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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
--------------------------------------------------------------------------------
/assistants/prospector-assistant/README.md:
--------------------------------------------------------------------------------
```markdown
# Using Semantic Workbench with python assistants
This project provides an assistant to help mine artifacts for ideas, 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)
or [OpenAI Content Moderation](https://platform.openai.com/docs/guides/moderation).
See the [Responsible AI FAQ](../../RESPONSIBLE_AI_FAQ.md) for more information.
# 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)
- If using Azure OpenAI, set up an Azure account and create a Content Safety resource
- See [Azure AI Content Safety](https://azure.microsoft.com/products/ai-services/ai-content-safety) for more information
- Copy the `.env.example` to `.env` and update the `ASSISTANT__AZURE_CONTENT_SAFETY_ENDPOINT` value with the endpoint of your Azure Content Safety resource
- From VS Code > `Terminal`, run `az login` to authenticate with Azure prior to starting the assistant
## 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**.
```
--------------------------------------------------------------------------------
/examples/python/python-02-simple-chatbot/README.md:
--------------------------------------------------------------------------------
```markdown
# Using Semantic Workbench with python assistants
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)
or [OpenAI Content Moderation](https://platform.openai.com/docs/guides/moderation).
See the [Responsible AI FAQ](../../../RESPONSIBLE_AI_FAQ.md) for more information.
## 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)
- If using Azure OpenAI, set up an Azure account and create a Content Safety resource
- See [Azure AI Content Safety](https://azure.microsoft.com/products/ai-services/ai-content-safety) for more information
- Copy the `.env.example` to `.env` and update the `ASSISTANT__AZURE_CONTENT_SAFETY_ENDPOINT` value with the endpoint of your Azure Content Safety resource
- From VS Code > `Terminal`, run `az login` to authenticate with Azure prior to starting the assistant
## 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**.
```
--------------------------------------------------------------------------------
/assistants/explorer-assistant/README.md:
--------------------------------------------------------------------------------
```markdown
# Using Semantic Workbench with python assistants
This project provides an assistant to help explore assistant ideas and capabilities, 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)
or [OpenAI Content Moderation](https://platform.openai.com/docs/guides/moderation).
See the [Responsible AI FAQ](../../RESPONSIBLE_AI_FAQ.md) for more information.
# 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)
- If using Azure OpenAI, set up an Azure account and create a Content Safety resource
- See [Azure AI Content Safety](https://azure.microsoft.com/products/ai-services/ai-content-safety) for more information
- Copy the `.env.example` to `.env` and update the `ASSISTANT__AZURE_CONTENT_SAFETY_ENDPOINT` value with the endpoint of your Azure Content Safety resource
- From VS Code > `Terminal`, run `az login` to authenticate with Azure prior to starting the assistant
## 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](./explorer_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**.
```
--------------------------------------------------------------------------------
/libraries/python/skills/skill-library/skill_library/skills/fabric/patterns/summarize_paper/README.md:
--------------------------------------------------------------------------------
```markdown
# Generate summary of an academic paper
This pattern generates a summary of an academic paper based on the provided text. The input should be the complete text of the paper. The output is a summary including the following sections:
**Title and authors of the Paper**
**Main Goal and Fundamental Concept**
**Technical Approach**
**Distinctive Features**
**Experimental Setup and Results**
**Advantages and Limitations**
**Conclusion**
# Example run in MacOS/Linux:
Copy the paper text to the clipboard and execute the following command:
```bash
pbpaste | fabric --pattern summarize_paper
```
or
```bash
pbpaste | summarize_paper
```
# Example output:
```markdown
### Title and authors of the Paper:
**Internet of Paint (IoP): Channel Modeling and Capacity Analysis for Terahertz Electromagnetic Nanonetworks Embedded in Paint**
Authors: Lasantha Thakshila Wedage, Mehmet C. Vuran, Bernard Butler, Yevgeni Koucheryavy, Sasitharan Balasubramaniam
### Main Goal and Fundamental Concept
The primary objective of this research is to introduce and analyze the concept of the Internet of Paint (IoP), a novel idea that integrates nano-network devices within paint to enable communication through painted surfaces using terahertz (THz) frequencies. The core hypothesis is that by embedding nano-scale radios in paint, it's possible to create a new medium for electromagnetic communication, leveraging the unique properties of THz waves for short-range, high-capacity data transmission.
### Technical Approach
The study employs a comprehensive channel model to assess the communication capabilities of nano-devices embedded in paint. This model considers multipath communication strategies, including direct wave propagation, reflections from interfaces (Air-Paint and Paint-Plaster), and lateral wave propagation along these interfaces. The research evaluates the performance across three different paint types, analyzing path losses, received powers, and channel capacities to understand how THz waves interact with painted surfaces.
### Distinctive Features
This research is pioneering in its exploration of paint as a medium for THz communication, marking a significant departure from traditional communication environments. The innovative aspects include:
- The concept of integrating nano-network devices within paint (IoP).
- A detailed channel model that accounts for the unique interaction of THz waves with painted surfaces and interfaces.
- The examination of lateral wave propagation as a key mechanism for communication in this novel medium.
### Experimental Setup and Results
The experimental analysis is based on simulations that explore the impact of frequency, line of sight (LoS) distance, and burial depth of transceivers within the paint on path loss and channel capacity. The study finds that path loss slightly increases with frequency and LoS distance, with higher refractive index paints experiencing higher path losses. Lateral waves show promising performance for communication at increased LoS distances, especially when transceivers are near the Air-Paint interface. The results also indicate a substantial reduction in channel capacity with increased LoS distance and burial depth, highlighting the need for transceivers to be closely positioned and near the Air-Paint interface for effective communication.
### Advantages and Limitations
The proposed IoP approach offers several advantages, including the potential for seamless integration of communication networks into building structures without affecting aesthetics, and the ability to support novel applications like gas sensing and posture recognition. However, the study also identifies limitations, such as the reduced channel capacity compared to air-based communication channels and the challenges associated with controlling the placement and orientation of nano-devices within the paint.
### Conclusion
The Internet of Paint represents a groundbreaking step towards integrating communication capabilities directly into building materials, opening up new possibilities for smart environments. Despite its limitations, such as lower channel capacity compared to traditional air-based channels, IoP offers a unique blend of aesthetics, functionality, and innovation in communication technology. This study lays the foundation for further exploration and development in this emerging field.
```
## Meta
- **Author**: Song Luo (https://www.linkedin.com/in/song-luo-bb17315/)
- **Published**: May 11, 2024
```
--------------------------------------------------------------------------------
/examples/python/python-03-multimodel-chatbot/README.md:
--------------------------------------------------------------------------------
```markdown
# Using Semantic Workbench with different AI models
This project provides a functional chatbot example that can be configured to use models from OpenAI, Anthropic, or Google Gemini that can be used in the **Semantic Workbench**. This example provides a general message model that adapts to the configured model at runtime. Each model is called using their own native Python SDK.
## 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)
or [OpenAI Content Moderation](https://platform.openai.com/docs/guides/moderation).
See the [Responsible AI FAQ](../../../RESPONSIBLE_AI_FAQ.md) for more information.
## 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)
- If using Azure OpenAI, set up an Azure account and create a Content Safety resource
- See [Azure AI Content Safety](https://azure.microsoft.com/products/ai-services/ai-content-safety) for more information
- Copy the `.env.example` to `.env` and update the `ASSISTANT__AZURE_CONTENT_SAFETY_ENDPOINT` value with the endpoint of your Azure Content Safety resource
- From VS Code > `Terminal`, run `az login` to authenticate with Azure prior to starting the assistant
## 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**.
```
--------------------------------------------------------------------------------
/assistants/guided-conversation-assistant/README.md:
--------------------------------------------------------------------------------
```markdown
# Using Semantic Workbench with python assistants
This project provides an assistant to demonstrate how to guide a user towards a goal, leveraging the [guided-conversation library](../../libraries/python/guided-conversation/), which 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.
## 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)
or [OpenAI Content Moderation](https://platform.openai.com/docs/guides/moderation).
See the [Responsible AI FAQ](../../RESPONSIBLE_AI_FAQ.md) for more information.
# 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)
- If using Azure OpenAI, set up an Azure account and create a Content Safety resource
- See [Azure AI Content Safety](https://azure.microsoft.com/products/ai-services/ai-content-safety) for more information
- Copy the `.env.example` to `.env` and update the `ASSISTANT__AZURE_CONTENT_SAFETY_ENDPOINT` value with the endpoint of your Azure Content Safety resource
- From VS Code > `Terminal`, run `az login` to authenticate with Azure prior to starting the assistant
## 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**.
```
--------------------------------------------------------------------------------
/libraries/python/skills/skill-library/skill_library/skills/fabric/patterns/humanize/README.md:
--------------------------------------------------------------------------------
```markdown
# Humanize: Turn stiff AI text 🤖 into human-sounding gold 🪙
**Humanize** aims to help make AI writing sound more like a real person wrote it. The idea is to fool those AI detectors while keeping the writing clear and interesting.
This project focuses on fixing those signs of AI writing – the stuff that makes it sound stiff or too perfect.
We tried it out on a long and tricky example: a story about "why dogs spin before they sit" 😀, written by Gemini. Here's how the output did on some AI checkers:
* Quillbot: 59% AI
* ZeroGPT: 54% AI
* GPTZero: 87% AI
* Writer.com: 15% AI
Other example give 0% score, so it reall depends on the input text, which AI and which scanner you use.
Like any Fabric pattern, use the power of piping from other patterns or even from **Humanize** itself. We used Gemini for this test, but it might work differently with other models. So play around and see what you find... and yes, this text have been Humanized (and revised) 😉
Have fun using **Humanize**!
## Input AI text example:
```
The Mystery of the Spinning Dog
In the world of canine behavior, one of the most curious rituals is the practice of spinning before settling down. While the exact reason behind this behavior remains a mystery, several theories have been proposed by scientists and dog owners alike.
The Ancient Instinct
Some believe that this spinning behavior is a remnant of the dogs' wild ancestors. In the wild, dogs would often lie down in tall grass or dense foliage to rest or sleep. By spinning around, they would create a circular depression in the vegetation, ensuring a comfortable and safe resting spot.
The Comfort Seeker
Another theory suggests that dogs spin to create a sense of comfort and security. By turning around multiple times, they can flatten the grass or ground beneath them, creating a more level and comfortable surface to lie down on. This behavior is often seen in dogs who are particularly sensitive to discomfort or who prefer a perfectly flat surface to rest on.
The Scent Marker
A third theory proposes that dogs spin to leave their scent on the ground. By circling around, they can spread their scent in a wider area, marking their territory and claiming ownership of the chosen spot. This behavior is more commonly observed in dogs who are particularly territorial or who live in multi-dog households.
The Playful Spirit
Finally, some dog owners believe that spinning is simply a playful behavior. Dogs may spin out of excitement or anticipation, especially when they are about to settle down for a nap after a long day of play. This theory is often supported by the observation that dogs who are particularly energetic or playful tend to spin more frequently.
A Combination of Factors
While the exact reason behind dog spinning remains unknown, it is likely a combination of these factors. Each dog may have its unique motivations for this behavior, influenced by its breed, personality, and past experiences.
Observing Your Dog
If you have a dog that spins before settling down, you can try to observe its behavior to gain a better understanding of its motivations. Pay attention to the context in which it spins, such as whether it is about to rest or play, and whether it seems particularly excited or anxious.
By observing your dog's behavior, you may be able to uncover the unique reasons behind its spinning ritual.
```
## Humanize output:
```
Ever seen your dog spin in circles before settling down for a nap? It's a pretty common thing, but why do they do it? Nobody's entirely sure, but there are a few good guesses.
One idea is that it's an instinct from their wolf ancestors. Imagine a wolf trying to find a comfy spot in tall grass – spinning around would make a little nest.
Another thought is that it's all about comfort. Spinning flattens things out, making a nice, even spot for a nap. You see this a lot in dogs who are picky about where they sleep.
Some people think it's about marking their territory. Spinning spreads their scent, kind of like saying, "This is mine!" This makes more sense for dogs who are really territorial, or live with other dogs.
And then there's the "it's just fun" theory. Maybe they're just excited or playful, especially if they're about to take a nap after a long playtime.
It's probably a mix of all these things. Every dog is different, so their reasons might be a little different too. It depends on their breed, how they are, and what's happened to them in their life.
If your dog does this, try watching them. What are they doing right before they start spinning? Are they tired, or excited? Paying attention might give you a clue about why *your* dog does it. It's a fun little mystery to solve together!
```
```
--------------------------------------------------------------------------------
/libraries/python/skills/skill-library/skill_library/skills/fabric/patterns/explain_math/README.md:
--------------------------------------------------------------------------------
```markdown
# explain_math
explain_math is a prompt designed to help you understand mathematical concepts in a clear and engaging way.
## Example:
This example was run on llama3:latest (8B), even with the 8B limitation, the example is still clear and engaging.
<details open>
<summary>Raw</summary>
```markdown
**Pythagorean Theorem: Understanding the Relationship Between Triangles and Squares**
The Pythagorean theorem, named after ancient Greek philosopher and mathematician Pythagoras, is a fundamental concept in geometry that helps us calculate the length of the hypotenuse (the longest side) of a right-angled triangle. This theorem has far-reaching applications in various fields, including mathematics, physics, engineering, and architecture.
**The Formula:**
a^2 + b^2 = c^2
where:
* `c` is the length of the hypotenuse (the longest side)
* `a` and `b` are the lengths of the other two sides (the legs)
**Step-by-Step Explanation:**
Let's consider a right-angled triangle with one angle that is 90 degrees (a square corner). We can use this theorem to find the length of the hypotenuse (`c`) when we know the lengths of the other two sides (`a` and `b`).
1. Square both legs (`a` and `b`) by multiplying them by themselves.
2. Add the squared values of the legs together: a^2 + b^2
3. The result is equal to the square of the hypotenuse (`c`): c^2
**Example:**
Suppose we have a right-angled triangle with one leg that is 3 inches long and the other leg is 4 inches long. We want to find the length of the hypotenuse.
1. Square both legs: 3^2 = 9, 4^2 = 16
2. Add the squared values together: 9 + 16 = 25
3. The result is equal to the square of the hypotenuse: c^2 = 25
Now, take the square root of both sides:
c = √25 ≈ 5 inches
**Real-World Applications:**
The Pythagorean theorem has numerous practical applications in various fields, including:
* Construction and architecture: Finding the length of diagonals, beams, or pillars.
* Physics: Calculating distances, velocities, and accelerations in problems involving right triangles.
* Engineering: Designing bridges, buildings, or other structures that rely on triangular shapes.
**Visual Aids:**
For a better understanding, you can use online tools or software to create interactive diagrams that demonstrate the Pythagorean theorem. Some popular options include:
* GeoGebra
* Desmos
* Khan Academy's math exercises
Remember, this theorem is not just a mathematical concept; it has many practical applications in various fields.
```
</details>
<details>
<summary>Rendered</summary>
**Pythagorean Theorem: Understanding the Relationship Between Triangles and Squares**
The Pythagorean theorem, named after ancient Greek philosopher and mathematician Pythagoras, is a fundamental concept in geometry that helps us calculate the length of the hypotenuse (the longest side) of a right-angled triangle. This theorem has far-reaching applications in various fields, including mathematics, physics, engineering, and architecture.
**The Formula:**
a^2 + b^2 = c^2
where:
* `c` is the length of the hypotenuse (the longest side)
* `a` and `b` are the lengths of the other two sides (the legs)
**Step-by-Step Explanation:**
Let's consider a right-angled triangle with one angle that is 90 degrees (a square corner). We can use this theorem to find the length of the hypotenuse (`c`) when we know the lengths of the other two sides (`a` and `b`).
1. Square both legs (`a` and `b`) by multiplying them by themselves.
2. Add the squared values of the legs together: a^2 + b^2
3. The result is equal to the square of the hypotenuse (`c`): c^2
**Example:**
Suppose we have a right-angled triangle with one leg that is 3 inches long and the other leg is 4 inches long. We want to find the length of the hypotenuse.
1. Square both legs: 3^2 = 9, 4^2 = 16
2. Add the squared values together: 9 + 16 = 25
3. The result is equal to the square of the hypotenuse: c^2 = 25
Now, take the square root of both sides:
c = √25 ≈ 5 inches
**Real-World Applications:**
The Pythagorean theorem has numerous practical applications in various fields, including:
* Construction and architecture: Finding the length of diagonals, beams, or pillars.
* Physics: Calculating distances, velocities, and accelerations in problems involving right triangles.
* Engineering: Designing bridges, buildings, or other structures that rely on triangular shapes.
**Visual Aids:**
For a better understanding, you can use online tools or software to create interactive diagrams that demonstrate the Pythagorean theorem. Some popular options include:
* GeoGebra
* Desmos
* Khan Academy's math exercises
Remember, this theorem is not just a mathematical concept; it has many practical applications in various fields.
</details>
```
--------------------------------------------------------------------------------
/libraries/python/skills/skill-library/skill_library/skills/research/README.md:
--------------------------------------------------------------------------------
```markdown
# Research skill
## Web Research [web_researcy.py](./routines/web_research.py)
This routine conducts thorough research on a given topic, interacts with the
user to refine the research plan, gathers information from web searches,
evaluates answers, and generates a structured research report. Here's how it
works:
1. **Generate a Research Plan:**
- The program starts by running a routine (`research.generate_research_plan`)
to create a research plan based on the given topic.
- The plan is then saved to a file (`plan_name.txt`).
2. **User Review & Refinement:**
- The user is asked if the plan looks good or needs modifications.
- If the user wants updates, the plan is read from the file and refined
(`research.update_research_plan`).
- This loop continues until the user confirms the plan or chooses to exit.
3. **Execute the Research Plan:**
- If the user exits, the temporary research plan file is deleted.
- Otherwise, the research begins, and a results file
(`plan_name_research_answers.md`) is created with a header.
4. **Web Search & Answer Collection:**
- For each research question in the plan:
- The system searches the web for relevant information
(`research.web_search`).
- It generates an answer using the retrieved content
(`research.answer_question_about_content`).
- The answer is evaluated for accuracy (`research.evaluate_answer`).
- If the answer is good, it is appended to the results file.
- If the answer is not satisfactory, the system refines its search based on
previous attempts.
5. **Summarize & Report Generation:**
- After answering all research questions, the collected information is read
from the file.
- A summary of the research is generated (`common.summarize`).
- The final research report is saved as `plan_name_research_report.txt`.
- The program prints a completion message and returns the report.
### Key Features
- **Iterative refinement** with user feedback before executing research.
- **Automated web searching** to gather relevant information.
- **Evaluation of answers** to ensure quality before adding them to the report.
- **Final summarization** for a concise, structured research report.
This routine is designed to **automate and streamline** the process of
conducting detailed web-based research, reducing manual effort while maintaining
quality through user interaction and validation steps.
## Future improvements
### User interaction
"one thing that I really like about the OpenAI deep research is that it always comes back with exactly 1 round of clarifying questions before running, which I believe significantly improves the result for the desired context" -- brkrabach
Or when running in an MCP, maybe make the tool description:
"If you feel it is appropriate to use this tool, express this intent and then make exactly one request of the user for specific details that may make this research request more on target with their ask. Include 4-6 clarifying questions. If you have already done this step, please make sure to include the user's answers in your request for this tool"
### Routine improvements
"Based on my analysis of the current Web Research routine and the feedback received, here are several structural changes I would recommend:
Recommended Structural Changes to the Research Routine" -- Claude 3.7 Sonnet
Add a Source Evaluation Step
Create a new routine (research2.evaluate_sources) that runs after visiting pages
This would assess the reliability and relevance of each source, flagging potential biases or SEO-optimized content
Could assign confidence ratings to different sources
Implement Iterative Fact Verification
Add a dedicated verification step (research2.verify_facts) that cross-checks facts across multiple sources
This would help identify and flag contradictions or unverified claims
Could implement a consensus mechanism for conflicting information
Create a Progressive Summary
Instead of waiting until the end, maintain a running summary (research2.progressive_summary)
Update this summary after each research iteration
This would help identify gaps more clearly as the research progresses
Add Information Classification
Create a routine to classify information by type (e.g., research2.classify_info)
Categories could include: technical specs, expert opinions, user experiences, statistical data
This would help organize the final report and highlight the diversity of information types
Implement a Counterpoint Search
Add a dedicated step (research2.find_counterpoints) to search specifically for contradictory information
This would deliberately look for opposing viewpoints or contradictions to current findings
Could help address confirmation bias in the research process
Create a Confidence Scoring System
Implement a numerical confidence score for each fact (research2.score_confidence)
Based on source reliability, number of corroborating sources, and consistency
Would make uncertainty more transparent in the final report
Add Early Termination Criteria
Implement more sophisticated criteria for when research is complete
Consider both coverage of topics and confidence levels in determining completion
Could prevent unnecessary searches when sufficient reliable information has been found
```
--------------------------------------------------------------------------------
/workbench-app/README.md:
--------------------------------------------------------------------------------
```markdown
# Semantic Workbench App
## Architecture
The Semantic Workbench app is designed as a client-side, single-page application (SPA) using React and TypeScript. It runs entirely in the user's browser and interacts with the backend Semantic Workbench service for state and conversation management.
### Key Components
**React/TypeScript**: The app is built using the React library and TypeScript for static typing.
**Vite**: Build system and development server that provides fast HMR (Hot Module Replacement).
**Fluent UI & Fluent Copilot**: Microsoft design system components that provide a consistent look and feel.
**Redux & Redux Toolkit**: Centralized state management with middleware for side effects.
**React Router**: Handles navigation and URL management within the SPA.
**Client-Server Interaction**: The app communicates with the Workbench backend service via RESTful APIs and SSE (Server-Sent Events) for real-time updates.
**Content Rendering**: Support for rich content types including Markdown, code syntax highlighting, Mermaid diagrams, ABC notation for music, and more.
### Initialization and Authentication
The application requires user authentication, typically via Microsoft AAD or MSA accounts. The app uses Microsoft Authentication Library (MSAL) for authentication flows. Instructions for setting up the development environment are in the repo readme. To deploy in your own environment see [app registration documentation](../docs/CUSTOM_APP_REGISTRATION.md).
### Connecting to the Backend Service
The Semantic Workbench app connects to the backend service using RESTful API calls. Here's how the interaction works:
1. **Initial Setup**: On application startup, the app establishes a connection to the backend service located at a specified endpoint. This connection requires SSL certificates, which are automatically generated by vite-plugin-mkcert during development.
2. **User Authentication**: Users must authenticate via Microsoft AAD or MSA accounts. This enables secure access and interaction between the app and the backend.
3. **Data Fetching & State Management**: The app uses Redux Toolkit Query to manage API requests and caching of conversation data, messages, and participant information.
4. **Event Handling**: User actions within the app (e.g., sending a message) trigger RESTful API calls to the backend, which processes the actions and returns the appropriate responses.
5. **Real-time Updates**: The app uses Server-Sent Events (SSE) to receive real-time updates from the backend service, enabling live updates of conversation state.
### Error Handling
The app includes error handling mechanisms that notify users of any issues with the backend connection, such as authentication failures or network issues.
## Setup Guide
The Semantic Workbench app is a React/TypeScript app integrated with the [Semantic Workbench](../workbench-service/README.md) backend service.
### Prerequisites
- **Node.js**: Version 20.x is required (enforced by the run script)
- **pnpm**: Used for package management
- **SSL certificates**: Automatically generated during development for HTTPS
Follow the [setup guide](../docs/SETUP_DEV_ENVIRONMENT.md) to install all required development tools.
## Installing dependencies
In the [workbench-app](./) directory:
```sh
make
```
This command runs `pnpm install` to install all required dependencies.
## Running from VS Code
To run and/or debug in VS Code:
1. Open the workspace file `semantic-workbench.code-workspace`
2. View -> Run
3. Select "app: semantic-workbench-app"
## Running from the command line
In the [workbench-app](./) directory:
```sh
pnpm dev
```
or
```sh
pnpm start
```
Then open https://127.0.0.1:4000 in your browser.
Note: The first time you run the app, your browser may warn about the self-signed SSL certificate.
## Development Tools
### Available Scripts
- `pnpm dev` / `pnpm start` - Start development server
- `pnpm build` - Build for production
- `pnpm preview` - Locally preview production build
- `pnpm lint` - Run ESLint to check code quality
- `pnpm format` / `pnpm prettify` - Format code with Prettier
- `pnpm type-check` - Run TypeScript type checking
- `pnpm find-deadcode` - Identify unused code
- `pnpm depcheck` - Check for dependency issues
## Documentation
### Application Documentation
- [App Development Guide](./docs/APP_DEV_GUIDE.md) - Guide for developing the app
- [Message Metadata](./docs/MESSAGE_METADATA.md) - Details about message metadata structure
- [Message Types](./docs/MESSAGE_TYPES.md) - Different types of messages supported
- [State Inspectors](./docs/STATE_INSPECTORS.md) - Information about state inspection tools
### Related Documentation
- [Workbench App Overview](../docs/WORKBENCH_APP.md) - Complete overview of the application
- [Custom App Registration](../docs/CUSTOM_APP_REGISTRATION.md) - Setting up authentication
## Project Structure
Key directories in the project:
```
workbench-app/
├── src/
│ ├── components/ # Reusable UI components
│ ├── models/ # TypeScript interfaces and types
│ ├── redux/ # State management
│ ├── routes/ # Application routes
│ ├── services/ # API services
│ └── libs/ # Utility functions and hooks
├── public/ # Static assets
├── docs/ # Documentation
└── certs/ # SSL certificates for development
```
```
--------------------------------------------------------------------------------
/workbench-service/README.md:
--------------------------------------------------------------------------------
```markdown
# Semantic Workbench Service
## Architecture
The Semantic Workbench service consists of several key components that interact to provide a seamless user experience:
**Workbench Service**: A backend Python service that handles state management, user interactions, conversation history, file storage, and real-time event distribution.
[**Workbench App**](../workbench-app): A single-page web application written in TypeScript and React that provides the user interface for interacting with assistants.
**FastAPI Framework**: Powers the HTTP API and Server-Sent Events (SSE) for real-time updates between clients and assistants.
**Database Layer**: Uses SQLModel (SQLAlchemy) with support for both SQLite (development) and PostgreSQL (production) for persistent storage.
**Authentication**: Integrated Azure AD/Microsoft authentication with JWT token validation.
**Assistants**: Independently developed services that connect to the Workbench through a RESTful API, enabling AI capabilities.
### Core Components
- **Controller Layer**: Implements business logic for conversations, assistants, files, and users
- **Database Models**: SQLModel-based entities for storing application state
- **Authentication**: Azure AD integration with JWT validation
- **File Storage**: Versioned file system for conversation attachments and artifacts
- **Event System**: Real-time event distribution using Server-Sent Events (SSE)
- **API Layer**: RESTful endpoints for all service operations
### Communication
The communication between the Workbench and Assistants is managed through multiple channels:
1. **HTTP API**: RESTful endpoints for CRUD operations and state management
2. **Server-Sent Events (SSE)**: Real-time event streaming for immediate updates
3. **Event System**: Structured event types (e.g., `message.created`, `conversation.updated`) for real-time state synchronization
4. **Webhook Callbacks**: Assistant registration with callback URLs for event delivery
### Database Structure
The service uses SQLModel to manage structured data:
- **Users**: Authentication and profile information
- **Conversations**: Messaging history and metadata
- **Messages**: Different message types with content and metadata
- **Participants**: Users and assistants in conversations
- **Files**: Versioned attachments for conversations
- **Assistants**: Registered assistants and their configurations
- **Shares**: Conversation sharing capabilities
## Features
### Conversation Management
- Create, update, and delete conversations
- Add and remove participants
- Different message types (chat, note, notice, command)
- Message metadata and debug information
### File Management
- File attachment support
- Versioned file storage
- Multiple content types
### Sharing
- Share conversations with other users
- Public/private share links
- Share redemption
### Integration with Assistants
- Assistant registration and discovery
- API key management for secure communication
- Event-based communication
### Speech Services
- Azure Speech integration for text-to-speech
## Configuration
The service is configured through environment variables:
```
# Basic configuration
WORKBENCH_SERVICE_HOST=127.0.0.1
WORKBENCH_SERVICE_PORT=5000
# Database settings
WORKBENCH_SERVICE_DB_CONNECTION=sqlite+aiosqlite:///./workbench-service.db
# Or for PostgreSQL:
# WORKBENCH_SERVICE_DB_CONNECTION=postgresql+asyncpg://user:pass@host:port/dbname
# Authentication
WORKBENCH_SERVICE_TENANT_ID=your-azure-tenant-id
WORKBENCH_SERVICE_CLIENT_ID=your-client-id
# File storage
WORKBENCH_SERVICE_FILES_DIR=./.data/files
```
See the [environment setup guide](../docs/SETUP_DEV_ENVIRONMENT.md) for complete configuration options.
## Setup Guide
### Prerequisites
- Python 3.11+
- Access to database (SQLite for development, PostgreSQL for production)
- Azure AD application registration (for authentication)
### Installing Dependencies
In the [workbench-service](./) directory:
```sh
make
```
This will use [uv](https://github.com/astral-sh/uv) to install all Python dependencies.
If this fails in Windows, try running a vanilla instance of `cmd` or `powershell` and not within `Cmder` or another shell that may have modified the environment.
### Database Migration
The service uses Alembic for database migrations:
```sh
# Initialize the database
uv run alembic upgrade head
```
### Running from VS Code
To run and/or debug in VS Code:
1. Open the workspace file `semantic-workbench.code-workspace`
2. View->Run
3. Select "service: semantic-workbench-service"
### Running from the Command Line
In the [workbench-service](./) directory:
```sh
uv run start-service [--host HOST] [--port PORT]
```
### Running Tests
```sh
uv run pytest
```
## API Documentation
When running the service, access the FastAPI auto-generated documentation at:
- Swagger UI: `http://localhost:5000/docs`
- ReDoc: `http://localhost:5000/redoc`
## Troubleshooting
### Common Issues
- **Database connection errors**: Verify your connection string and database permissions
- **Authentication failures**: Check your Azure AD configuration and client IDs
- **File storage permissions**: Ensure the service has write access to the files directory
### Debug Mode
Enable debug logging for more detailed information:
```sh
WORKBENCH_SERVICE_LOG_LEVEL=DEBUG uv run start-service
```
```
--------------------------------------------------------------------------------
/libraries/python/assistant-extensions/README.md:
--------------------------------------------------------------------------------
```markdown
# Assistant Extensions
Extensions that enhance Semantic Workbench assistants with additional capabilities beyond the core functionality.
## Overview
The `assistant-extensions` library provides several modules that can be integrated with your Semantic Workbench assistants:
- **Artifacts**: Create and manage file artifacts during conversations (markdown, code, mermaid diagrams, etc.)
- **Attachments**: Process and extract content from file attachments added to conversations
- **AI Clients**: Configure and manage different AI service providers (OpenAI, Azure OpenAI, Anthropic)
- **MCP (Model Context Protocol)**: Connect to and utilize MCP tool servers for extended functionality
- **Workflows**: Define and execute multi-step automated workflows
These extensions are designed to work with the `semantic-workbench-assistant` framework and can be added to your assistant implementation to enhance its capabilities.
## Module Details
### Artifacts
The Artifacts extension enables assistants to create and manage rich content artifacts within conversations.
```python
from assistant_extensions.artifacts import ArtifactsExtension, ArtifactsConfigModel
from semantic_workbench_assistant import AssistantApp
async def get_artifacts_config(context):
return ArtifactsConfigModel(enabled=True)
# Create and add the extension to your assistant
assistant = AssistantApp(name="My Assistant")
artifacts_extension = ArtifactsExtension(
assistant=assistant,
config_provider=get_artifacts_config
)
# The extension is now ready to create and manage artifacts
```
Supports content types including Markdown, code (with syntax highlighting), Mermaid diagrams, ABC notation for music, and more.
### Attachments
Process files uploaded during conversations, extracting and providing content to the AI model.
```python
from assistant_extensions.attachments import AttachmentsExtension, AttachmentsConfigModel
from semantic_workbench_assistant import AssistantApp
assistant = AssistantApp(name="My Assistant")
attachments_extension = AttachmentsExtension(assistant=assistant)
@assistant.events.conversation.message.chat.on_created
async def handle_message(context, event, message):
config = AttachmentsConfigModel(
context_description="Files attached to this conversation"
)
# Get attachment content to include in AI prompt
messages = await attachments_extension.get_completion_messages_for_attachments(context, config)
# Use messages in your AI completion request
```
Supports text files, PDFs, Word documents, and images with OCR capabilities.
### AI Clients
Configuration models for different AI service providers to simplify client setup.
```python
from assistant_extensions.ai_clients.config import OpenAIClientConfigModel, AIServiceType
from openai_client import OpenAIServiceConfig, OpenAIRequestConfig
# Configure an OpenAI client
config = OpenAIClientConfigModel(
ai_service_type=AIServiceType.OpenAI,
service_config=OpenAIServiceConfig(
api_key=os.environ.get("OPENAI_API_KEY")
),
request_config=OpenAIRequestConfig(
model="gpt-4o",
temperature=0.7
)
)
# Use this config with openai_client or anthropic_client libraries
```
### MCP (Model Context Protocol)
Connect to and utilize MCP tool servers to extend your assistant with external capabilities.
```python
from assistant_extensions.mcp import establish_mcp_sessions, retrieve_mcp_tools_from_sessions
from contextlib import AsyncExitStack
async def setup_mcp_tools(config):
async with AsyncExitStack() as stack:
# Connect to MCP servers and get available tools
sessions = await establish_mcp_sessions(config, stack)
tools = retrieve_mcp_tools_from_sessions(sessions, config)
# Use tools with your AI model
return sessions, tools
```
### Workflows
Define and execute multi-step workflows within conversations, such as automated sequences.
```python
from assistant_extensions.workflows import WorkflowsExtension, WorkflowsConfigModel
from semantic_workbench_assistant import AssistantApp
async def get_workflows_config(context):
return WorkflowsConfigModel(
enabled=True,
workflow_definitions=[
{
"workflow_type": "user_proxy",
"command": "analyze_document",
"name": "Document Analysis",
"description": "Analyze a document for quality and completeness",
"user_messages": [
{"message": "Please analyze this document for accuracy"},
{"message": "What improvements would you suggest?"}
]
}
]
)
assistant = AssistantApp(name="My Assistant")
workflows_extension = WorkflowsExtension(
assistant=assistant,
config_provider=get_workflows_config
)
```
## Integration
These extensions are designed to enhance Semantic Workbench assistants. To use them:
1. Configure your assistant using the `semantic-workbench-assistant` framework
2. Add the desired extensions to your assistant
3. Implement event handlers for extension functionality
4. Configure extension behavior through their respective config models
For detailed examples, see the [Assistant Development Guide](../../docs/ASSISTANT_DEVELOPMENT_GUIDE.md) and explore the existing assistant implementations in the repository.
## Optional Dependencies
Some extensions require additional packages:
```
# For attachments support (PDF, Word docs)
pip install "assistant-extensions[attachments]"
# For MCP tool support
pip install "assistant-extensions[mcp]"
```
This library is part of the Semantic Workbench project, which provides a complete framework for building and deploying intelligent assistants.
```
--------------------------------------------------------------------------------
/assistants/skill-assistant/README.md:
--------------------------------------------------------------------------------
```markdown
# Skill Assistant
The Skill Assistant serves as a demonstration of integrating the Skill Library within an Assistant in the Semantic Workbench. Specifically, this assistant showcases the Posix skill and the chat driver. The [Posix skill](../../libraries/python/skills/skills/posix-skill/README.md) demonstrates file system management by allowing the assistant to perform posix-style actions. The [chat driver](../../libraries/python/openai-client/openai_client/chat_driver/README.md) handles conversations and interacts with underlying AI models like OpenAI and Azure OpenAI.
## Overview
[skill_controller.py](assistant/skill_controller.py) file is responsible for managing the assistants. It includes functionality to create and retrieve assistants, configure chat drivers, and map skill events to the Semantic Workbench.
- AssistantRegistry: Manages multiple assistants, each associated with a unique conversation.
- \_event_mapper: Maps skill events to message types understood by the Semantic Workbench.
- create_assistant: Defines how to create and configure a new assistant.
[skill_assistant.py](assistant/skill_assistant.py) file defines the main Skill Assistant class that integrates with the Semantic Workbench. It handles workbench events and coordinates the assistant's responses based on the conversation state.
- SkillAssistant Class: The main class that integrates with the Semantic Workbench.
- on_workbench_event: Handles various workbench events to drive the assistant's behavior.
[config.py](assistant/config.py) file defines the configuration model for the Skill Assistant. It includes settings for both Azure OpenAI and OpenAI services, along with request-specific settings such as max_tokens and response_tokens.
- RequestConfig: Defines parameters for generating responses, including tokens settings.
## Responsible AI
The assistant 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)
or [OpenAI Content Moderation](https://platform.openai.com/docs/guides/moderation).
See the [Responsible AI FAQ](../../RESPONSIBLE_AI_FAQ.md) for more information.
# 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)
- If using Azure OpenAI, set up an Azure account and create a Content Safety resource
- See [Azure AI Content Safety](https://azure.microsoft.com/products/ai-services/ai-content-safety) for more information
- Copy the `.env.example` to `.env` and update the `ASSISTANT__AZURE_CONTENT_SAFETY_ENDPOINT` value with the endpoint of your Azure Content Safety resource
- From VS Code > `Terminal`, run `az login` to authenticate with Azure prior to starting the assistant
## 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/skill_assistant.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**.
```
--------------------------------------------------------------------------------
/assistants/document-assistant/README.md:
--------------------------------------------------------------------------------
```markdown
# Document Assistant
NOTE: DO NOT DEPLOY THIS ASSISTANT OUTSIDE OF CODESPACES (OR LOCAL, BUT THIS HAS NOT BEEN TESTED)
This assistant is designed to help with development within the Semantic Workbench repo in Codespaces, and is not intended for deployment to production environments.
The Document Assistant is an AI assistant focused on being easy to use for everyone with a core feature being
reliable document creation and editing, grounded in all of your context across files and the conversation.
---
## Key Features
- **MCP Server Integration**:
- Provides access to tools like the MCP filesystem and VS Code servers by default.
- Additional tools (e.g., Bing Search, OpenAI integrations) can be enabled via configuration.
- **Content Safety and Guardrails**:
- Integrated with Azure OpenAI and OpenAI APIs for responsible AI usage.
- Includes prompts for instruction, guidance, and guardrails.
- **Codespaces Optimization**:
- Ready to run directly within Codespaces for a streamlined developer experience.
- Also supports local setups, but **Windows users must use WSL** due to Linux dependencies.
---
## Prerequisites
### Codespaces Development
- Follow the guide in [Optimizing for Codespaces](../../.devcontainer/OPTIMIZING_FOR_CODESPACES.md) to set up your environment.
- **Using VS Code Desktop**:
- Open the workspace: `/workspaces/semanticworkbench/semantic-workbench.code-workspace`.
### Local Development
- Refer to [Setup Developer Environment](../../docs/SETUP_DEV_ENVIRONMENT.md) for full instructions.
- **Windows Users**:
- Must host the repository in **WSL (Windows Subsystem for Linux)** due to Linux library dependencies.
### Authentication
- You must authenticate with the Semantic Workbench using a **Microsoft or organizational account**. See [Workbench App Overview](../../docs/WORKBENCH_APP.md) for details.
---
## Setup Instructions
### Creating a Codespace
1. Go to the **Semantic Workbench** repository in GitHub.
2. Create a new Codespace.
3. Open the Codespace in **VS Code Desktop**.
- Open the workspace file: `/workspaces/semanticworkbench/semantic-workbench.code-workspace`.
### Configure `.env` Variables
1. Navigate to the folder: `/assistants/codespace-assistant`.
2. Copy `.env.example` to `.env`.
3. Replace default values with your resource details for **Azure OpenAI** and **OpenAI** APIs.
- **Azure**:
- `ASSISTANT__AZURE_OPENAI_ENDPOINT`: Azure OpenAI endpoint.
- `ASSISTANT__AZURE_OPENAI_API_KEY`: Azure API key (use managed identities if possible).
- `ASSISTANT__AZURE_CONTENT_SAFETY_ENDPOINT`: Azure Content Safety endpoint.
- **OpenAI**:
- `ASSISTANT__OPENAI_API_KEY`: API key for OpenAI.
- `ASSISTANT__OPENAI_ORGANIZATION_ID`: Organization ID (optional).
### First Launch
1. Go to the **Debug** pane in VS Code.
2. Select `assistants: codespace-assistant (demo)`.
3. Start the assistant.
- For more MCP servers, select `assistants: codespace-assistant (for dev)` (requires custom API keys).
4. Open your browser: [https://127.0.0.1:4000/](https://127.0.0.1:4000/).
- Click "Advanced" > "Proceed to localhost" to bypass security warnings.
5. Create a conversation and add the assistant:
- Provide a title.
- Create a new assistant and select the Codespace Assistant service.
- Start interacting with the assistant (e.g., ask questions about the repo).
---
## Extending Functionality
### Add Your Own Code
1. Open a terminal in VS Code.
2. Navigate to the `/workspaces` directory (default MCP filesystem server location).
3. Clone your repository or create a new folder.
- Optionally, add it to the workspace using **File > Add Folder to Workspace**.
The assistant can now read, write, and edit your custom code.
---
## Additional MCP Servers (Advanced)
The `assistants: codespace-assistant (for dev)` debug configuration enables additional MCP servers not active by default. These servers can extend the assistant's functionality, but they require custom API keys to activate.
### Available MCP Servers
1. **Bing Search**:
- **Command**: `http://127.0.0.1:6030/sse`
- **Purpose**: Enables search capabilities via Bing.
2. **Open Deep Research**:
- **Command**: `http://127.0.0.1:6020/sse`
- **Purpose**: Facilitates deeper research workflows.
3. **Giphy**:
- **Command**: `http://127.0.0.1:6000/sse`
- **Purpose**: Fetches GIFs for use in conversations.
4. **Memory**:
- **Command**: `npx @modelcontextprotocol/server-memory`
- **Purpose**: Integrates a memory or knowledge graph system.
5. **Sequential Thinking**:
- **Command**: `npx @modelcontextprotocol/server-sequential-thinking`
- **Purpose**: Enables tools for sequential reasoning tasks.
### How to Enable Additional MCP Servers
1. Use the assistant configuration interface to enable these MCP servers directly. In the Semantic Workbench, navigate to the assistant's configuration panel, locate the MCP server settings, and toggle the desired servers on.
2. Check the `.env.example` file for each server's required API keys and configuration.
3. To enable a server, update the `.env` file with the necessary values and restart the assistant.
---
## Frequently Asked Questions (FAQs)
### Authentication and Access
- **Q**: How do I log into the Semantic Workbench?
- **A**: Log in using your Microsoft or organizational account. See [Workbench App Overview](../../docs/WORKBENCH_APP.md).
### Common Errors
1. **Azure Content Safety Error**:
- Issue: `Bearer token authentication is not permitted for non-HTTPS URLs.`
- Solution: Configure the endpoint properly.
2. **Blank Screen on Startup**:
- Check if pop-up blockers are preventing access.
3. **Connection Issues on 127.0.0.1**:
- Ensure you're navigating to `https://127.0.0.1:4000/`.
### Enabling MCP Servers
- Navigate to the assistant configuration panel and enable or configure servers as needed.
- By default, the filesystem and VS Code servers are active. Others, like Bing Search or Giphy, can be enabled manually.
### Limits and Customization
1. **Maximum Steps Reached**:
- Expand the assistant's steps by updating the `Maximum Steps` setting in the assistant configuration.
2. **Folder Not Found**:
- Verify the path is under `/workspaces`. Adjust permissions if needed.
---
## Additional Resources
- [Optimizing for Codespaces](../../.devcontainer/OPTIMIZING_FOR_CODESPACES.md)
- [Workbench App Overview](../../docs/WORKBENCH_APP.md)
- [Setup Developer Environment](../../docs/SETUP_DEV_ENVIRONMENT.md)
- [Assistant Development Guide](../../docs/ASSISTANT_DEVELOPMENT_GUIDE.md)
For issues, see the [Semantic Workbench README](../../README.md) or raise a question in the repository.
```