ ├── echo.py
│ │ │ │ ├── gather_context.py
│ │ │ │ ├── get_content_from_url.py
│ │ │ │ ├── gpt_complete.py
│ │ │ │ ├── select_user_intent.py
│ │ │ │ └── summarize.py
│ │ │ ├── eval
│ │ │ │ ├── __init__.py
│ │ │ │ ├── eval_skill.py
│ │ │ │ └── routines
│ │ │ │ └── eval.py
│ │ │ ├── fabric
│ │ │ │ ├── __init__.py
│ │ │ │ ├── fabric_skill.py
│ │ │ │ ├── patterns
│ │ │ │ │ ├── agility_story
│ │ │ │ │ │ ├── system.md
│ │ │ │ │ │ └── user.md
│ │ │ │ │ ├── ai
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── analyze_answers
│ │ │ │ │ │ ├── README.md
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── analyze_candidates
│ │ │ │ │ │ ├── system.md
│ │ │ │ │ │ └── user.md
│ │ │ │ │ ├── analyze_cfp_submission
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── analyze_claims
│ │ │ │ │ │ ├── system.md
│ │ │ │ │ │ └── user.md
│ │ │ │ │ ├── analyze_comments
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── analyze_debate
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── analyze_email_headers
│ │ │ │ │ │ ├── system.md
│ │ │ │ │ │ └── user.md
│ │ │ │ │ ├── analyze_incident
│ │ │ │ │ │ ├── system.md
│ │ │ │ │ │ └── user.md
│ │ │ │ │ ├── analyze_interviewer_techniques
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── analyze_logs
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── analyze_malware
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── analyze_military_strategy
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── analyze_mistakes
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── analyze_paper
│ │ │ │ │ │ ├── system.md
│ │ │ │ │ │ └── user.md
│ │ │ │ │ ├── analyze_patent
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── analyze_personality
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── analyze_presentation
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── analyze_product_feedback
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── analyze_proposition
│ │ │ │ │ │ ├── system.md
│ │ │ │ │ │ └── user.md
│ │ │ │ │ ├── analyze_prose
│ │ │ │ │ │ ├── system.md
│ │ │ │ │ │ └── user.md
│ │ │ │ │ ├── analyze_prose_json
│ │ │ │ │ │ ├── system.md
│ │ │ │ │ │ └── user.md
│ │ │ │ │ ├── analyze_prose_pinker
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── analyze_risk
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── analyze_sales_call
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── analyze_spiritual_text
│ │ │ │ │ │ ├── system.md
│ │ │ │ │ │ └── user.md
│ │ │ │ │ ├── analyze_tech_impact
│ │ │ │ │ │ ├── system.md
│ │ │ │ │ │ └── user.md
│ │ │ │ │ ├── analyze_threat_report
│ │ │ │ │ │ ├── system.md
│ │ │ │ │ │ └── user.md
│ │ │ │ │ ├── analyze_threat_report_cmds
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── analyze_threat_report_trends
│ │ │ │ │ │ ├── system.md
│ │ │ │ │ │ └── user.md
│ │ │ │ │ ├── answer_interview_question
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── ask_secure_by_design_questions
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── ask_uncle_duke
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── capture_thinkers_work
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── check_agreement
│ │ │ │ │ │ ├── system.md
│ │ │ │ │ │ └── user.md
│ │ │ │ │ ├── clean_text
│ │ │ │ │ │ ├── system.md
│ │ │ │ │ │ └── user.md
│ │ │ │ │ ├── coding_master
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── compare_and_contrast
│ │ │ │ │ │ ├── system.md
│ │ │ │ │ │ └── user.md
│ │ │ │ │ ├── convert_to_markdown
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── create_5_sentence_summary
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── create_academic_paper
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── create_ai_jobs_analysis
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── create_aphorisms
│ │ │ │ │ │ ├── system.md
│ │ │ │ │ │ └── user.md
│ │ │ │ │ ├── create_art_prompt
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── create_better_frame
│ │ │ │ │ │ ├── system.md
│ │ │ │ │ │ └── user.md
│ │ │ │ │ ├── create_coding_project
│ │ │ │ │ │ ├── README.md
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── create_command
│ │ │ │ │ │ ├── README.md
│ │ │ │ │ │ ├── system.md
│ │ │ │ │ │ └── user.md
│ │ │ │ │ ├── create_cyber_summary
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── create_design_document
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── create_diy
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── create_formal_email
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── create_git_diff_commit
│ │ │ │ │ │ ├── README.md
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── create_graph_from_input
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── create_hormozi_offer
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── create_idea_compass
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── create_investigation_visualization
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── create_keynote
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── create_logo
│ │ │ │ │ │ ├── system.md
│ │ │ │ │ │ └── user.md
│ │ │ │ │ ├── create_markmap_visualization
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── create_mermaid_visualization
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── create_mermaid_visualization_for_github
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── create_micro_summary
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── create_network_threat_landscape
│ │ │ │ │ │ ├── system.md
│ │ │ │ │ │ └── user.md
│ │ │ │ │ ├── create_newsletter_entry
│ │ │ │ │ │ ├── system.md
│ │ │ │ │ │ └── user.md
│ │ │ │ │ ├── create_npc
│ │ │ │ │ │ ├── system.md
│ │ │ │ │ │ └── user.md
│ │ │ │ │ ├── create_pattern
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── create_prd
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── create_prediction_block
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── create_quiz
│ │ │ │ │ │ ├── README.md
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── create_reading_plan
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── create_recursive_outline
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── create_report_finding
│ │ │ │ │ │ ├── system.md
│ │ │ │ │ │ └── user.md
│ │ │ │ │ ├── create_rpg_summary
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── create_security_update
│ │ │ │ │ │ ├── system.md
│ │ │ │ │ │ └── user.md
│ │ │ │ │ ├── create_show_intro
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── create_sigma_rules
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── create_story_explanation
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── create_stride_threat_model
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── create_summary
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── create_tags
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── create_threat_scenarios
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── create_ttrc_graph
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── create_ttrc_narrative
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── create_upgrade_pack
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── create_user_story
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── create_video_chapters
│ │ │ │ │ │ ├── system.md
│ │ │ │ │ │ └── user.md
│ │ │ │ │ ├── create_visualization
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── dialog_with_socrates
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── enrich_blog_post
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── explain_code
│ │ │ │ │ │ ├── system.md
│ │ │ │ │ │ └── user.md
│ │ │ │ │ ├── explain_docs
│ │ │ │ │ │ ├── system.md
│ │ │ │ │ │ └── user.md
│ │ │ │ │ ├── explain_math
│ │ │ │ │ │ ├── README.md
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── explain_project
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── explain_terms
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── export_data_as_csv
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── extract_algorithm_update_recommendations
│ │ │ │ │ │ ├── system.md
│ │ │ │ │ │ └── user.md
│ │ │ │ │ ├── extract_article_wisdom
│ │ │ │ │ │ ├── dmiessler
│ │ │ │ │ │ │ └── extract_wisdom-1.0.0
│ │ │ │ │ │ │ ├── system.md
│ │ │ │ │ │ │ └── user.md
│ │ │ │ │ │ ├── README.md
│ │ │ │ │ │ ├── system.md
│ │ │ │ │ │ └── user.md
│ │ │ │ │ ├── extract_book_ideas
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── extract_book_recommendations
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── extract_business_ideas
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── extract_controversial_ideas
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── extract_core_message
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── extract_ctf_writeup
│ │ │ │ │ │ ├── README.md
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── extract_domains
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── extract_extraordinary_claims
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── extract_ideas
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── extract_insights
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── extract_insights_dm
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── extract_instructions
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── extract_jokes
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── extract_latest_video
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── extract_main_idea
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── extract_most_redeeming_thing
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── extract_patterns
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── extract_poc
│ │ │ │ │ │ ├── system.md
│ │ │ │ │ │ └── user.md
│ │ │ │ │ ├── extract_predictions
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── extract_primary_problem
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── extract_primary_solution
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── extract_product_features
│ │ │ │ │ │ ├── dmiessler
│ │ │ │ │ │ │ └── extract_wisdom-1.0.0
│ │ │ │ │ │ │ ├── system.md
│ │ │ │ │ │ │ └── user.md
│ │ │ │ │ │ ├── README.md
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── extract_questions
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── extract_recipe
│ │ │ │ │ │ ├── README.md
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── extract_recommendations
│ │ │ │ │ │ ├── system.md
│ │ │ │ │ │ └── user.md
│ │ │ │ │ ├── extract_references
│ │ │ │ │ │ ├── system.md
│ │ │ │ │ │ └── user.md
│ │ │ │ │ ├── extract_skills
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── extract_song_meaning
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── extract_sponsors
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── extract_videoid
│ │ │ │ │ │ ├── system.md
│ │ │ │ │ │ └── user.md
│ │ │ │ │ ├── extract_wisdom
│ │ │ │ │ │ ├── dmiessler
│ │ │ │ │ │ │ └── extract_wisdom-1.0.0
│ │ │ │ │ │ │ ├── system.md
│ │ │ │ │ │ │ └── user.md
│ │ │ │ │ │ ├── README.md
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── extract_wisdom_agents
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── extract_wisdom_dm
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── extract_wisdom_nometa
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── find_hidden_message
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── find_logical_fallacies
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── get_wow_per_minute
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── get_youtube_rss
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── humanize
│ │ │ │ │ │ ├── README.md
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── identify_dsrp_distinctions
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── identify_dsrp_perspectives
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── identify_dsrp_relationships
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── identify_dsrp_systems
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── identify_job_stories
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── improve_academic_writing
│ │ │ │ │ │ ├── system.md
│ │ │ │ │ │ └── user.md
│ │ │ │ │ ├── improve_prompt
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── improve_report_finding
│ │ │ │ │ │ ├── system.md
│ │ │ │ │ │ └── user.md
│ │ │ │ │ ├── improve_writing
│ │ │ │ │ │ ├── system.md
│ │ │ │ │ │ └── user.md
│ │ │ │ │ ├── judge_output
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── label_and_rate
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── loaded
│ │ │ │ │ ├── md_callout
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── official_pattern_template
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── pattern_explanations.md
│ │ │ │ │ ├── prepare_7s_strategy
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── provide_guidance
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── rate_ai_response
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── rate_ai_result
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── rate_content
│ │ │ │ │ │ ├── system.md
│ │ │ │ │ │ └── user.md
│ │ │ │ │ ├── rate_value
│ │ │ │ │ │ ├── README.md
│ │ │ │ │ │ ├── system.md
│ │ │ │ │ │ └── user.md
│ │ │ │ │ ├── raw_query
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── raycast
│ │ │ │ │ │ ├── capture_thinkers_work
│ │ │ │ │ │ ├── create_story_explanation
│ │ │ │ │ │ ├── extract_primary_problem
│ │ │ │ │ │ ├── extract_wisdom
│ │ │ │ │ │ └── yt
│ │ │ │ │ ├── recommend_artists
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── recommend_pipeline_upgrades
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── recommend_talkpanel_topics
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── refine_design_document
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── review_design
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── sanitize_broken_html_to_markdown
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── show_fabric_options_markmap
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── solve_with_cot
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── stringify
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── suggest_pattern
│ │ │ │ │ │ ├── system.md
│ │ │ │ │ │ └── user.md
│ │ │ │ │ ├── summarize
│ │ │ │ │ │ ├── dmiessler
│ │ │ │ │ │ │ └── summarize
│ │ │ │ │ │ │ ├── system.md
│ │ │ │ │ │ │ └── user.md
│ │ │ │ │ │ ├── system.md
│ │ │ │ │ │ └── user.md
│ │ │ │ │ ├── summarize_debate
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── summarize_git_changes
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── summarize_git_diff
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── summarize_lecture
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── summarize_legislation
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── summarize_meeting
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── summarize_micro
│ │ │ │ │ │ ├── system.md
│ │ │ │ │ │ └── user.md
│ │ │ │ │ ├── summarize_newsletter
│ │ │ │ │ │ ├── system.md
│ │ │ │ │ │ └── user.md
│ │ │ │ │ ├── summarize_paper
│ │ │ │ │ │ ├── README.md
│ │ │ │ │ │ ├── system.md
│ │ │ │ │ │ └── user.md
│ │ │ │ │ ├── summarize_prompt
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── summarize_pull-requests
│ │ │ │ │ │ ├── system.md
│ │ │ │ │ │ └── user.md
│ │ │ │ │ ├── summarize_rpg_session
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── t_analyze_challenge_handling
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── t_check_metrics
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── t_create_h3_career
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── t_create_opening_sentences
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── t_describe_life_outlook
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── t_extract_intro_sentences
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── t_extract_panel_topics
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── t_find_blindspots
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── t_find_negative_thinking
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── t_find_neglected_goals
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── t_give_encouragement
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── t_red_team_thinking
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── t_threat_model_plans
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── t_visualize_mission_goals_projects
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── t_year_in_review
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── to_flashcards
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── transcribe_minutes
│ │ │ │ │ │ ├── README.md
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── translate
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── tweet
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── write_essay
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── write_hackerone_report
│ │ │ │ │ │ ├── README.md
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── write_latex
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── write_micro_essay
│ │ │ │ │ │ └── system.md
│ │ │ │ │ ├── write_nuclei_template_rule
│ │ │ │ │ │ ├── system.md
│ │ │ │ │ │ └── user.md
│ │ │ │ │ ├── write_pull-request
│ │ │ │ │ │ └── system.md
│ │ │ │ │ └── write_semgrep_rule
│ │ │ │ │ ├── system.md
│ │ │ │ │ └── user.md
│ │ │ │ └── routines
│ │ │ │ ├── list.py
│ │ │ │ ├── run.py
│ │ │ │ └── show.py
│ │ │ ├── guided_conversation
│ │ │ │ ├── __init__.py
│ │ │ │ ├── agenda.py
│ │ │ │ ├── artifact_helpers.py
│ │ │ │ ├── chat_completions
│ │ │ │ │ ├── fix_agenda_error.py
│ │ │ │ │ ├── fix_artifact_error.py
│ │ │ │ │ ├── generate_agenda.py
│ │ │ │ │ ├── generate_artifact_updates.py
│ │ │ │ │ ├── generate_final_artifact.py
│ │ │ │ │ └── generate_message.py
│ │ │ │ ├── conversation_guides
│ │ │ │ │ ├── __init__.py
│ │ │ │ │ ├── acrostic_poem.py
│ │ │ │ │ ├── er_triage.py
│ │ │ │ │ ├── interview.py
│ │ │ │ │ └── patient_intake.py
│ │ │ │ ├── guide.py
│ │ │ │ ├── guided_conversation_skill.py
│ │ │ │ ├── logging.py
│ │ │ │ ├── message.py
│ │ │ │ ├── resources.py
│ │ │ │ ├── routines
│ │ │ │ │ └── guided_conversation.py
│ │ │ │ └── tests
│ │ │ │ ├── conftest.py
│ │ │ │ ├── test_artifact_helpers.py
│ │ │ │ ├── test_generate_agenda.py
│ │ │ │ ├── test_generate_artifact_updates.py
│ │ │ │ ├── test_generate_final_artifact.py
│ │ │ │ └── test_resource.py
│ │ │ ├── meta
│ │ │ │ ├── __init__.py
│ │ │ │ ├── meta_skill.py
│ │ │ │ ├── README.md
│ │ │ │ └── routines
│ │ │ │ └── generate_routine.py
│ │ │ ├── posix
│ │ │ │ ├── __init__.py
│ │ │ │ ├── posix_skill.py
│ │ │ │ ├── routines
│ │ │ │ │ ├── append_file.py
│ │ │ │ │ ├── cd.py
│ │ │ │ │ ├── ls.py
│ │ │ │ │ ├── make_home_dir.py
│ │ │ │ │ ├── mkdir.py
│ │ │ │ │ ├── mv.py
│ │ │ │ │ ├── pwd.py
│ │ │ │ │ ├── read_file.py
│ │ │ │ │ ├── rm.py
│ │ │ │ │ ├── touch.py
│ │ │ │ │ └── write_file.py
│ │ │ │ └── sandbox_shell.py
│ │ │ ├── README.md
│ │ │ ├── research
│ │ │ │ ├── __init__.py
│ │ │ │ ├── README.md
│ │ │ │ ├── research_skill.py
│ │ │ │ └── routines
│ │ │ │ ├── answer_question_about_content.py
│ │ │ │ ├── evaluate_answer.py
│ │ │ │ ├── generate_research_plan.py
│ │ │ │ ├── generate_search_query.py
│ │ │ │ ├── update_research_plan.py
│ │ │ │ ├── web_research.py
│ │ │ │ └── web_search.py
│ │ │ ├── research2
│ │ │ │ ├── __init__.py
│ │ │ │ ├── README.md
│ │ │ │ ├── research_skill.py
│ │ │ │ └── routines
│ │ │ │ ├── facts.py
│ │ │ │ ├── make_final_report.py
│ │ │ │ ├── research.py
│ │ │ │ ├── search_plan.py
│ │ │ │ ├── search.py
│ │ │ │ └── visit_pages.py
│ │ │ └── web_research
│ │ │ ├── __init__.py
│ │ │ ├── README.md
│ │ │ ├── research_skill.py
│ │ │ └── routines
│ │ │ ├── facts.py
│ │ │ ├── make_final_report.py
│ │ │ ├── research.py
│ │ │ ├── search_plan.py
│ │ │ ├── search.py
│ │ │ └── visit_pages.py
│ │ ├── tests
│ │ │ ├── test_common_skill.py
│ │ │ ├── test_integration.py
│ │ │ ├── test_routine_stack.py
│ │ │ ├── tst_skill
│ │ │ │ ├── __init__.py
│ │ │ │ └── routines
│ │ │ │ ├── __init__.py
│ │ │ │ └── a_routine.py
│ │ │ └── utilities
│ │ │ ├── test_find_template_vars.py
│ │ │ ├── test_make_arg_set.py
│ │ │ ├── test_paramspec.py
│ │ │ ├── test_parse_command_string.py
│ │ │ └── test_to_string.py
│ │ ├── types.py
│ │ ├── usage.py
│ │ └── utilities.py
│ └── uv.lock
├── LICENSE
├── Makefile
├── mcp-servers
│ ├── ai-assist-content
│ │ ├── .vscode
│ │ │ └── settings.json
│ │ ├── mcp-example-brave-search.md
│ │ ├── mcp-fastmcp-typescript-README.md
│ │ ├── mcp-llms-full.txt
│ │ ├── mcp-metadata-tips.md
│ │ ├── mcp-python-sdk-README.md
│ │ ├── mcp-typescript-sdk-README.md
│ │ ├── pydanticai-documentation.md
│ │ ├── pydanticai-example-question-graph.md
│ │ ├── pydanticai-example-weather.md
│ │ ├── pydanticai-tutorial.md
│ │ └── README.md
│ ├── Makefile
│ ├── mcp-server-bing-search
│ │ ├── .env.example
│ │ ├── .gitignore
│ │ ├── .vscode
│ │ │ ├── launch.json
│ │ │ └── settings.json
│ │ ├── Makefile
│ │ ├── mcp_server_bing_search
│ │ │ ├── __init__.py
│ │ │ ├── config.py
│ │ │ ├── prompts
│ │ │ │ ├── __init__.py
│ │ │ │ ├── clean_website.py
│ │ │ │ └── filter_links.py
│ │ │ ├── server.py
│ │ │ ├── start.py
│ │ │ ├── tools.py
│ │ │ ├── types.py
│ │ │ ├── utils.py
│ │ │ └── web
│ │ │ ├── __init__.py
│ │ │ ├── get_content.py
│ │ │ ├── llm_processing.py
│ │ │ ├── process_website.py
│ │ │ └── search_bing.py
│ │ ├── pyproject.toml
│ │ ├── README.md
│ │ ├── tests
│ │ │ └── test_tools.py
│ │ └── uv.lock
│ ├── mcp-server-bundle
│ │ ├── .vscode
│ │ │ ├── launch.json
│ │ │ └── settings.json
│ │ ├── Makefile
│ │ ├── mcp_server_bundle
│ │ │ ├── __init__.py
│ │ │ └── main.py
│ │ ├── pyinstaller.spec
│ │ ├── pyproject.toml
│ │ ├── README.md
│ │ └── uv.lock
│ ├── mcp-server-filesystem
│ │ ├── .env.example
│ │ ├── .github
│ │ │ └── workflows
│ │ │ └── ci.yml
│ │ ├── .gitignore
│ │ ├── .vscode
│ │ │ ├── launch.json
│ │ │ └── settings.json
│ │ ├── Makefile
│ │ ├── mcp_server_filesystem
│ │ │ ├── __init__.py
│ │ │ ├── config.py
│ │ │ ├── server.py
│ │ │ └── start.py
│ │ ├── pyproject.toml
│ │ ├── README.md
│ │ ├── tests
│ │ │ └── test_filesystem.py
│ │ └── uv.lock
│ ├── mcp-server-filesystem-edit
│ │ ├── .env.example
│ │ ├── .gitignore
│ │ ├── .vscode
│ │ │ ├── launch.json
│ │ │ └── settings.json
│ │ ├── data
│ │ │ ├── attachments
│ │ │ │ ├── Daily Game Ideas.txt
│ │ │ │ ├── Frontend Framework Proposal.txt
│ │ │ │ ├── ReDoodle.txt
│ │ │ │ └── Research Template.tex
│ │ │ ├── test_cases.yaml
│ │ │ └── transcripts
│ │ │ ├── transcript_research_simple.md
│ │ │ ├── transcript_Startup_Idea_1_202503031513.md
│ │ │ ├── transcript_Startup_Idea_2_202503031659.md
│ │ │ └── transcript_Web_Frontends_202502281551.md
│ │ ├── Makefile
│ │ ├── mcp_server_filesystem_edit
│ │ │ ├── __init__.py
│ │ │ ├── app_handling
│ │ │ │ ├── __init__.py
│ │ │ │ ├── excel.py
│ │ │ │ ├── miktex.py
│ │ │ │ ├── office_common.py
│ │ │ │ ├── powerpoint.py
│ │ │ │ └── word.py
│ │ │ ├── config.py
│ │ │ ├── evals
│ │ │ │ ├── __init__.py
│ │ │ │ ├── common.py
│ │ │ │ ├── run_comments.py
│ │ │ │ ├── run_edit.py
│ │ │ │ └── run_ppt_edit.py
│ │ │ ├── prompts
│ │ │ │ ├── __init__.py
│ │ │ │ ├── add_comments.py
│ │ │ │ ├── analyze_comments.py
│ │ │ │ ├── latex_edit.py
│ │ │ │ ├── markdown_draft.py
│ │ │ │ ├── markdown_edit.py
│ │ │ │ └── powerpoint_edit.py
│ │ │ ├── server.py
│ │ │ ├── start.py
│ │ │ ├── tools
│ │ │ │ ├── __init__.py
│ │ │ │ ├── add_comments.py
│ │ │ │ ├── edit_adapters
│ │ │ │ │ ├── __init__.py
│ │ │ │ │ ├── common.py
│ │ │ │ │ ├── latex.py
│ │ │ │ │ └── markdown.py
│ │ │ │ ├── edit.py
│ │ │ │ └── helpers.py
│ │ │ └── types.py
│ │ ├── pyproject.toml
│ │ ├── README.md
│ │ ├── tests
│ │ │ ├── app_handling
│ │ │ │ ├── test_excel.py
│ │ │ │ ├── test_miktext.py
│ │ │ │ ├── test_office_common.py
│ │ │ │ ├── test_powerpoint.py
│ │ │ │ └── test_word.py
│ │ │ ├── conftest.py
│ │ │ └── tools
│ │ │ └── edit_adapters
│ │ │ ├── test_latex.py
│ │ │ └── test_markdown.py
│ │ └── uv.lock
│ ├── mcp-server-fusion
│ │ ├── .gitignore
│ │ ├── .vscode
│ │ │ ├── launch.json
│ │ │ └── settings.json
│ │ ├── AddInIcon.svg
│ │ ├── config.py
│ │ ├── FusionMCPServerAddIn.manifest
│ │ ├── FusionMCPServerAddIn.py
│ │ ├── mcp_server_fusion
│ │ │ ├── __init__.py
│ │ │ ├── fusion_mcp_server.py
│ │ │ ├── fusion_utils
│ │ │ │ ├── __init__.py
│ │ │ │ ├── event_utils.py
│ │ │ │ ├── general_utils.py
│ │ │ │ └── tool_utils.py
│ │ │ ├── mcp_tools
│ │ │ │ ├── __init__.py
│ │ │ │ ├── fusion_3d_operation.py
│ │ │ │ ├── fusion_geometry.py
│ │ │ │ ├── fusion_pattern.py
│ │ │ │ └── fusion_sketch.py
│ │ │ └── vendor
│ │ │ └── README.md
│ │ ├── README.md
│ │ └── requirements.txt
│ ├── mcp-server-giphy
│ │ ├── .env.example
│ │ ├── .gitignore
│ │ ├── .vscode
│ │ │ ├── launch.json
│ │ │ └── settings.json
│ │ ├── Makefile
│ │ ├── mcp_server
│ │ │ ├── __init__.py
│ │ │ ├── config.py
│ │ │ ├── giphy_search.py
│ │ │ ├── sampling.py
│ │ │ ├── server.py
│ │ │ ├── start.py
│ │ │ └── utils.py
│ │ ├── pyproject.toml
│ │ ├── README.md
│ │ └── uv.lock
│ ├── mcp-server-memory-user-bio
│ │ ├── .env.example
│ │ ├── .gitignore
│ │ ├── .vscode
│ │ │ ├── launch.json
│ │ │ └── settings.json
│ │ ├── Makefile
│ │ ├── mcp_server_memory_user_bio
│ │ │ ├── __init__.py
│ │ │ ├── config.py
│ │ │ ├── server.py
│ │ │ └── start.py
│ │ ├── pyproject.toml
│ │ ├── README.md
│ │ └── uv.lock
│ ├── mcp-server-memory-whiteboard
│ │ ├── .env.example
│ │ ├── .gitignore
│ │ ├── .vscode
│ │ │ ├── launch.json
│ │ │ └── settings.json
│ │ ├── Makefile
│ │ ├── mcp_server_memory_whiteboard
│ │ │ ├── __init__.py
│ │ │ ├── config.py
│ │ │ ├── server.py
│ │ │ └── start.py
│ │ ├── pyproject.toml
│ │ ├── README.md
│ │ └── uv.lock
│ ├── mcp-server-office
│ │ ├── .env.example
│ │ ├── .vscode
│ │ │ ├── launch.json
│ │ │ └── settings.json
│ │ ├── build.sh
│ │ ├── data
│ │ │ ├── attachments
│ │ │ │ ├── Daily Game Ideas.txt
│ │ │ │ ├── Frontend Framework Proposal.txt
│ │ │ │ └── ReDoodle.txt
│ │ │ └── word
│ │ │ ├── test_cases.yaml
│ │ │ └── transcripts
│ │ │ ├── transcript_Startup_Idea_1_202503031513.md
│ │ │ ├── transcript_Startup_Idea_2_202503031659.md
│ │ │ └── transcript_Web_Frontends_202502281551.md
│ │ ├── Makefile
│ │ ├── mcp_server
│ │ │ ├── __init__.py
│ │ │ ├── app_interaction
│ │ │ │ ├── __init__.py
│ │ │ │ ├── excel_editor.py
│ │ │ │ ├── powerpoint_editor.py
│ │ │ │ └── word_editor.py
│ │ │ ├── config.py
│ │ │ ├── constants.py
│ │ │ ├── evals
│ │ │ │ ├── __init__.py
│ │ │ │ ├── common.py
│ │ │ │ ├── run_comment_analysis.py
│ │ │ │ ├── run_feedback.py
│ │ │ │ └── run_markdown_edit.py
│ │ │ ├── helpers.py
│ │ │ ├── markdown_edit
│ │ │ │ ├── __init__.py
│ │ │ │ ├── comment_analysis.py
│ │ │ │ ├── feedback_step.py
│ │ │ │ ├── markdown_edit.py
│ │ │ │ └── utils.py
│ │ │ ├── prompts
│ │ │ │ ├── __init__.py
│ │ │ │ ├── comment_analysis.py
│ │ │ │ ├── feedback.py
│ │ │ │ ├── markdown_draft.py
│ │ │ │ └── markdown_edit.py
│ │ │ ├── server.py
│ │ │ ├── start.py
│ │ │ └── types.py
│ │ ├── pyproject.toml
│ │ ├── README.md
│ │ ├── tests
│ │ │ └── test_word_editor.py
│ │ └── uv.lock
│ ├── mcp-server-open-deep-research
│ │ ├── .env.example
│ │ ├── .gitignore
│ │ ├── .vscode
│ │ │ ├── launch.json
│ │ │ └── settings.json
│ │ ├── Makefile
│ │ ├── mcp_server
│ │ │ ├── __init__.py
│ │ │ ├── config.py
│ │ │ ├── libs
│ │ │ │ └── open_deep_research
│ │ │ │ ├── cookies.py
│ │ │ │ ├── mdconvert.py
│ │ │ │ ├── run_agents.py
│ │ │ │ ├── text_inspector_tool.py
│ │ │ │ ├── text_web_browser.py
│ │ │ │ └── visual_qa.py
│ │ │ ├── open_deep_research.py
│ │ │ ├── server.py
│ │ │ └── start.py
│ │ ├── pyproject.toml
│ │ ├── README.md
│ │ └── uv.lock
│ ├── mcp-server-open-deep-research-clone
│ │ ├── .env.example
│ │ ├── .gitignore
│ │ ├── .vscode
│ │ │ ├── launch.json
│ │ │ └── settings.json
│ │ ├── Makefile
│ │ ├── mcp_server_open_deep_research_clone
│ │ │ ├── __init__.py
│ │ │ ├── azure_openai.py
│ │ │ ├── config.py
│ │ │ ├── logging.py
│ │ │ ├── sampling.py
│ │ │ ├── server.py
│ │ │ ├── start.py
│ │ │ ├── utils.py
│ │ │ └── web_research.py
│ │ ├── pyproject.toml
│ │ ├── README.md
│ │ ├── test
│ │ │ └── test_open_deep_research_clone.py
│ │ └── uv.lock
│ ├── mcp-server-template
│ │ ├── .taplo.toml
│ │ ├── .vscode
│ │ │ └── settings.json
│ │ ├── copier.yml
│ │ ├── README.md
│ │ └── template
│ │ └── {{ project_slug }}
│ │ ├── .env.example.jinja
│ │ ├── .gitignore
│ │ ├── .vscode
│ │ │ ├── launch.json.jinja
│ │ │ └── settings.json
│ │ ├── {{ module_name }}
│ │ │ ├── __init__.py
│ │ │ ├── config.py.jinja
│ │ │ ├── server.py.jinja
│ │ │ └── start.py.jinja
│ │ ├── Makefile.jinja
│ │ ├── pyproject.toml.jinja
│ │ └── README.md.jinja
│ ├── mcp-server-vscode
│ │ ├── .eslintrc.cjs
│ │ ├── .gitignore
│ │ ├── .npmrc
│ │ ├── .vscode
│ │ │ ├── extensions.json
│ │ │ ├── launch.json
│ │ │ ├── settings.json
│ │ │ └── tasks.json
│ │ ├── .vscode-test.mjs
│ │ ├── .vscodeignore
│ │ ├── ASSISTANT_BOOTSTRAP.md
│ │ ├── eslint.config.mjs
│ │ ├── images
│ │ │ └── icon.png
│ │ ├── LICENSE
│ │ ├── Makefile
│ │ ├── out
│ │ │ ├── extension.d.ts
│ │ │ ├── extension.js
│ │ │ ├── test
│ │ │ │ ├── extension.test.d.ts
│ │ │ │ └── extension.test.js
│ │ │ ├── tools
│ │ │ │ ├── code_checker.d.ts
│ │ │ │ ├── code_checker.js
│ │ │ │ ├── debug_tools.d.ts
│ │ │ │ ├── debug_tools.js
│ │ │ │ ├── focus_editor.d.ts
│ │ │ │ ├── focus_editor.js
│ │ │ │ ├── search_symbol.d.ts
│ │ │ │ └── search_symbol.js
│ │ │ └── utils
│ │ │ ├── port.d.ts
│ │ │ └── port.js
│ │ ├── package.json
│ │ ├── pnpm-lock.yaml
│ │ ├── prettier.config.cjs
│ │ ├── README.md
│ │ ├── src
│ │ │ ├── extension.d.ts
│ │ │ ├── extension.ts
│ │ │ ├── test
│ │ │ │ ├── extension.test.d.ts
│ │ │ │ └── extension.test.ts
│ │ │ ├── tools
│ │ │ │ ├── code_checker.d.ts
│ │ │ │ ├── code_checker.ts
│ │ │ │ ├── debug_tools.d.ts
│ │ │ │ ├── debug_tools.ts
│ │ │ │ ├── focus_editor.d.ts
│ │ │ │ ├── focus_editor.ts
│ │ │ │ ├── search_symbol.d.ts
│ │ │ │ └── search_symbol.ts
│ │ │ └── utils
│ │ │ ├── port.d.ts
│ │ │ └── port.ts
│ │ ├── tsconfig.json
│ │ ├── tsconfig.tsbuildinfo
│ │ ├── vsc-extension-quickstart.md
│ │ └── webpack.config.js
│ └── mcp-server-web-research
│ ├── .env.example
│ ├── .gitignore
│ ├── .vscode
│ │ ├── launch.json
│ │ └── settings.json
│ ├── Makefile
│ ├── mcp_server_web_research
│ │ ├── __init__.py
│ │ ├── azure_openai.py
│ │ ├── config.py
│ │ ├── logging.py
│ │ ├── sampling.py
│ │ ├── server.py
│ │ ├── start.py
│ │ ├── utils.py
│ │ └── web_research.py
│ ├── pyproject.toml
│ ├── README.md
│ ├── test
│ │ └── test_web_research.py
│ └── uv.lock
├── README.md
├── RESPONSIBLE_AI_FAQ.md
├── ruff.toml
├── SECURITY.md
├── semantic-workbench.code-workspace
├── SUPPORT.md
├── tools
│ ├── build_ai_context_files.py
│ ├── collect_files.py
│ ├── docker
│ │ ├── azure_website_sshd.conf
│ │ ├── docker-entrypoint.sh
│ │ ├── Dockerfile.assistant
│ │ └── Dockerfile.mcp-server
│ ├── makefiles
│ │ ├── docker-assistant.mk
│ │ ├── docker-mcp-server.mk
│ │ ├── docker.mk
│ │ ├── python.mk
│ │ ├── recursive.mk
│ │ └── shell.mk
│ ├── reset-service-data.ps1
│ ├── reset-service-data.sh
│ ├── run-app.ps1
│ ├── run-app.sh
│ ├── run-canonical-agent.ps1
│ ├── run-canonical-agent.sh
│ ├── run-dotnet-examples-with-aspire.sh
│ ├── run-python-example1.sh
│ ├── run-python-example2.ps1
│ ├── run-python-example2.sh
│ ├── run-service.ps1
│ ├── run-service.sh
│ ├── run-workbench-chatbot.ps1
│ └── run-workbench-chatbot.sh
├── workbench-app
│ ├── .dockerignore
│ ├── .env.example
│ ├── .eslintrc.cjs
│ ├── .gitignore
│ ├── .vscode
│ │ ├── launch.json
│ │ └── settings.json
│ ├── docker-entrypoint.sh
│ ├── Dockerfile
│ ├── docs
│ │ ├── APP_DEV_GUIDE.md
│ │ ├── MESSAGE_METADATA.md
│ │ ├── MESSAGE_TYPES.md
│ │ ├── README.md
│ │ └── STATE_INSPECTORS.md
│ ├── index.html
│ ├── Makefile
│ ├── nginx.conf
│ ├── package.json
│ ├── pnpm-lock.yaml
│ ├── prettier.config.cjs
│ ├── public
│ │ └── assets
│ │ ├── background-1-upscaled.jpg
│ │ ├── background-1-upscaled.png
│ │ ├── background-1.jpg
│ │ ├── background-1.png
│ │ ├── background-2.jpg
│ │ ├── background-2.png
│ │ ├── experimental-feature.jpg
│ │ ├── favicon.svg
│ │ ├── workflow-designer-1.jpg
│ │ ├── workflow-designer-outlets.jpg
│ │ ├── workflow-designer-states.jpg
│ │ └── workflow-designer-transitions.jpg
│ ├── README.md
│ ├── run.sh
│ ├── src
│ │ ├── components
│ │ │ ├── App
│ │ │ │ ├── AppFooter.tsx
│ │ │ │ ├── AppHeader.tsx
│ │ │ │ ├── AppMenu.tsx
│ │ │ │ ├── AppView.tsx
│ │ │ │ ├── CodeLabel.tsx
│ │ │ │ ├── CommandButton.tsx
│ │ │ │ ├── ConfirmLeave.tsx
│ │ │ │ ├── ContentExport.tsx
│ │ │ │ ├── ContentImport.tsx
│ │ │ │ ├── CopyButton.tsx
│ │ │ │ ├── DialogControl.tsx
│ │ │ │ ├── DynamicIframe.tsx
│ │ │ │ ├── ErrorListFromAppState.tsx
│ │ │ │ ├── ErrorMessageBar.tsx
│ │ │ │ ├── ExperimentalNotice.tsx
│ │ │ │ ├── FormWidgets
│ │ │ │ │ ├── BaseModelEditorWidget.tsx
│ │ │ │ │ ├── CustomizedArrayFieldTemplate.tsx
│ │ │ │ │ ├── CustomizedFieldTemplate.tsx
│ │ │ │ │ ├── CustomizedObjectFieldTemplate.tsx
│ │ │ │ │ └── InspectableWidget.tsx
│ │ │ │ ├── LabelWithDescription.tsx
│ │ │ │ ├── Loading.tsx
│ │ │ │ ├── MenuItemControl.tsx
│ │ │ │ ├── MiniControl.tsx
│ │ │ │ ├── MyAssistantServiceRegistrations.tsx
│ │ │ │ ├── MyItemsManager.tsx
│ │ │ │ ├── OverflowMenu.tsx
│ │ │ │ ├── PresenceMotionList.tsx
│ │ │ │ ├── ProfileSettings.tsx
│ │ │ │ └── TooltipWrapper.tsx
│ │ │ ├── Assistants
│ │ │ │ ├── ApplyConfigButton.tsx
│ │ │ │ ├── AssistantAdd.tsx
│ │ │ │ ├── AssistantConfigExportButton.tsx
│ │ │ │ ├── AssistantConfigImportButton.tsx
│ │ │ │ ├── AssistantConfiguration.tsx
│ │ │ │ ├── AssistantConfigure.tsx
│ │ │ │ ├── AssistantCreate.tsx
│ │ │ │ ├── AssistantDelete.tsx
│ │ │ │ ├── AssistantDuplicate.tsx
│ │ │ │ ├── AssistantExport.tsx
│ │ │ │ ├── AssistantImport.tsx
│ │ │ │ ├── AssistantRemove.tsx
│ │ │ │ ├── AssistantRename.tsx
│ │ │ │ ├── AssistantServiceInfo.tsx
│ │ │ │ ├── AssistantServiceMetadata.tsx
│ │ │ │ └── MyAssistants.tsx
│ │ │ ├── AssistantServiceRegistrations
│ │ │ │ ├── AssistantServiceRegistrationApiKey.tsx
│ │ │ │ ├── AssistantServiceRegistrationApiKeyReset.tsx
│ │ │ │ ├── AssistantServiceRegistrationCreate.tsx
│ │ │ │ └── AssistantServiceRegistrationRemove.tsx
│ │ │ ├── Conversations
│ │ │ │ ├── Canvas
│ │ │ │ │ ├── AssistantCanvas.tsx
│ │ │ │ │ ├── AssistantCanvasList.tsx
│ │ │ │ │ ├── AssistantInspector.tsx
│ │ │ │ │ ├── AssistantInspectorList.tsx
│ │ │ │ │ └── ConversationCanvas.tsx
│ │ │ │ ├── ChatInputPlugins
│ │ │ │ │ ├── ClearEditorPlugin.tsx
│ │ │ │ │ ├── LexicalMenu.ts
│ │ │ │ │ ├── ParticipantMentionsPlugin.tsx
│ │ │ │ │ ├── TypeaheadMenuPlugin.css
│ │ │ │ │ └── TypeaheadMenuPlugin.tsx
│ │ │ │ ├── ContentRenderers
│ │ │ │ │ ├── CodeContentRenderer.tsx
│ │ │ │ │ ├── ContentListRenderer.tsx
│ │ │ │ │ ├── ContentRenderer.tsx
│ │ │ │ │ ├── DiffRenderer.tsx
│ │ │ │ │ ├── HtmlContentRenderer.tsx
│ │ │ │ │ ├── JsonSchemaContentRenderer.tsx
│ │ │ │ │ ├── MarkdownContentRenderer.tsx
│ │ │ │ │ ├── MarkdownEditorRenderer.tsx
│ │ │ │ │ ├── MermaidContentRenderer.tsx
│ │ │ │ │ ├── MusicABCContentRenderer.css
│ │ │ │ │ └── MusicABCContentRenderer.tsx
│ │ │ │ ├── ContextWindow.tsx
│ │ │ │ ├── ConversationCreate.tsx
│ │ │ │ ├── ConversationDuplicate.tsx
│ │ │ │ ├── ConversationExport.tsx
│ │ │ │ ├── ConversationFileIcon.tsx
│ │ │ │ ├── ConversationRemove.tsx
│ │ │ │ ├── ConversationRename.tsx
│ │ │ │ ├── ConversationShare.tsx
│ │ │ │ ├── ConversationShareCreate.tsx
│ │ │ │ ├── ConversationShareList.tsx
│ │ │ │ ├── ConversationShareView.tsx
│ │ │ │ ├── ConversationsImport.tsx
│ │ │ │ ├── ConversationTranscript.tsx
│ │ │ │ ├── DebugInspector.tsx
│ │ │ │ ├── FileItem.tsx
│ │ │ │ ├── FileList.tsx
│ │ │ │ ├── InputAttachmentList.tsx
│ │ │ │ ├── InputOptionsControl.tsx
│ │ │ │ ├── InteractHistory.tsx
│ │ │ │ ├── InteractInput.tsx
│ │ │ │ ├── Message
│ │ │ │ │ ├── AttachmentSection.tsx
│ │ │ │ │ ├── ContentRenderer.tsx
│ │ │ │ │ ├── ContentSafetyNotice.tsx
│ │ │ │ │ ├── InteractMessage.tsx
│ │ │ │ │ ├── MessageActions.tsx
│ │ │ │ │ ├── MessageBase.tsx
│ │ │ │ │ ├── MessageBody.tsx
│ │ │ │ │ ├── MessageContent.tsx
│ │ │ │ │ ├── MessageFooter.tsx
│ │ │ │ │ ├── MessageHeader.tsx
│ │ │ │ │ ├── NotificationAccordion.tsx
│ │ │ │ │ └── ToolResultMessage.tsx
│ │ │ │ ├── MessageDelete.tsx
│ │ │ │ ├── MessageLink.tsx
│ │ │ │ ├── MyConversations.tsx
│ │ │ │ ├── MyShares.tsx
│ │ │ │ ├── ParticipantAvatar.tsx
│ │ │ │ ├── ParticipantAvatarGroup.tsx
│ │ │ │ ├── ParticipantItem.tsx
│ │ │ │ ├── ParticipantList.tsx
│ │ │ │ ├── ParticipantStatus.tsx
│ │ │ │ ├── RewindConversation.tsx
│ │ │ │ ├── ShareRemove.tsx
│ │ │ │ ├── SpeechButton.tsx
│ │ │ │ └── ToolCalls.tsx
│ │ │ └── FrontDoor
│ │ │ ├── Chat
│ │ │ │ ├── AssistantDrawer.tsx
│ │ │ │ ├── CanvasDrawer.tsx
│ │ │ │ ├── Chat.tsx
│ │ │ │ ├── ChatCanvas.tsx
│ │ │ │ ├── ChatControls.tsx
│ │ │ │ └── ConversationDrawer.tsx
│ │ │ ├── Controls
│ │ │ │ ├── AssistantCard.tsx
│ │ │ │ ├── AssistantSelector.tsx
│ │ │ │ ├── AssistantServiceSelector.tsx
│ │ │ │ ├── ConversationItem.tsx
│ │ │ │ ├── ConversationList.tsx
│ │ │ │ ├── ConversationListOptions.tsx
│ │ │ │ ├── NewConversationButton.tsx
│ │ │ │ ├── NewConversationForm.tsx
│ │ │ │ └── SiteMenuButton.tsx
│ │ │ ├── GlobalContent.tsx
│ │ │ └── MainContent.tsx
│ │ ├── Constants.ts
│ │ ├── global.d.ts
│ │ ├── index.css
│ │ ├── libs
│ │ │ ├── AppStorage.ts
│ │ │ ├── AuthHelper.ts
│ │ │ ├── EventSubscriptionManager.ts
│ │ │ ├── Theme.ts
│ │ │ ├── useAssistantCapabilities.ts
│ │ │ ├── useChatCanvasController.ts
│ │ │ ├── useConversationEvents.ts
│ │ │ ├── useConversationUtility.ts
│ │ │ ├── useCreateConversation.ts
│ │ │ ├── useDebugComponentLifecycle.ts
│ │ │ ├── useDragAndDrop.ts
│ │ │ ├── useEnvironment.ts
│ │ │ ├── useExportUtility.ts
│ │ │ ├── useHistoryUtility.ts
│ │ │ ├── useKeySequence.ts
│ │ │ ├── useMediaQuery.ts
│ │ │ ├── useMicrosoftGraph.ts
│ │ │ ├── useNotify.tsx
│ │ │ ├── useParticipantUtility.tsx
│ │ │ ├── useSiteUtility.ts
│ │ │ ├── useWorkbenchEventSource.ts
│ │ │ ├── useWorkbenchService.ts
│ │ │ └── Utility.ts
│ │ ├── main.tsx
│ │ ├── models
│ │ │ ├── Assistant.ts
│ │ │ ├── AssistantCapability.ts
│ │ │ ├── AssistantServiceInfo.ts
│ │ │ ├── AssistantServiceRegistration.ts
│ │ │ ├── Config.ts
│ │ │ ├── Conversation.ts
│ │ │ ├── ConversationFile.ts
│ │ │ ├── ConversationMessage.ts
│ │ │ ├── ConversationMessageDebug.ts
│ │ │ ├── ConversationParticipant.ts
│ │ │ ├── ConversationShare.ts
│ │ │ ├── ConversationShareRedemption.ts
│ │ │ ├── ConversationState.ts
│ │ │ ├── ConversationStateDescription.ts
│ │ │ ├── ServiceEnvironment.ts
│ │ │ └── User.ts
│ │ ├── redux
│ │ │ ├── app
│ │ │ │ ├── hooks.ts
│ │ │ │ ├── rtkQueryErrorLogger.ts
│ │ │ │ └── store.ts
│ │ │ └── features
│ │ │ ├── app
│ │ │ │ ├── appSlice.ts
│ │ │ │ └── AppState.ts
│ │ │ ├── chatCanvas
│ │ │ │ ├── chatCanvasSlice.ts
│ │ │ │ └── ChatCanvasState.ts
│ │ │ ├── localUser
│ │ │ │ ├── localUserSlice.ts
│ │ │ │ └── LocalUserState.ts
│ │ │ └── settings
│ │ │ ├── settingsSlice.ts
│ │ │ └── SettingsState.ts
│ │ ├── Root.tsx
│ │ ├── routes
│ │ │ ├── AcceptTerms.tsx
│ │ │ ├── AssistantEditor.tsx
│ │ │ ├── AssistantServiceRegistrationEditor.tsx
│ │ │ ├── Dashboard.tsx
│ │ │ ├── ErrorPage.tsx
│ │ │ ├── FrontDoor.tsx
│ │ │ ├── Login.tsx
│ │ │ ├── Settings.tsx
│ │ │ ├── ShareRedeem.tsx
│ │ │ └── Shares.tsx
│ │ ├── services
│ │ │ └── workbench
│ │ │ ├── assistant.ts
│ │ │ ├── assistantService.ts
│ │ │ ├── conversation.ts
│ │ │ ├── file.ts
│ │ │ ├── index.ts
│ │ │ ├── participant.ts
│ │ │ ├── share.ts
│ │ │ ├── state.ts
│ │ │ └── workbench.ts
│ │ └── vite-env.d.ts
│ ├── tools
│ │ └── filtered-ts-prune.cjs
│ ├── tsconfig.json
│ └── vite.config.ts
└── workbench-service
├── .env.example
├── .vscode
│ ├── extensions.json
│ ├── launch.json
│ └── settings.json
├── alembic.ini
├── devdb
│ ├── docker-compose.yaml
│ └── postgresql-init.sh
├── Dockerfile
├── Makefile
├── migrations
│ ├── env.py
│ ├── README
│ ├── script.py.mako
│ └── versions
│ ├── 2024_09_19_000000_69dcda481c14_init.py
│ ├── 2024_09_19_190029_dffb1d7e219a_file_version_filename.py
│ ├── 2024_09_20_204130_b29524775484_share.py
│ ├── 2024_10_30_231536_039bec8edc33_index_message_type.py
│ ├── 2024_11_04_204029_5149c7fb5a32_conversationmessagedebug.py
│ ├── 2024_11_05_015124_245baf258e11_double_check_debugs.py
│ ├── 2024_11_25_191056_a106de176394_drop_workflow.py
│ ├── 2025_03_19_140136_aaaf792d4d72_set_user_title_set.py
│ ├── 2025_03_21_153250_3763629295ad_add_assistant_template_id.py
│ ├── 2025_05_19_163613_b2f86e981885_delete_context_transfer_assistants.py
│ └── 2025_06_18_174328_503c739152f3_delete_knowlege_transfer_assistants.py
├── pyproject.toml
├── README.md
├── semantic_workbench_service
│ ├── __init__.py
│ ├── api.py
│ ├── assistant_api_key.py
│ ├── auth.py
│ ├── azure_speech.py
│ ├── config.py
│ ├── controller
│ │ ├── __init__.py
│ │ ├── assistant_service_client_pool.py
│ │ ├── assistant_service_registration.py
│ │ ├── assistant.py
│ │ ├── conversation_share.py
│ │ ├── conversation.py
│ │ ├── convert.py
│ │ ├── exceptions.py
│ │ ├── export_import.py
│ │ ├── file.py
│ │ ├── participant.py
│ │ └── user.py
│ ├── db.py
│ ├── event.py
│ ├── files.py
│ ├── logging_config.py
│ ├── middleware.py
│ ├── query.py
│ ├── service_user_principals.py
│ ├── service.py
│ └── start.py
├── tests
│ ├── __init__.py
│ ├── conftest.py
│ ├── docker-compose.yaml
│ ├── test_assistant_api_key.py
│ ├── test_files.py
│ ├── test_integration.py
│ ├── test_middleware.py
│ ├── test_migrations.py
│ ├── test_workbench_service.py
│ └── types.py
└── uv.lock
```
# Files
--------------------------------------------------------------------------------
/libraries/dotnet/README.md:
--------------------------------------------------------------------------------
```markdown
# Semantic Workbench
Semantic Workbench is a versatile tool designed for quickly prototyping intelligent assistants.
Whether you're building new assistants or integrating existing ones, the workbench offers a unified
interface for managing conversations, configuring settings, and customizing behavior.
# Connector
The Connector allows to seamlessly integrate .NET agents, built with any framework, into Semantic
Workbench. By using HTTP for communication, the connector enables your agent to handle instructions
and exchange data with both the frontend and backend of Semantic Workbench.
# Setup Guide
To integrate your agent:
1. Add the `Microsoft.SemanticWorkbench.Connector` nuget to the .NET project containing your agent.
2. **Define an agent configuration**: Create a configuration class for your agent. This can be empty
if no configuration is needed from the workbench UI.
3. **Extend Agent Functionality**: Inherit from `Microsoft.SemanticWorkbench.Connector.AgentBase`
and implement the `GetDefaultConfig` and `ParseConfig` methods in your agent class. Examples
are available in the repository.
4. **Create a Connector**: Implement `Microsoft.SemanticWorkbench.Connector.WorkbenchConnector` and
its `CreateAgentAsync` method to allow the workbench to create multiple agent instances.
5. Start a `Microsoft.SemanticWorkbench.Connector.WorkbenchConnector` calling the `ConnectAsync`
method.
6. Start a Web service using the endpoints defined in `Microsoft.SemanticWorkbench.Connector.Webservice`.
# Examples
Find sample .NET agents and assistants using this connector in the
[official repository](https://github.com/microsoft/semanticworkbench/tree/main/examples).
```
--------------------------------------------------------------------------------
/mcp-servers/mcp-server-giphy/README.md:
--------------------------------------------------------------------------------
```markdown
# MCP GIPHY Server
## Overview
This project implements a Model Context Protocol (MCP) server integration with the GIPHY API. The server allows an assistant to submit recent chat history ('context') and a search term to retrieve a list of candidate results from GIPHY and load their image data.
## Features
- **Search and Retrieve:** Uses the GIPHY API to search for images based on a provided search term.
- (Future) **Sampling:** Employs the MCP "sampling" feature to request the assistant to choose the most appropriate image for the context.
- **Integration:** The selected GIPHY image and its metadata are returned to the original assistant tool call, enabling it to be included in assistant responses to users.
## Setup and Installation
Simply run:
```bash
make
```
To create the virtual environment and install dependencies.
### Running the Server
Use the VSCode launch configuration, or run manually:
Defaults to stdio transport:
```bash
uv run -m mcp_server.start
```
For SSE transport:
```bash
uv run -m mcp_server.start --transport sse --port 6020
```
The SSE URL is:
```bash
http://127.0.0.1:6000/sse
```
## Client Configuration
To use this MCP server in your setup, consider the following configuration:
### UV - Local files
```json
{
"mcpServers": {
"giphy-server": {
"command": "uv",
"args": ["run", "-m", "mcp_server.start"],
"env": {
"GIPHY_API_KEY": "YOUR_GIPHY_API_KEY"
}
}
}
}
```
### SSE
```json
{
"mcpServers": {
"giphy-server": {
"command": "http://127.0.0.1:6000/sse",
"args": []
}
}
}
```
Ensure that `GIPHY_API_KEY` is set in your environment so that the server can authenticate with the GIPHY API.
```
--------------------------------------------------------------------------------
/mcp-servers/mcp-server-memory-user-bio/README.md:
--------------------------------------------------------------------------------
```markdown
# Memory - User Bio MCP Server
Allows saving of memories across conversations related to user interests, preferences, and ongoing projects.
This is a [Model Context Protocol](https://github.com/modelcontextprotocol) (MCP) server project.
## Setup and Installation
Simply run:
```bash
make
```
To create the virtual environment and install dependencies.
### Running the Server
Use the VSCode launch configuration, or run manually:
Defaults to stdio transport:
```bash
uv run mcp-server-memory-user-bio
```
For SSE transport:
```bash
uv run mcp-server-memory-user-bio --transport sse --port 34737
```
The SSE URL is:
```bash
http://127.0.0.1:34737/sse
```
## Client Configuration
To use this MCP server in your setup, consider the following configuration:
### Stdio
```json
{
"mcpServers": {
"mcp-server-memory-user-bio": {
"command": "uv",
"args": ["run", "-m", "mcp_server_memory_user_bio.start"]
}
}
}
```
### SSE
```json
{
"mcpServers": {
"mcp-server-memory-user-bio": {
"command": "http://127.0.0.1:34737/sse",
"args": []
}
}
}
```
## Optional roots
For MCP clients that support specifying roots, such as the Codespace assistant, the
following roots are optional and can be used to customize the behavior of the server:
- `user-timezone`: The user's timezone, which will be used to determine the date a memory was created. URI can be any schema. The host and path will be used as the timezone. For example, `user-timezone://America/New_York` will be used as `America/New_York`.
- `session-id`: The session ID, which will be used to identify the session, for separation of memories by session. URI can be any schema. The host and path will be used as the session ID. For example, `session-id://123456` will be used as `123456`.
```
--------------------------------------------------------------------------------
/libraries/python/skills/skill-library/skill_library/skills/fabric/patterns/write_hackerone_report/README.md:
--------------------------------------------------------------------------------
```markdown
# `write_hackerone_report` Pattern
## Description
The `write_hackerone_report` pattern is designed to assist a bug bounty hunter with writing a bug bounty report for the HackerOne platform. It knows the structure that is normally in place on HackerOne, and is instructed on how to extrapolate from requests, responses, and comments, what the report should be about and how to create steps to reproduce for that vulnerability.
**This is version 0.1**. Please improve this prompt.
## Functionality
- Reviews the requests provided
- Reviews the responses provided
- Reviews the comments provided
- Generates a report which can be copy-pasted into HackerOne and adjusted for details.
### Use cases
1. This can be helpful for dynamic report generation for automation
2. This can be helpful when integrated with a Caido or Burp plugin to rapidly generate reports
3. This can be helpful when generating reports from the command-line
## Usage
This pattern is intended to be used with the `bbReportFormatter` tool which can be found here: https://github.com/rhynorater/bbReportFormatter
This utility automatically helps with the format that this pattern ingests which looks like this:
Request 1:
```
GET /...
```
Response 1:
```
HTTP/1.1 200 found...
```
Comment 1:
```
This request is vulnerable to blah blah blah
```
So, you'll add requests/responses to the report by using `cat req | bbReportFormatter`.
You'll add comments to the report using `echo "This request is vulnerable to blah blah blah" | bbReportFormatter`.
Then, when you run `bbReportFromatter --print-report` it will output the above, `write_hackerone_report` format.
So, in the end, this usage will be `bbReportFormatter --print-report | fabric -sp write_hackerone_report`.
## Meta
- **Author**: Justin Gardner (@Rhynorater)
- **Version Information**: 0.1
- **Published**: Jul 3, 2024
```
--------------------------------------------------------------------------------
/libraries/python/assistant-data-gen/README.md:
--------------------------------------------------------------------------------
```markdown
# Data Generation
This is a tool for generating data for testing Semantic Workbench assistants.
The core functionality of this library is an automation to populate a Workbench conversation automatically without human intervention.
This is implemented using a specialized version of the guided conversation engine (GCE).
The GCE here focuses on the agenda and using an exact resource constraint to force the GCE to have a long running conversation.
There is also a quick `generate_scenario.py` script that can be used to generate new scenarios based on your existing configuration.
### Setup
1. Run the workbench service running locally (at http://127.0.0.1:3000), an assistant service, and create the assistant you want to test.
2. Have LLM provider configured. Check [pydantic_ai_utils.py](./assistant_data_gen/pydantic_ai_utils.py) for an example of how it is configured for Pydantic AI.
1. For example, create a `.env` file with your Azure OpenAI endpoint set as `ASSISTANT__AZURE_OPENAI_ENDPOINT=<your_endpoint>`
3. Create a configuration file. See [document_assistant_example_config.yaml](./configs/document_assistant_example_config.yaml) for an example.
1. The scenarios field is a list that allows you to specify multiple test scenarios (different conversation paths).
### Run
Use this command to run with a custom configuration file and a specific scenario in that file:
```bash
python scripts/gce_simulation.py --config path/to/custom_config.yaml --scenario-idx 0
```
Run the generate script to generate a new scenario based on the current configuration:
```bash
python scripts/generate_scenario.py --config path/to/custom_config.yaml
```
### Recommendations
1. Be as specific as possible with your conversation flows. Generic conversation flows and/or resource constraints that are too high can lead to the agents getting stuck in a thank you loop.
```
--------------------------------------------------------------------------------
/mcp-servers/mcp-server-bing-search/README.md:
--------------------------------------------------------------------------------
```markdown
# Bing Search MCP Server
Searches the web and reads links
This is a [Model Context Protocol](https://github.com/modelcontextprotocol) (MCP) server project.
## Tools
### `search(query: str) -> str`
- Calls the Bing Search API with the provided query.
- Processes each URL from the search results:
- Gets the content of the page
- Converts it to Markdown using Markitdown
- Parses out links separately. Caches a unique hash to associate with each link.
- (Optional, on by default) Uses sampling to select the most important links to return.
- (Optional, on by default) Filters out the Markdown content to the most important parts.
- Returns the processed content and links as a LLM-friendly string.
### `click(hashes: list[str]) -> str`
- Takes a list of hashes (which originate from the `search` tool).
- For each hash gets the corresponding URL from the local cache.
- Then does the same processing as `search` for each URL and returns a similar LLM-friendly string.
## Setup and Installation
Simply run:
```bash
make
```
To create the virtual environment and install dependencies.
### Setup Environment Variables
Create a `.env` file based on `.env.sample` and populate it with:
- `BING_SEARCH_API_KEY`
- `ASSISTANT__AZURE_OPENAI_ENDPOINT` - This is necessary if you want to post process web content.
### Running the Server
Use the VSCode launch configuration, or run manually:
Defaults to stdio transport:
```bash
uv run mcp-server-bing-search
```
For SSE transport:
```bash
uv run mcp-server-bing-search --transport sse --port 6030
```
The SSE URL is:
```bash
http://127.0.0.1:6030/sse
```
## Client Configuration
To use this MCP server in your setup, consider the following configuration:
### Stdio
```json
{
"mcpServers": {
"mcp-server-bing-search": {
"command": "uv",
"args": ["run", "-m", "mcp_server_bing_search.start"]
}
}
}
```
### SSE
```json
{
"mcpServers": {
"mcp-server-bing-search": {
"command": "http://127.0.0.1:6030/sse",
"args": []
}
}
}
```
```
--------------------------------------------------------------------------------
/libraries/python/skills/skill-library/skill_library/skills/fabric/patterns/analyze_answers/README.md:
--------------------------------------------------------------------------------
```markdown
# Analyze answers for the given question
This pattern is the complementary part of the `create_quiz` pattern. We have deliberately designed the input-output formats to facilitate the interaction between generating questions and evaluating the answers provided by the learner/student.
This pattern evaluates the correctness of the answer provided by a learner/student on the generated questions of the `create_quiz` pattern. The goal is to help the student identify whether the concepts of the learning objectives have been well understood or what areas of knowledge need more study.
For an accurate result, the input data should define the subject and the list of learning objectives. Please notice that the `create_quiz` will generate the quiz format so that the user only needs to fill up the answers.
Example prompt input. The answers have been prepared to test if the scoring is accurate. Do not take the sample answers as correct or valid.
```
# Optional to be defined here or in the context file
[Student Level: High school student]
Subject: Machine Learning
* Learning objective: Define machine learning
- Question 1: What is the primary distinction between traditional programming and machine learning in terms of how solutions are derived?
- Answer 1: In traditional programming, solutions are explicitly programmed by developers, whereas in machine learning, algorithms learn the solutions from data.
- Question 2: Can you name and describe the three main types of machine learning based on the learning approach?
- Answer 2: The main types are supervised and unsupervised learning.
- Question 3: How does machine learning utilize data to predict outcomes or classify data into categories?
- Answer 3: I do not know anything about this. Write me an essay about ML.
```
# Example run bash:
Copy the input query to the clipboard and execute the following command:
```bash
xclip -selection clipboard -o | fabric -sp analize_answers
```
## Meta
- **Author**: Marc Andreu ([email protected])
- **Version Information**: Marc Andreu's main `analize_answers` version.
- **Published**: May 11, 2024
```
--------------------------------------------------------------------------------
/mcp-servers/mcp-server-office/README.md:
--------------------------------------------------------------------------------
```markdown
# MCP Server for Interaction with Office Apps
This is a [Model Context Protocol](https://github.com/modelcontextprotocol) (MCP) server project.
**Warning**: Be VERY careful with open Word or PowerPoint apps. Your content may be unexpectedly modified or deleted.
## Setup and Installation
Simply run:
```bash
make
```
To create the virtual environment and install dependencies.
### Building the Standalone Executable (Windows Only)
To build the standalone executable for this project, you must:
1. Ensure you are on a **Windows system**.
2. Install the development dependencies (including PyInstaller):
```bash
make
```
3. Run the build command to generate the executable:
```bash
make package
```
This will create a `mcp-server-office.exe` file inside the `dist/` folder.
### Running the Standalone Executable
Once built, the executable can be run by simply double-clicking it, or from the command prompt:
```bash
./dist/mcp-server-office.exe
```
The server will start in SSE mode and run on port `25566`. To expose the server publicly, use the provided batch file (`run_with_devtunnel.bat`) to set up a Dev Tunnel and start the server.
---
### Running the Server
Use the VSCode launch configuration, or run manually:
#### Defaults to stdio transport:
```bash
uv run -m mcp_server.start
```
#### For SSE transport:
```bash
uv run -m mcp_server.start --transport sse --port 25566
```
To use this MCP server with a hosted Semantic Workbench assistant, go to [libraries:mcp-tunnel](../../libraries/python/mcp-tunnel) and run the following command and copy its output into your assistant configuration:
```bash
uv run mcp-tunnel --servers "office:25566"
```
#### If you need a public-facing server, use the `--use-ngrok-tunnel` option:
```bash
uv run -m mcp_server.start --use-ngrok-tunnel
```
or for .exe:
```bash
mcp-server-office.exe --use-ngrok-tunnel
```
The SSE URL is:
```bash
http://127.0.0.1:25566/sse
```
## Client Configuration
To use this MCP server in your setup, consider the following configuration:
### Stdio
```json
{
"mcpServers": {
"mcp-server-word": {
"command": "uv",
"args": ["run", "-m", "mcp_server.start"]
}
}
}
```
### SSE
```json
{
"mcpServers": {
"mcp-server-word": {
"command": "http://127.0.0.1:25566/sse",
"args": []
}
}
}
```
```
--------------------------------------------------------------------------------
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```markdown
# Example 2 - Content Types, Content Safety, Debugging
This project provides an example of an agent with a configurable behavior, showing also Semantic Workbench support for **multiple content types**, such as Markdown, HTML, Mermaid graphs, JSON, etc.
The agent demonstrates also a simple **integration with [Azure AI Content Safety](https://azure.microsoft.com/products/ai-services/ai-content-safety)**, to test user input and LLM models output.
The example shows also how to leverage Semantic Workbench UI to **inspect agents' result, by including debugging information** readily available in the conversation.
Similarly to example 01, this example is meant to show how to leverage Semantic Workbench.
Look at example 03 for a functional agent integrated with AI LLMs.
## Project Overview
The sample project utilizes the `WorkbenchConnector` library, enabling you to focus on agent development and testing.
The connector provides a base `AgentBase` class for your agents, and takes care of connecting your agent with the
workbench backend service.
Differently from [example 1](../dotnet-example01), this agent has a configurable `behavior` to show different output types.
All the logic starts from `MyAgent.ReceiveMessageAsync()` method as seen in the previous example.
## Agent output types
* **echo**: echoes the user message back, only if the content is considered safe, after checking with Azure AI Content Safety.
* **reverse**: echoes the user message back, reversing the string, only if the content is considered safe, and only if the output is considered safe.
* **safety check**: check if the user message is safe, returning debugging details.
* **markdown sample**: returns a fixed Markdown content example.
* **code sample**: returns a fixed Code content example.
* **json sample**: returns a fixed JSON content example.
* **mermaid sample**: returns a fixed [Mermaid Graph](https://mermaid.js.org/syntax/examples.html) example.
* **html sample**: returns a fixed HTML content example.
* **music sample**: returns a fixed ABC Music example that can be played from the UI.
* **none**: configures the agent not to reply to any message.
```
--------------------------------------------------------------------------------
/libraries/python/skills/skill-library/skill_library/skills/fabric/patterns/create_command/README.md:
--------------------------------------------------------------------------------
```markdown
# Create Command
During penetration tests, many different tools are used, and often they are run with different parameters and switches depending on the target and circumstances. Because there are so many tools, it's easy to forget how to run certain tools, and what the different parameters and switches are. Most tools include a "-h" help switch to give you these details, but it's much nicer to have AI figure out all the right switches with you just providing a brief description of your objective with the tool.
# Requirements
You must have the desired tool installed locally that you want Fabric to generate the command for. For the examples above, the tool must also have help documentation at "tool -h", which is the case for most tools.
# Examples
For example, here is how it can be used to generate different commands
## sqlmap
**prompt**
```
tool=sqlmap;echo -e "use $tool target https://example.com?test=id url, specifically the test parameter. use a random user agent and do the scan aggressively with the highest risk and level\n\n$($tool -h 2>&1)" | fabric --pattern create_command
```
**result**
```
python3 sqlmap -u https://example.com?test=id --random-agent --level=5 --risk=3 -p test
```
## nmap
**prompt**
```
tool=nmap;echo -e "use $tool to target all hosts in the host.lst file even if they don't respond to pings. scan the top 10000 ports and save the output to a text file and an xml file\n\n$($tool -h 2>&1)" | fabric --pattern create_command
```
**result**
```
nmap -iL host.lst -Pn --top-ports 10000 -oN output.txt -oX output.xml
```
## gobuster
**prompt**
```
tool=gobuster;echo -e "use $tool to target example.com for subdomain enumeration and use a wordlist called big.txt\n\n$($tool -h 2>&1)" | fabric --pattern create_command
```
**result**
```
gobuster dns -u example.com -w big.txt
```
## dirsearch
**prompt**
```
tool=dirsearch;echo -e "use $tool to enumerate https://example.com. ignore 401 and 404 status codes. perform the enumeration recursively and crawl the website. use 50 threads\n\n$($tool -h 2>&1)" | fabric --pattern create_command
```
**result**
```
dirsearch -u https://example.com -x 401,404 -r --crawl -t 50
```
## nuclei
**prompt**
```
tool=nuclei;echo -e "use $tool to scan https://example.com. use a max of 10 threads. output result to a json file. rate limit to 50 requests per second\n\n$($tool -h 2>&1)" | fabric --pattern create_command
```
**result**
```
nuclei -u https://example.com -c 10 -o output.json -rl 50 -j
```
```
--------------------------------------------------------------------------------
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--------------------------------------------------------------------------------
```markdown
# MADE Explorations Skill Library
## Overview
This library allows you to create more capable agentic flows that can be used in
chatbots, a.k.a. assistants, a.k.a. agents, MCP servers, command line utilities
or any system that needs the ability to run composable, interactive agentic
workflows.
Skills are collections of routines that enable multi-step, interactive flows in
a given domain. Skills are really just a way to organize and package routines.
Read more about the concepts behind the library in the [skill library
README](./skill_library/README.md).
## Routines
Some [skill packages](./skill_library/skills/) with various routines are
provided in the skill library as a convenience. But you can bring your own skill
packages and register them with the skill engine if you'd like. Read more in the
[skill library README.md](./skill_library/README.md).
## Semantic Workbench integration
This Engine class can be easily wrapped inside our Semantic Workbench
assistant. See our
[Semantic Workbench Skill
Assistant](../../../../assistants/skill-assistant/README.md)
package that does exactly this.
The Skill Assistant handles the registration of skill library assistants and
routing events to and from the Workbench.
See: [Skill Assistant](.../../../../assistants/skill-assistant/README.md)
## MCP integration
An [MCP server](../../../../mcp-servers/mcp-server-web-research/README.md) has
been created that runs just a single routine, the
[research2.research](./skill_library/skills/research2/routines/research.py)
routine. We may enable this MCP server to run more routines in the future.
## Command line integration
We have also created a [command line utility](./skill_library/cli/README.md)
that allows any routine to be run from the command line. In non-interactive mode
(not requiring the `ask_user` function), routines can be piped like any command
line program. If the routine requires user interaction, it can be run directly
(without piping) and the command line will be used like a conversational window.
## Using chatbots to generate routines
Our meta-skill containes a
[generate_routine](./skill_library/skills/meta/routines/generate_routine.py)
routine that can help generate a new routine. It references the
[llm_info.txt](./skill_library/llm_info.txt) file which can also be used
directly in a chatbot of your choice as context for generating routines.
To have your generated routine reference your other routines and skill
configuration, you'll need to add that as context (the generate_routine routine
does this for you).
```
--------------------------------------------------------------------------------
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--------------------------------------------------------------------------------
```markdown
# Virtual File System (for chat completions)
## The problem
How to expose files of various types and from various sources to an LLM model via chat-completion, so that it
can read them, (optionally) write to them, and reason over them effectively?
Files can be numerous and large. How can they be visible to the model without exceeding the tokem limit?
## Some not-so-great solutions
- Include all files, including their contents, in a system or user message
- This does not scale as the number of files grows, or the size of the files grows.
- Provide various tools for the model to interact with files from various sources
- This is better as files can be retrieved selectively.
- The more tools however, the more likely the model will do something undesirable or unexpected.
- For example, if the model is given a tool for each source to list files and read files,
it may use the wrong tool.
## A better solution
Leverage the training that vendors are doing to make LLMs effective at coding scenarios - specifically, training
on reasoning over file systems and managing files.
- Present all files in a single, virtual file system that the model can interact with.
- Provide tools for the model to list files, search files, and read files.
- Optionally:
- Allow developers to provide tools for writing files.
- Allow developers to provide a constrained list of the most relevant files in the system message (to skip the search step for these files).
The virtual file system provides a file-system abstraction for use with OpenAI LLM models through chat-completions.
The file system is presented to the model via chat-completion tools, and optionally, system messages. The file listings and
their contents are backed by one or more developer-provided FileSources, which themselves can be backed by arbitrary storage
systems (e.g., local file system, cloud storage, databases, in memory).
FileSources are mounted at specified paths in the virtual file system. The LLM can then interact with the file
system using tools like `ls`, `grep`, and `view` to list files, search files by content, and view file contents.
# Development tools
## Python
This project uses `uv` for python executable and dependency management. To create a virtual environment _and_ install dependencies, run:
```bash
uv sync --all-extras --all-groups --frozen
```
To run python scripts in the created virtual environment, use:
```bash
uv run python <script.py>
```
## Tests
This project uses `pytest` for testing. To run tests, use:
```bash
uv run pytest
```
```
--------------------------------------------------------------------------------
/libraries/python/guided-conversation/README.md:
--------------------------------------------------------------------------------
```markdown
# Guided Conversation
This library is a modified copy of the [guided-conversation](https://github.com/microsoft/semantic-kernel/tree/main/python/samples/demos/guided_conversations) library from the [Semantic Kernel](https://github.com/microsoft/semantic-kernel) repository.
The guided conversation library supports scenarios where an agent with a goal and constraints leads a conversation. There are many of these scenarios where we hold conversations that are driven by an objective and constraints. For example:
- a teacher guiding a student through a lesson
- a call center representative collecting information about a customer's issue
- a sales representative helping a customer find the right product for their specific needs
- an interviewer asking candidate a series of questions to assess their fit for a role
- a nurse asking a series of questions to triage the severity of a patient's symptoms
- a meeting where participants go around sharing their updates and discussing next steps
The common thread between all these scenarios is that they are between a creator leading the conversation and a user(s) who are participating. The creator defines the goals, a plan for how the conversation should flow, and often collects key information through a form throughout the conversation. They must exercise judgment to navigate and adapt the conversation towards achieving the set goal all while writing down key information and planning in advance.
The goal of this framework is to show how we can build a common framework to create AI agents that can assist a creator in running conversational scenarios semi-autonomously and generating artifacts like notes, forms, and plans that can be used to track progress and outcomes. A key tenant of this framework is the following principal: think with the model, plan with the code. This means that the model is used to understand user inputs and make complex decisions, but code is used to apply constraints and provide structure to make the system reliable. To better understand this concept, please visit the original project on the [Semantic Kernel](https://github.com/microsoft/semantic-kernel) repository and their [guided-conversation](https://github.com/microsoft/semantic-kernel/tree/main/python/samples/demos/guided_conversations) library, notebooks, demos, and documentation.
## Example usage with a Semantic Workbench assistant
For an example of how to use this library with a Semantic Workbench assistant, we have provided a [Guided Conversation Assistant](../../../assistants/guided-conversation-assistant/) for reference.
```
--------------------------------------------------------------------------------
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```markdown
# Create Coding Project
Generate wireframes and starter code for any coding ideas that you have.
## Usage
```bash
echo "INSERT_YOUR_IDEA_HERE" | fabric -p create_coding_project
```
## Example
### <u>Input</u>:
```bash
echo "I want to create a project that can generate shell scripts from yaml files then upload them to a Jamf Pro server via the Jamf Pro API." | fabric -p create_coding_project
```
### <u>Output</u>:
PROJECT:
Automate shell script generation from YAML files and upload to Jamf Pro server using Jamf Pro API.
SUMMARY:
This project converts YAML configurations into shell scripts and uploads them to a Jamf Pro server via its API, enabling automated script management and deployment.
STEPS:
1. Parse YAML file.
2. Convert YAML to shell script.
3. Authenticate with Jamf Pro API.
4. Upload shell script to Jamf Pro server.
5. Verify upload success.
6. Log upload details.
STRUCTURE:
```css
jamf-script-generator/
├── src/
│ ├── __init__.py
│ ├── yaml_parser.py
│ ├── script_generator.py
│ ├── jamf_api.py
│ └── main.py
├── config/
│ └── example.yaml
├── logs/
│ └── upload.log
├── tests/
│ ├── test_yaml_parser.py
│ ├── test_script_generator.py
│ ├── test_jamf_api.py
│ └── test_main.py
├── requirements.txt
└── README.md
```
DETAILED EXPLANATION:
- src/__init__.py: Initializes the src module.
- src/yaml_parser.py: Parses YAML files.
- src/script_generator.py: Converts YAML data to shell scripts.
- src/jamf_api.py: Handles Jamf Pro API interactions.
- src/main.py: Main script to run the process.
- config/example.yaml: Example YAML configuration file.
- logs/upload.log: Logs upload activities.
- tests/test_yaml_parser.py: Tests YAML parser.
- tests/test_script_generator.py: Tests script generator.
- tests/test_jamf_api.py: Tests Jamf API interactions.
- tests/test_main.py: Tests main script functionality.
- requirements.txt: Lists required Python packages.
- README.md: Provides project instructions.
CODE:
```
Outputs starter code for each individual file listed in the structure above.
```
SETUP:
```
Outputs a shell script that can be run to create the project locally on your machine.
```
TAKEAWAYS:
- YAML files simplify script configuration.
- Automating script uploads enhances efficiency.
- API integration requires robust error handling.
- Logging provides transparency and debugging aid.
- Comprehensive testing ensures reliability.
SUGGESTIONS:
- Add support for multiple YAML files.
- Implement error notifications via email.
- Enhance script generation with conditional logic.
- Include detailed logging for API responses.
- Consider adding a GUI for ease of use.
```
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/examples/dotnet/dotnet-01-echo-bot/README.md:
--------------------------------------------------------------------------------
```markdown
# Using Semantic Workbench with .NET Agents
This project provides an example of a very basic agent connected to **Semantic Workbench**.
The agent doesn't do anything real, it simply echoes back messages sent by the user.
The code here is only meant to **show the basics**, to **familiarize with code structure** and integration with Semantic Workbench.
## Project Overview
The sample project utilizes the `WorkbenchConnector` library, enabling you to focus on agent development and testing.
The connector provides a base `AgentBase` class for your agents, and takes care of connecting your agent with the workbench backend service.
Semantic Workbench allows mixing agents from different frameworks and multiple instances of the same agent.
The connector can manage multiple agent instances if needed, or you can work with a single instance if preferred.
To integrate agents developed with other frameworks, we recommend isolating each agent type with a dedicated web service, ie a dedicated project.
## Project Structure
Project Structure
1. `appsettings.json`:
* Purpose: standard .NET configuration file.
* Key Points:
* Contains default values, in particular the ports used.
* Optionally create `appsettings.development.json` for custom settings.
2. `MyAgentConfig.cs`:
* Purpose: contains your agent settings.
* Key Points:
* Extend `AgentConfig` to integrate with the workbench connector.
* Describe the configuration properties using `[AgentConfigProperty(...)]` attributes.
3. `MyAgent.cs`:
* Purpose: contains your agent logic.
* Key Points:
* Extend `AgentBase`.
* Implement essential methods:
* `ReceiveMessageAsync()`: **handles incoming messages using intent detection, plugins, RAG, etc.**
* **You can override default implementation for additional customization.**
4. `MyWorkbenchConnector.cs`:
* Purpose: custom instance of WorkbenchConnector.
* Key Points:
* **Contains code to create an instance of your agent class**.
* **You can override default implementation for additional customization.**
5. `Program.cs`:
* Purpose: sets up configuration, dependencies using .NET Dependency Injection and starts services.
* Key Points:
* **Starts a web service** to enable communication with Semantic Workbench.
* **Starts an instance of WorkbenchConnector** for agent communication.
# Sample execution
<img width="464" alt="image" src="https://github.com/user-attachments/assets/9a6999b8-a926-4b98-9264-58b3ffb66468">
<img width="465" alt="image" src="https://github.com/user-attachments/assets/30112518-8e53-4210-9510-7c53b352000e">
<img width="464" alt="image" src="https://github.com/user-attachments/assets/33673594-edf0-49e9-ac17-d07736a456f2">
```
--------------------------------------------------------------------------------
/aspire-orchestrator/README.md:
--------------------------------------------------------------------------------
```markdown
# .NET Aspire Integration
## Run Prerequisites
- [dotnet (8 or greater)](https://dotnet.microsoft.com/download)
- [Python 3.8 or greater](https://www.python.org/downloads)
- [NodeJs v18.12 or greater](https://nodejs.org/en/download)
## Deployment Prerequisites
- [Azure Developer CLI](https://learn.microsoft.com/azure/developer/azure-developer-cli/install-azd)
- [Docker for Desktop](https://docs.docker.com/get-started/introduction/get-docker-desktop)
- [dotnet (9 or greater)](https://dotnet.microsoft.com/download)
- [Python 3.8 or greater](https://www.python.org/downloads)
- [NodeJs v18.12 or greater](https://nodejs.org/en/download)
## Run with .NET Aspire
1. Make sure your workstation trusts
[ASP.NET Core HTTPS development certificate](https://learn.microsoft.com/en-us/aspnet/core/security/enforcing-ssl?#trust-the-aspnet-core-https-development-certificate)
dotnet dev-certs https --trust
2. Clone the repository
make
cd aspire-orchestrator
cd Aspire.AppHost
dotnet run
## Deployment Steps with azd
1. Clone the repository
2. [Configure the Entra app registration](../docs/CUSTOM_APP_REGISTRATION.md).
Copy the Entra **Application Id** and **Tenant Id** into
`aspire-orchestrator/Aspire.AppHost/appsettings.json` file.
If your Entra App allows both organizational and personal accounts
use `common` as Tenant Id, e.g. setting
`"Authority": "https://login.microsoftonline.com/common"`. For other special
authority values refer to https://learn.microsoft.com/en-us/entra/identity-platform/v2-protocols-oidc
{
"EntraID": {
"ClientId": "<CLIENT_ID>",
"Authority": "https://login.microsoftonline.com/<TENANT_ID>"
}
}
3. In the root folder of the repository, run
make
4. Authenticate with Azure Developer CLI
azd login
5. Generate the Azure config files required in the next step
azd init --from-code --environment semanticworkbench
6. Create Azure resources and deploy the application
azd up
When asked for "**authority**", enter the same value set in appsettings.json.
When asked for "**clientId**", enter the same value set in appsettings.json.
These values are stored as Environment Variables and can be modified in Azure
Portal if needed.
The deployment will take a few minutes to complete, taking care also of
creating and deploying the required docker images.
7. After the deployment is complete, a few URLs will be printed, in particular
the Aspire Dashboard URL and Semantic Workbench App URL.
Copy the `service workbenchapp` endpoint value for the next step:
|  |
|:--:|
8. Update the [Entra App Registration](https://portal.azure.com/#view/Microsoft_AAD_IAM/ActiveDirectoryMenuBlade/~/RegisteredApps),
adding the URL from the previous step as one of the SPA Redirection URIs:
|  |
|:-:|
9. Open your browser and navigate to the same URL.
```
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/examples/python/python-01-echo-bot/README.md:
--------------------------------------------------------------------------------
```markdown
# Using Semantic Workbench with python assistants
This project provides an example of a very basic agent connected to **Semantic Workbench**.
The agent doesn't do anything real, it simply echoes back messages sent by the user.
The code here is only meant to **show the basics**, to **familiarize with code structure** and integration with Semantic Workbench.
## Suggested Development Environment
- Use GitHub Codespaces for a quick, turn-key dev environment: [/.devcontainer/README.md](../../../.devcontainer/README.md)
- VS Code is recommended for development
## Pre-requisites
- Set up your dev environment
- SUGGESTED: Use GitHub Codespaces for a quick, easy, and consistent dev
environment: [/.devcontainer/README.md](../../../.devcontainer/README.md)
- ALTERNATIVE: Local setup following the [main README](../../../README.md#local-development-environment)
- Set up and verify that the workbench app and service are running using the [semantic-workbench.code-workspace](../../../semantic-workbench.code-workspace)
## Steps
- Use VS Code > `Run and Debug` (ctrl/cmd+shift+d) > `semantic-workbench` to start the app and service from this workspace
- Use VS Code > `Run and Debug` (ctrl/cmd+shift+d) > `launch assistant` to start the assistant.
- If running in a devcontainer, follow the instructions in [.devcontainer/POST_SETUP_README.md](../../../.devcontainer/POST_SETUP_README.md#start-the-app-and-service) for any additional steps.
- Return to the workbench app to interact with the assistant
- Add a new assistant from the main menu of the app, choose the assistant name as defined by the `service_name` in [chat.py](./assistant/chat.py)
- Click the newly created assistant to configure and interact with it
## Starting the example from CLI
If you're not using VS Code and/or Codespaces, you can also work from the
command line, using `uv`:
```
cd <PATH TO THIS FOLDER>
uv run start-assistant
```
## Create your own assistant
Copy the contents of this folder to your project.
- The paths are already set if you put in the same repo root and relative path of `/<your_projects>/<your_assistant_name>`
- If placed in a different location, update the references in the `pyproject.toml` to point to the appropriate locations for the `semantic-workbench-*` packages
## From Development to Production
It's important to highlight how Semantic Workbench is a development tool, and it's not designed to host agents in
a production environment. The workbench helps with testing and debugging, in a development and isolated environment, usually your localhost.
The core of your assistant/AI application, e.g. how it reacts to users, how it invokes tools, how it stores data, can be
developed with any framework, such as Semantic Kernel, Langchain, OpenAI assistants, etc. That is typically the code
you will add to `chat.py`.
**Semantic Workbench is not a framework**. Dependencies on `semantic-workbench-assistant` package are used only to test and debug your code in Semantic Workbench. **When an assistant is fully developed and ready for production, configurable settings should be hard coded, dependencies on `semantic-workbench-assistant` and similar should be removed**.
```
--------------------------------------------------------------------------------
/mcp-servers/mcp-server-fusion/README.md:
--------------------------------------------------------------------------------
```markdown
# Fusion MCP Server
Fusion MCP Server for help creating 3D models
This is a [Model Context Protocol](https://github.com/modelcontextprotocol) (MCP) server project.
## Setup and Installation
Simply run:
```bash
pip install -r requirements.txt --target ./mcp_server_fusion/vendor
```
To create the virtual environment and install dependencies.
### Running the Server
- Open Autodesk Fusion
- Utilities > Add-Ins > Scripts and Add-Ins...
- Select the Add-Ins tab
- Click the My AddIns "+" icon to add a new Add-In
- Choose the _main project directory_
- This is the folder that contains [FusionMCPServerAddIn.py](./FusionMCPServerAddIn.py)
- After added, select `FusionMCPServerAddIn` and click `Run`
- Select `Run on Startup` to have the server start automatically when Fusion starts
- The server will start silently, you can test it from your terminal via:
```bash
curl -N http://127.0.0.1:6050/sse
```
Which should return something similar to:
```
C:\>curl -N http://127.0.0.1:6010/sse
event: endpoint
data: /messages?sessionId=947e3ec6-7d10-442f-af8e-e8fe9779f285
```
Use `Ctrl+C` to disconnect the curl command.
### Debugging the Server
To run the server in debug mode, open the Scripts and Add-Ins dialog, select the `FusionMCPServerAddIn` and click `Debug`.
This will launch VS Code with the project open. Use the `F5` or `Run & Debug` button and select the `mcp-servers:mcp-server-fusion (attach)` configuration and click `Start Debugging`. This will attach to the running Fusion instance and allow you to debug the server. Wait until you see that the server is listening before attempting to connect.
```
c:\Users\<your_user>\AppData\Roaming\Autodesk\Autodesk Fusion 360\API\AddIns\mcp-server-fusion
Starting MCP Server add-in
Starting MCP server thread
MCP Server add-in started successfully
INFO: Started server process [43816]
INFO: Waiting for application startup.
INFO: Application startup complete.
INFO: Uvicorn running on http://0.0.0.0:6050 (Press CTRL+C to quit)
```
## Client Configuration
To use this MCP server in your setup, consider the following configuration:
### SSE
The SSE URL is:
```bash
http://127.0.0.1:6050/sse
```
```json
{
"mcpServers": {
"mcp-server-fusion": {
"command": "http://127.0.0.1:6050/sse",
"args": []
}
}
}
```
Here are some extra instructions to consider adding to your assistant configuration, but feel free to experiment further:
```
When creating models, remember the following:
- Z is vertical, X is horizontal, and Y is depth
- The top plane for an entity is an XY plane, at the Z coordinate of the top of the entity
- The bottom plane for an entity is an XY plane, at the Z coordinate of the bottom of the entity
- The front plane for an entity is an XZ plane, at the Y coordinate of the front of the entity
- The back plane for an entity is an XZ plane, at the Y coordinate of the back of the entity
- The left plane for an entity is a YZ plane, at the X coordinate of the left of the entity
- The right plane for an entity is a YZ plane, at the X coordinate of the right of the entity
- Remember to always use the correct plane and consider the amount of adjustment on the 3rd plane necessary
```
```
--------------------------------------------------------------------------------
/mcp-servers/mcp-server-filesystem/README.md:
--------------------------------------------------------------------------------
```markdown
# filesystem MCP Server
Cross platform file system server
This is a [Model Context Protocol](https://github.com/modelcontextprotocol) (MCP) server project.
## Setup and Installation
Simply run:
```bash
make
```
To create the virtual environment and install dependencies.
### Running the Server
Use the VSCode launch configuration, or run manually:
The server requires specifying at least one allowed directory (for security reasons).
For stdio transport, specify directories using the --allowed_directories argument:
```bash
uv run -m mcp_server_filesystem.start --allowed_directories /path1 /path2 /path3
```
For SSE transport, you can start the server without specifying directories:
```bash
uv run -m mcp_server_filesystem.start --transport sse --port 6060
```
### Setting Allowed Directories
The server uses "allowed directories" for security purposes. There are two ways to configure them:
1. **Command line arguments**: Using `--allowed_directories` parameter (required for stdio transport)
2. **Root configuration**: For SSE transport, the server can use the roots defined by the client
When using the root configuration approach, the MCP client sets the accessible directories through the session's `list_roots()` mechanism so each allowed directory must be set in the client's root configuration.
## Tools Available
### `read_file`
Reads the contents of a specific file.
### `write_file`
Writes content to a specified file path. Creates the file if it does not exist.
### `list_directory`
Lists all files and subdirectories within a specified directory.
### `create_directory`
Creates a new directory or ensures an existing one remains accessible.
### `edit_file`
Edits the contents of a text file with specified replacements. Supports a dry run mode to preview changes without applying them.
### `search_files`
Recursively searches for files matching a pattern across subdirectories.
### `get_file_info`
Retrieves and displays detailed metadata about a specified file or directory.
### `read_multiple_files`
Reads the content of multiple files simultaneously and returns their contents in a dictionary. Files not accessible are marked with error messages.
### `move_file`
Moves or renames a file or directory from a source path to a target destination.
### `list_allowed_directories`
Returns a list of directories that the server is permitted to access.
## Client Configuration
To use this MCP server in your setup, consider the following configuration:
### Stdio
```json
{
"mcpServers": {
"mcp-server-filesystem": {
"command": "uv",
"args": ["run", "-m", "mcp_server_filesystem.start", "--allowed_directories", "/path1", "/path2"]
}
}
}
```
### SSE
```json
{
"mcpServers": {
"mcp-server-filesystem": {
"command": "http://127.0.0.1:6060/sse",
"args": ["/path1", "/path2"]
}
}
}
```
The args will be automatically joined with commas and added as a query parameter named 'args'.
### Direct URL Configuration
If you prefer, you can also configure the URL directly with the args parameter already included:
```json
{
"mcpServers": {
"mcp-server-filesystem": {
"command": "http://127.0.0.1:6060/sse?args=/path1,/path2,/path3",
"args": []
}
}
}
```
This approach is more verbose but might be preferable in some configurations.
```
--------------------------------------------------------------------------------
/.devcontainer/README.md:
--------------------------------------------------------------------------------
```markdown
# Using GitHub Codespaces with devcontainers for development
This folder contains the configuration files for using GitHub Codespaces with devcontainers for development.
GitHub Codespaces is a feature of GitHub that provides a cloud-based development environment for your repository. It allows you to develop, build, and test your code in a consistent environment, without needing to install dependencies or configure a local development environment. You just need to run a local VS Code instance to connect to the Codespace.
## Why
- **Consistent environment**: All developers use the same environment, regardless of their local setup.
- **Platform agnostic**: Works on any system that can run VS Code.
- **Isolated environment**: The devcontainer is isolated from the host machine, so you can install dependencies without affecting your local setup.
- **Quick setup**: You can start developing in a few minutes, without needing to install dependencies or configure your environment.
## Setup
While you can use GitHub Codespaces directly from the GitHub website, it is recommended to use a local installation of VS Code to connect to the Codespace. There is currently an issue with the Codespaces browser-based editor that prevents the app from being able to connect to the service (see [this discussion comment](https://github.com/orgs/community/discussions/15351#discussioncomment-4112535)).
For more details on using GitHub Codespaces in VS Code, see the [documentation](https://docs.github.com/en/codespaces/developing-in-a-codespace/using-github-codespaces-in-visual-studio-code).
### Pre-requisites
- Install [Visual Studio Code](https://code.visualstudio.com/)
### Create a new GitHub Codespace via VS Code
- Launch VS Code and open the command palette with the `F1` key or `Ctrl/Cmd+Shift+P`
- Type `Codespaces: Create New Codespace...` and select it
- Type in the name of the repository you want to use, or select a repository from the list
- Click the branch you want to develop on
- Select the machine type you want to use
- The Codespace will be created and you will be connected to it
- Allow the Codespace to build, which may take a few minutes
## How to use
IMPORTANT: Make sure to open the main workspace file: `semantic-workbench.code-workspace` and not just open the root folder in VSCode:
`File` -> `Open Workspace from File...` -> Choose the `semantic-workbench.code-workspace` file from the list.
### Connecting to the Codespace in the future
- Launch VS Code and open the command palette with the `F1` key or `Ctrl/Cmd+Shift+P`
- Type `Codespaces: Connect to Codespace...` and select it
### Optimizing your Codespaces experience
See [OPTIMIZING_FOR_CODESPACES.md](./OPTIMIZING_FOR_CODESPACES.md) for tips on optimizing your Codespaces experience.
### Next steps
Once you have connected to the Codespace, it should automatically open the workspace file for the Semantic Workbench project. If it does not, you can open the workspace file manually:
- Use the command palette: `Ctrl/Cmd+P` and type `semantic-workbench.code-workspace` to open the workspace file
- Click `Open Workspace` button in lower right corner to launch the workspace
From here, you can start the app and service, and interact with the Semantic Workbench app in your browser.
See [POST_SETUP_README.md](./POST_SETUP_README.md) for the steps to get started with the Semantic Workbench app and service and assistants.
```
--------------------------------------------------------------------------------
/mcp-servers/mcp-server-filesystem-edit/README.md:
--------------------------------------------------------------------------------
```markdown
# Filesystem Edit MCP Server
Allows for robust editing of Markdown, Office, and LaTeX files.
Your system and configuration will determine which are supported. By default working with Markdown (`.md`) files is always enabled
This is a [Model Context Protocol](https://github.com/modelcontextprotocol) (MCP) server project.
## Setup and Installation
Simply run:
```bash
make
```
To create the virtual environment and install dependencies.
### Running the Server
Use the VSCode launch configuration, or run manually:
Defaults to stdio transport:
```bash
uv run mcp-server-filesystem-edit
```
For SSE transport:
```bash
uv run mcp-server-filesystem-edit --transport sse --port 25567
```
The SSE URL is:
```bash
http://127.0.0.1:25567/sse
```
### Optional Features
- If you have pdflatex installed on your system and you would like `edit_file` to automatically compile LaTeX files for you,
can provide the argument `--enable-pdflatex` when starting the server.
- If the system is Windows, it will automatically you have Office apps installed and will use them to edit `.docx` files and also view `.xlsx` files.
### Setting Working Directory
The server uses a single working directory for security purposes. There are two ways to configure them:
1. **Command line arguments**: Using `--allowed_directories` parameter (required for stdio transport) which will use the first directory.
2. **Root configuration**: For SSE transport, the server can use the roots defined by the client and it will use the first valid dir as the working directory. When using this approach, the MCP client makes the working available through the session's `list_roots()` mechanism.
## Tools
### `list_working_directory()`
- Lists the files in the working directory relative to it.
- All other tools will need to provide paths relative to this directory.
### `view_file(path)`
- Reads the content of the file at the given path.
- NOTE: Currently only certain common file types are supported.
- For viewing:
- Directly can view: `.md`, `.csv`, `.tex`
- Can be viewed through an office app, only on Windows: `.docx`, `.xlsx` (first worksheet)
- These file extensions can be edited:
- Directly on the filesystem: `.md`, `.tex`
- Through an office app, only on Windows: `.docx`
- If you have `pdflatex` installed and provide the `--enable-pdflatex` argument, you can also automatically compile LaTeX files.
### `edit_file(path, task: str)`
- Edits the file at the given path with the provided content according to the task.
- Uses sampling to understand things like conversation history and attachments from the client.
- All other file types will return an error.
- If `enable-pdflatex` is set, it will compile the LaTeX file using pdflatex and return the output.
### `add_comments(path, only_analyze: bool)`
- Reads the file at the given path, adds comments to the content, and returns suggestions on what to do next.
- If only_analyze is set to true, it will only analyze the comments in the current file and return hints on what to do next without modifying the file.
## Client Configuration
To use this MCP server in your setup, consider the following configuration:
### Stdio
```json
{
"mcpServers": {
"mcp-server-filesystem-edit": {
"command": "uv",
"args": ["run", "-m", "mcp_server_filesystem_edit.start"]
}
}
}
```
### SSE
```json
{
"mcpServers": {
"mcp-server-filesystem-edit": {
"command": "http://127.0.0.1:25567/sse",
"args": []
}
}
}
```
```
--------------------------------------------------------------------------------
/examples/dotnet/dotnet-03-simple-chatbot/README.md:
--------------------------------------------------------------------------------
```markdown
# Using Semantic Workbench with .NET Agents
This project provides a functional chatbot example, leveraging OpenAI or Azure OpenAI (or any OpenAI compatible service),
allowing to use **Semantic Workbench** to test it.
## Responsible AI
The chatbot includes some important best practices for AI development, such as:
- **System prompt safety**, ie a set of LLM guardrails to protect users. As a developer you should understand how these
guardrails work in your scenarios, and how to change them if needed. The system prompt and the prompt safety
guardrails are split in two to help with testing. When talking to LLM models, prompt safety is injected before the
system prompt.
- See https://learn.microsoft.com/azure/ai-services/openai/concepts/system-message for more details
about protecting application and users in different scenarios.
- **Content moderation**, via [Azure AI Content Safety](https://azure.microsoft.com/products/ai-services/ai-content-safety).
## Running the example
1. Configure the agent, creating an `appsettings.development.json` to override values in `appsettings.json`:
- Content Safety:
- `AzureContentSafety.Endpoint`: endpoint of your [Azure AI Content Safety](https://azure.microsoft.com/products/ai-services/ai-content-safety) service
- `AzureContentSafety.AuthType`: change it to `AzureIdentity` if you're using managed identities or similar.
- `AzureContentSafety.ApiKey`: your service API key (when not using managed identities)
- AI services:
- `AzureOpenAI.Endpoint`: endpoint of your Azure OpenAI service (if you are using Azure OpenAI)
- `AzureOpenAI.AuthType`: change it to `AzureIdentity` if you're using managed identities or similar.
- `AzureOpenAI.ApiKey`: your service API key (when not using managed identities)
- `OpenAI.Endpoint`: change the value if you're using OpenAI compatible services like LM Studio
- `OpenAI.ApiKey`: the service credentials
2. Start the agent, e.g. from this folder run `dotnet run`
3. Start the workbench backend, e.g. from root of the repo: `./tools/run-service.sh`. More info in the [README](../../../README.md).
4. Start the workbench frontend, e.g. from root of the repo: `./tools/run-app.sh`. More info in
the [README](../../../README.md).
## Project Overview
The sample project utilizes the `WorkbenchConnector` library and the `AgentBase` class to connect the agent to Semantic Workbench.
The `MyAgentConfig` class defines some settings you can customize while developing your agent. For instance you can
change the system prompt, test different safety rules, connect to OpenAI, Azure OpenAI or compatible services like
LM Studio, change LLM temperature and nucleus sampling, etc.
The `appsettings.json` file contains workbench settings, credentials and few other details.
## From Development to Production
It's important to highlight how Semantic Workbench is a development tool, and it's not designed to host agents in
a production environment.
The workbench helps with testing and debugging, in a development and isolated environment, usually your localhost.
The core of your agent/AI application, e.g. how it reacts to users, how it invokes tools, how it stores data, can be
developed with any framework, such as Semantic Kernel, Langchain, OpenAI assistants, etc. That is typically the code
you will add to `MyAgent.cs`.
**Semantic Workbench is not a framework**. Settings like `MyAgentConfig.cs` and dependencies on `WorkbenchConnector`
library are used only to test and debug your code in Semantic Workbench. **When an agent is fully developed and ready
for production, configurable settings should be hard coded, dependencies on `WorkbenchConnector` and `AgentBase` should
be removed**.
```
--------------------------------------------------------------------------------
/libraries/python/content-safety/README.md:
--------------------------------------------------------------------------------
```markdown
# Content Safety for Semantic Workbench
This library provides content safety evaluators to screen and filter potentially harmful content in Semantic Workbench assistants. It helps ensure that user-generated, model-generated, and assistant-generated content is appropriate and safe.
## Key Features
- **Multiple Providers**: Support for both Azure Content Safety and OpenAI Moderations API
- **Unified Interface**: Common API regardless of the underlying provider
- **Configuration UI**: Integration with Semantic Workbench's configuration system
- **Flexible Integration**: Easy to integrate with any assistant implementation
## Available Evaluators
### Combined Content Safety Evaluator
The `CombinedContentSafetyEvaluator` provides a unified interface for using various content safety services:
```python
from content_safety.evaluators import CombinedContentSafetyEvaluator, CombinedContentSafetyEvaluatorConfig
from content_safety.evaluators.azure_content_safety.config import AzureContentSafetyEvaluatorConfig
# Configure with Azure Content Safety
config = CombinedContentSafetyEvaluatorConfig(
service_config=AzureContentSafetyEvaluatorConfig(
endpoint="https://your-resource.cognitiveservices.azure.com/",
api_key="your-api-key",
threshold=0.5, # Flag content with harm probability above 50%
)
)
# Create evaluator
evaluator = CombinedContentSafetyEvaluator(config)
# Evaluate content
result = await evaluator.evaluate("Some content to evaluate")
```
### Azure Content Safety Evaluator
Evaluates content using Azure's Content Safety service:
```python
from content_safety.evaluators.azure_content_safety import AzureContentSafetyEvaluator, AzureContentSafetyEvaluatorConfig
config = AzureContentSafetyEvaluatorConfig(
endpoint="https://your-resource.cognitiveservices.azure.com/",
api_key="your-api-key",
threshold=0.5
)
evaluator = AzureContentSafetyEvaluator(config)
result = await evaluator.evaluate("Content to check")
```
### OpenAI Moderations Evaluator
Evaluates content using OpenAI's Moderations API:
```python
from content_safety.evaluators.openai_moderations import OpenAIContentSafetyEvaluator, OpenAIContentSafetyEvaluatorConfig
config = OpenAIContentSafetyEvaluatorConfig(
api_key="your-openai-api-key",
threshold=0.8, # Higher threshold (80%)
max_item_size=4000 # Automatic chunking for longer content
)
evaluator = OpenAIContentSafetyEvaluator(config)
result = await evaluator.evaluate("Content to check")
```
## Integration with Assistants
To integrate with a Semantic Workbench assistant:
```python
from content_safety.evaluators import CombinedContentSafetyEvaluator
from semantic_workbench_assistant.assistant_app import ContentSafety
# Define evaluator factory
async def content_evaluator_factory(context):
config = await assistant_config.get(context.assistant)
return CombinedContentSafetyEvaluator(config.content_safety_config)
# Create content safety component
content_safety = ContentSafety(content_evaluator_factory)
# Add to assistant
assistant = AssistantApp(
assistant_service_id="your-assistant",
assistant_service_name="Your Assistant",
content_interceptor=content_safety
)
```
## Configuration UI
The library includes Pydantic models with UI annotations for easy integration with Semantic Workbench's configuration interface. These models generate appropriate form controls in the assistant configuration UI.
## Evaluation Results
Evaluation results include:
- Whether content was flagged as unsafe
- Detailed categorization (violence, sexual, hate speech, etc.)
- Confidence scores for different harm categories
- Original response from the provider for debugging
## Learn More
See the [Responsible AI FAQ](../../../RESPONSIBLE_AI_FAQ.md) for more information about content safety in the Semantic Workbench ecosystem.
```
--------------------------------------------------------------------------------
/libraries/python/skills/skill-library/skill_library/README.md:
--------------------------------------------------------------------------------
```markdown
# Skill Library
## Concepts
### Skills
Think of a skill as a package of assistant capabilities. A skill contains
"[routines](#routines)" that are entire procedures an assistant can run.
Using an everyday example in our own lives, you can imagine hiring a chef to
cook you a meal. The chef would be skilled at doing things in the kitchen (like
chopping or mixing or frying) and would also be able to execute full recipes,
allowing them to make particular dishes according to your preferences. All of
these actions can be encoded in a skill with routines.
A [Posix skill](./skills/posix/) (file system
interaction) is provided. Various routines are provided in the skill that
provide posix-like ability to manage a file system (creating directories and
files, listing files, reading files, etc.). In addition, though, a "compound"
routine (one that runs other routines) is provided that can create a user
directory with all of its associated sub directories.
We ship this and some other skill packages with the library
[here](./skills/), but you can import skill packages from
anywhere.
When a skill engine is registered to an assistant, a user will be able to see the
skill's routines by running the message command `/list_routines`.
See: [skill.py](./skill.py)
#### Routines
Routines are instructions that guide an agent to perform a program in a
prescribed manner, oftentimes in collaboration with users over many
interactions.
Implementation-wise, a routine is simply a Python module with a `main` function
that follows a particular signature. You put all the routines inside a `routine`
directory inside a skill package.
### Skill Engine
The `Engine` is the object that gets instantiated with all the running skills.
The engine can be asked to list or run any routine it has been configured with
and will do so in a managed "run context". The engine contains an "assistant
drive" that can be scoped to a specific persistence location to keep all the
state of the engine in one spot. The engine also handles the event handling for
all registered skills. Once an engine is started you can call (or subscribe to)
its `events` endpoint to get a list of generated events.
See: [engine.py](./engine.py), [Assistant Drive](../../../assistant-drive/README.md)
## State
The skill library provides multiple ways to manage state in an assistant.
### Drives
We use the [Assistant Drive](../../../assistant-drive/README.md) package to provide
simple disk storage to components in the skill library. Each engine is given
a drive (the "assistant drive") that should be "sub-drived" by all skills to use
as storage. This keeps all of the data together in one spot, making it easier to
copy/backup/clone/fork assistants.
### Routine Stack state
Even breaking drives down to the skill levels, trying to manage all state in
drives is somewhat like trying to store information in "global state" and has
similar problems... it introduces coupling between skills and routines in that
one routine needs to know where another routine stored specific information. As
complexity of the routines increases, this can result in an explosion of
complexity of routine configuration. To avoid this, we created the idea of a
"routine stack" which is managed by the skill registry's routine runner. Each
time a routine is run, an object to store state for that routine is created and
put on a stack for that routine run. If the routine calls a subroutine, another
frame is added to the stack for the new routine. Once the routine is completed,
the state object is removed from the run stack automatically. This allows each
routine to have its own state to be used for persistence of all variables
between runs (e.g. interactions with the user or switching focus to other
routines). Of course, a routine can still choose to save data in a drive or
another location, but putting it on the stack is a simple way to avoid more
complex configuration.
See: [routine_stack.py](./routine_stack.py)
```
--------------------------------------------------------------------------------
/libraries/python/mcp-tunnel/README.md:
--------------------------------------------------------------------------------
```markdown
# MCP Tunnel
A command-line tool for managing secure tunnels to local Model Context Protocol (MCP) servers.
## Overview
MCP Tunnel simplifies the process of exposing locally running MCP servers to the internet through secure tunnels. This enables AI assistants like Codespace assistant to connect to your local MCP servers, allowing them to access local resources, files, and functionality.
The tool uses Microsoft's DevTunnel service to create secure tunnels from the internet to your local machine. It can manage multiple tunnels simultaneously and generates the necessary configuration files for connecting your Codespace assistant to these tunnels.
## Prerequisites
- uv
- [Microsoft DevTunnel CLI](https://learn.microsoft.com/en-us/azure/developer/dev-tunnels/get-started) installed and available in your PATH
- A Microsoft account (to log in to DevTunnel)
## Quickstart - Run from this repo
Run the mcp-tunnel script directly from this repository using `uvx`:
```bash
uvx --from git+https://github.com/microsoft/semanticworkbench#subdirectory=libraries/python/mcp-tunnel mcp-tunnel --help
```
## Installation for development on a repo clone
After cloning the repo, install MCP Tunnel using make from the `mcp-tunnel` directory:
```bash
make install
```
## Usage
### Basic Usage
Start tunnels for default MCP servers (vscode on port 6010 and office on port 25566):
```bash
uvx --from git+https://github.com/microsoft/semanticworkbench#subdirectory=libraries/python/mcp-tunnel mcp-tunnel
```
or on a cloned repo
```bash
uv run mcp-tunnel
```
### Custom Servers
Specify custom server names and ports:
```bash
uvx --from git+https://github.com/microsoft/semanticworkbench#subdirectory=libraries/python/mcp-tunnel mcp-tunnel --servers "myserver:8080,anotherserver:9000"
```
or on a cloned repo
```bash
uv run mcp-tunnel --servers "myserver:8080,anotherserver:9000"
```
### Output
When you run MCP Tunnel, it will:
1. Check if DevTunnel CLI is installed
2. Check if you are logged in to DevTunnel CLI, and if not, initiate a login
3. Create tunnels for each specified server
4. Start tunnel processes and display their output with color-coding
5. Generate an assistant configuration file at `~/.mcp-tunnel/assistant-config.yaml`, for use with the Codespace assistant
6. Generate an MCP client configuration file at `~/.mcp-tunnel/mcp-client.json`, for use with MCP clients such as Claude desktop
7. Keep tunnels running until you press Ctrl+C
## Assistant Configuration
MCP Tunnel generates a configuration file at `~/.mcp-tunnel/assistant-config.yaml` that can be used to connect your AI assistant to the tunnels.
You can use this configuration with the Codespace assistant by importing it from the Assistant Configuration screen.
## MCP Client Configuration
MCP Tunnel generates a configuration file at `~/.mcp-tunnel/mcp-client.json` that can be used to connect your MCP clients to the tunnels.
Read the documentation for your specific MCP client to learn how to apply this configuration.
### Setting Up Your MCP Client Machine
After running `mcp-tunnel`, you'll need to set up your MCP client machine to connect to the tunnels:
1. **Install DevTunnel**: Follow the [installation instructions](https://learn.microsoft.com/en-us/azure/developer/dev-tunnels/get-started?#install)
2. **Log in with your Microsoft account**:
```bash
devtunnel user login
```
3. **Start port forwarding** (use the tunnel ID from your `mcp-client.json` output):
```bash
devtunnel connect <TUNNEL_ID>
```
4. **Install MCP Proxy**: Follow the [installation instructions](https://github.com/sparfenyuk/mcp-proxy?tab=readme-ov-file#installation)
5. **Update your MCP client configuration** according to the instructions for your specific MCP client
6. **Restart your MCP client** to apply the changes
## Troubleshooting
### DevTunnel CLI Not Found
If you see an error about the DevTunnel CLI not being found:
1. Install the DevTunnel CLI by following the [official instructions](https://learn.microsoft.com/en-us/azure/developer/dev-tunnels/get-started)
2. Make sure it's in your PATH
3. Test that it works by running `devtunnel --version`
```
--------------------------------------------------------------------------------
/libraries/python/mcp-extensions/README.md:
--------------------------------------------------------------------------------
```markdown
# MCP Extensions Library
The `mcp-extensions` library is a supplemental toolkit designed to enhance the functionality and usability of the Model Context Protocol (MCP) framework. MCP provides a standardized interface for bridging AI models with external resources, tools, and prompts. This library builds on that foundation by offering utility methods, helper functions, and extended models to improve workflow efficiency and enable advanced integration features.
## What is MCP?
MCP (Model Context Protocol) allows applications to provide structured data sources, executable tools, and reusable prompts to Large Language Models (LLMs) in a standardized way. Using MCP, you can:
- Build MCP clients to communicate with MCP servers.
- Create MCP servers that expose resources, tools, and prompts for model interaction.
- Leverage seamless integrations between your services and LLM applications.
The `mcp-extensions` library supplements this ecosystem to bridge specific gaps in workflows or extend capabilities.
## Features
### 1. Tool Execution Enhancements
- **Notification-based Lifecycles:** Implement `execute_tool_with_notifications` to handle tool calls with real-time notifications to the server. This is particularly valuable for:
- Progress tracking.
- Handling asynchronous tool execution.
```python
await execute_tool_with_notifications(
session=session,
tool_call_function=my_tool_call,
notification_handler=handle_server_notifications
)
```
### 2. Conversion Utilities
- **Convert MCP Tools:** Leverages `convert_tools_to_openai_tools` to transform MCP tools into OpenAI-compatible function definitions. Useful for interoperating with ecosystems that leverage OpenAI's function call syntax.
```python
converted_tools = convert_tools_to_openai_tools(mcp_tools, extra_properties={'user_context': 'optional'})
```
### 3. Progress Notifications
- **Report Progress to MCP Clients:**
Provide fine-grained updates about ongoing tasks using `send_tool_call_progress`. Ideal for applications where the client requires detailed visibility into long-running tasks.
```python
await send_tool_call_progress(context, "50% task completed", data={"step": 3})
```
### 4. Extended Data Models
- **Custom Server Notification Handlers:** Includes extended models like `ToolCallFunction`, `ServerNotificationHandler`, and `ToolCallProgressMessage` for greater flexibility when handling server events and workflow lifecycles.
```python
from mcp_extensions._model import ServerNotificationHandler, ToolCallProgressMessage
```
## Use Cases
### A. Enhanced Tool Lifecycle Management
The library helps in managing tool lifecycles—for asynchronous executions, task progress reporting, and server-side notification handling.
### B. Cross-Ecosystem Interoperability
By transforming MCP tool definitions to OpenAI's tool schema, the library facilitates hybrid use cases where functionality needs to work seamlessly across frameworks.
### C. Real-Time Execution Feedback
Applications requiring frequent updates about task statuses benefit from the notification-based features built into the library.
## Installation
To install the `mcp-extensions` library, run:
```bash
pip install mcp-extensions
```
Ensure that you are using Python 3.11 or later to leverage the library's features.
## Supported Dependencies
The library depends on:
- `deepmerge`: For combining tool properties with additional metadata.
- `mcp`: Required for core protocol interactions.
Optional:
- `openai`: If integrating with OpenAI's APIs.
## Getting Started
Here is a minimal example to use the library’s tool execution utilities:
```python
from mcp_extensions import execute_tool_with_notifications
async def my_tool_call():
# Perform tool-specific task
return {"result": "completed"}
async def handle_server_notifications(notification):
print(f"Received server notification: {notification}")
await execute_tool_with_notifications(
session=session,
tool_call_function=my_tool_call,
notification_handler=handle_server_notifications
)
```
---
For more information on the Model Context Protocol, visit the [official documentation](https://modelcontextprotocol.io).
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