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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
--------------------------------------------------------------------------------
/mcp-servers/mcp-server-vscode/src/tools/code_checker.ts:
--------------------------------------------------------------------------------
```typescript
import { DiagnosticSeverity, languages } from 'vscode';
/**
* Retrieve diagnostics for the active workspace, with filtering by severity level.
*
* @param 'Warning' - Minimum severity level to include (default is Warning).
*/
export const codeCheckerTool = (severityLevel: DiagnosticSeverity = DiagnosticSeverity.Warning) => {
// Retrieve diagnostics from all files
const diagnosticsByFile = languages.getDiagnostics();
// Filter diagnostics based on the target severity
const aggregated = diagnosticsByFile
.filter(([_uri, diags]) => diags.some((diag) => diag.severity <= severityLevel))
.map(([uri, diags]) => ({
file: uri.fsPath,
diagnostics: diags
.filter((diag) => diag.severity <= severityLevel)
.map((diag) => ({
severity: DiagnosticSeverity[diag.severity],
message: diag.message,
source: diag.source || '',
})),
}));
if (aggregated.length === 0) {
// If no diagnostics found, return an empty result
return { content: [{ type: 'text', text: 'No issues found.' }], isError: false };
}
// Otherwise, return the aggregated diagnostics as formatted JSON
return { content: [{ type: 'text', text: JSON.stringify(aggregated, null, 2) }], isError: false };
};
```
--------------------------------------------------------------------------------
/workbench-service/tests/test_files.py:
--------------------------------------------------------------------------------
```python
import io
import uuid
import pytest
from semantic_workbench_service import files
def test_read_file_not_found(storage_settings: files.StorageSettings) -> None:
file_storage = files.Storage(settings=storage_settings)
with (
pytest.raises(FileNotFoundError),
file_storage.read_file(
namespace="conversation_id",
filename="filename",
) as f,
):
f.read()
def test_write_file(storage_settings: files.StorageSettings) -> None:
file_storage = files.Storage(settings=storage_settings)
file_storage.write_file(namespace="conversation_id", filename="filename", content=io.BytesIO(b"content"))
def test_write_read_delete_file(storage_settings: files.StorageSettings) -> None:
file_storage = files.Storage(settings=storage_settings)
conversation_id = uuid.uuid4().hex
filename = "myfile.txt"
file_content = b"""
this is a text file.
"""
file_storage.write_file(namespace=conversation_id, filename=filename, content=io.BytesIO(file_content))
with file_storage.read_file(namespace=conversation_id, filename=filename) as f:
assert f.read() == file_content
file_storage.delete_file(namespace=conversation_id, filename=filename)
with pytest.raises(FileNotFoundError), file_storage.read_file(namespace=conversation_id, filename=filename) as f:
pass
```
--------------------------------------------------------------------------------
/assistants/prospector-assistant/assistant/form_fill_extension/steps/_llm.py:
--------------------------------------------------------------------------------
```python
from typing import Any, TypeVar
from openai.types.chat import ChatCompletionMessageParam
from pydantic import BaseModel
from .types import LLMConfig
class NoResponseChoicesError(Exception):
pass
class NoParsedMessageError(Exception):
pass
ResponseModelT = TypeVar("ResponseModelT", bound=BaseModel)
async def structured_completion(
llm_config: LLMConfig, messages: list[ChatCompletionMessageParam], response_model: type[ResponseModelT]
) -> tuple[ResponseModelT, dict[str, Any]]:
async with llm_config.openai_client_factory() as client:
response = await client.beta.chat.completions.parse(
messages=messages,
model=llm_config.openai_model,
response_format=response_model,
max_tokens=llm_config.max_response_tokens,
)
if not response.choices:
raise NoResponseChoicesError()
if not response.choices[0].message.parsed:
raise NoParsedMessageError()
metadata = {
"request": {
"model": llm_config.openai_model,
"messages": messages,
"max_tokens": llm_config.max_response_tokens,
"response_format": response_model.model_json_schema(),
},
"response": response.model_dump(),
}
return response.choices[0].message.parsed, metadata
```
--------------------------------------------------------------------------------
/libraries/python/skills/skill-library/skill_library/skills/fabric/patterns/extract_primary_problem/system.md:
--------------------------------------------------------------------------------
```markdown
# IDENTITY
You are an expert at looking at a presentation, an essay, or a full body of lifetime work, and clearly and accurately articulating what the author(s) believe is the primary problem with the world.
# GOAL
- Produce a clear sentence that perfectly articulates the primary problem with the world as presented in a given text or body of work.
# EXAMPLE
If the body of work is all of Ted Kazcynski's writings, then the primary problem with the world would be:
Technology is destroying the human spirit and the environment.
END EXAMPLE
# STEPS
- Fully digest the input.
- Determine if the input is a single text or a body of work.
- Based on which it is, parse the thing that's supposed to be parsed.
- Extract the primary problem with the world from the parsed text into a single sentence.
# OUTPUT
- Output a single, 15-word sentence that perfectly articulates the primary problem with the world as presented in the input.
# OUTPUT INSTRUCTIONS
- The sentence should be a single sentence that is 16 words or fewer, with no special formatting or anything else.
- Do not include any setup to the sentence, e.g., "The problem according to…", etc. Just list the problem and nothing else.
- ONLY OUTPUT THE PROBLEM, not a setup to the problem. Or a description of the problem. Just the problem.
- Do not ask questions or complain in any way about the task.
```
--------------------------------------------------------------------------------
/libraries/python/skills/skill-library/skill_library/skills/fabric/patterns/create_ttrc_graph/system.md:
--------------------------------------------------------------------------------
```markdown
# IDENTITY
You are an expert at data visualization and information security. You create a progress over time graph for the Time to Remediate Critical Vulnerabilities metric.
# GOAL
Show how the time to remediate critical vulnerabilities has changed over time.
# STEPS
- Fully parse the input and spend 431 hours thinking about it and its implications to a security program.
- Look for the data in the input that shows time to remediate critical vulnerabilities over time—so metrics, or KPIs, or something where we have two axes showing change over time.
# OUTPUT
- Output a CSV file that has all the necessary data to tell the progress story.
- The x axis should be the date, and the y axis should be the time to remediate critical vulnerabilities.
The format will be like so:
EXAMPLE OUTPUT FORMAT
Date TTR-C_days
Month Year 81
Month Year 80
Month Year 72
Month Year 67
(Continue)
END EXAMPLE FORMAT
- Only output numbers in the fields, no special characters like "<, >, =," etc..
- Do not output any other content other than the CSV data. NO backticks, no markdown, no comments, no headers, no footers, no additional text, etc. Just the CSV data.
- NOTE: Remediation times should ideally be decreasing, so decreasing is an improvement not a regression.
- Only output valid CSV data and nothing else.
- Use the field names in the input; don't make up your own.
```
--------------------------------------------------------------------------------
/assistants/codespace-assistant/assistant/response/utils/formatting_utils.py:
--------------------------------------------------------------------------------
```python
import logging
from textwrap import dedent
logger = logging.getLogger(__name__)
def get_response_duration_message(response_duration: float) -> str:
"""
Generate a display friendly message for the response duration, to be added to the footer items.
"""
return f"Response time: {response_duration:.2f} seconds"
def get_formatted_token_count(tokens: int) -> str:
# if less than 1k, return the number of tokens
# if greater than or equal to 1k, return the number of tokens in k
# use 1 decimal place for k
# drop the decimal place if the number of tokens in k is a whole number
if tokens < 1000:
return str(tokens)
else:
tokens_in_k = tokens / 1000
if tokens_in_k.is_integer():
return f"{int(tokens_in_k)}k"
else:
return f"{tokens_in_k:.1f}k"
def get_token_usage_message(
max_tokens: int,
total_tokens: int,
request_tokens: int,
completion_tokens: int,
) -> str:
"""
Generate a display friendly message for the token usage, to be added to the footer items.
"""
return dedent(f"""
Tokens used: {get_formatted_token_count(total_tokens)}
({get_formatted_token_count(request_tokens)} in / {get_formatted_token_count(completion_tokens)} out)
of {get_formatted_token_count(max_tokens)} ({int(total_tokens / max_tokens * 100)}%)
""").strip()
```
--------------------------------------------------------------------------------
/workbench-app/src/components/Conversations/ConversationFileIcon.tsx:
--------------------------------------------------------------------------------
```typescript
import { Image } from '@fluentui/react-components';
import { FileTypeIconSize, getFileTypeIconAsUrl } from '@fluentui/react-file-type-icons';
import { FileIconTypeInput } from '@fluentui/react-file-type-icons/lib/FileIconType';
import React from 'react';
import { ConversationFile } from '../../models/ConversationFile';
interface ConversationFileIconProps {
file: File | ConversationFile | string;
className?: string;
/**
* The type of file type icon you need. Use this property for
* file type icons that are not associated with a file extension,
* such as folder.
*/
type?: FileIconTypeInput;
size?: FileTypeIconSize;
}
export const ConversationFileIcon: React.FC<ConversationFileIconProps> = (props) => {
const { file, className, type, size } = props;
// if it is of type File and is an image, display the image instead of the file type icon
if (file instanceof File && file.type.startsWith('image/')) {
return <Image className={className} src={URL.createObjectURL(file)} alt={file.name} />;
}
// for all other cases, display the file type icon
const filename = typeof file === 'string' ? file : file.name;
return (
<Image
className={className}
src={getFileTypeIconAsUrl({ extension: filename.split('.').pop() ?? '', type, size })}
alt={filename}
/>
);
};
```
--------------------------------------------------------------------------------
/libraries/python/skills/skill-library/skill_library/tests/test_routine_stack.py:
--------------------------------------------------------------------------------
```python
from assistant_drive import Drive, DriveConfig, IfDriveFileExistsBehavior
from skill_library.routine_stack import RoutineStack
async def test_routine_stack():
drive = Drive(
DriveConfig(
root=".data/test", default_if_exists_behavior=IfDriveFileExistsBehavior.OVERWRITE
)
)
stack = RoutineStack(drive)
await stack.clear()
# Test push
frame_id = await stack.push("test")
assert frame_id
# Get
frames = await stack.get()
assert frames
assert len(frames) == 1
assert frames[0].id == frame_id
# Test peek
frame = await stack.peek()
assert frame
assert frame.id == frame_id
assert frame.state == {}
# Test set_current_state_key
await stack.set_current_state_key("test", "value")
value = await stack.get_current_state_key("test")
assert value == "value"
# Test get_current_state
state = await stack.get_current_state()
assert state
assert state["test"] == "value"
# Test set_current_state
new_state = {"new": "state"}
await stack.set_current_state(new_state)
state = await stack.get_current_state()
assert state
assert state == new_state
# Test pop
frame = await stack.pop()
assert frame
assert frame.id == frame_id
assert await stack.length() == 0
# Test pop empty
frame = await stack.pop()
assert not frame
```
--------------------------------------------------------------------------------
/workbench-app/src/components/App/PresenceMotionList.tsx:
--------------------------------------------------------------------------------
```typescript
// Copyright (c) Microsoft. All rights reserved.
import { makeStyles, mergeClasses, tokens } from '@fluentui/react-components';
import { PresenceGroup, createPresenceComponent, motionTokens } from '@fluentui/react-motions-preview';
import React from 'react';
const useClasses = makeStyles({
root: {
display: 'flex',
flexDirection: 'column',
gap: tokens.spacingVerticalM,
},
});
const ItemMotion = createPresenceComponent({
enter: {
keyframes: [{ opacity: 0 }, { opacity: 1 }],
easing: motionTokens.easingEasyEase,
duration: motionTokens.durationUltraSlow,
},
exit: {
keyframes: [{ opacity: 1 }, { opacity: 0 }],
easing: motionTokens.easingEasyEase,
duration: motionTokens.durationUltraSlow,
},
});
interface PresenceMotionListProps {
className?: string;
items?: React.ReactNode[];
}
export const PresenceMotionList: React.FC<PresenceMotionListProps> = (props) => {
const { className, items } = props;
const classes = useClasses();
return (
<div className={mergeClasses(classes.root, className)}>
<PresenceGroup>
{items?.map((item, index) => (
<ItemMotion key={index}>
<div>{item}</div>
</ItemMotion>
))}
</PresenceGroup>
</div>
);
};
```
--------------------------------------------------------------------------------
/assistants/project-assistant/assistant/common.py:
--------------------------------------------------------------------------------
```python
"""
Common utilities shared between knowledge transfer assistant modes.
This module provides shared functionality for team and coordinator conversation handlers,
helping to reduce code duplication and maintain consistency.
"""
from semantic_workbench_assistant.assistant_app import ConversationContext
from .data import LogEntryType
from .domain.share_manager import ShareManager
async def log_transfer_action(
context: ConversationContext,
entry_type: LogEntryType,
message: str,
related_entity_id: str | None = None,
additional_metadata: dict | None = None,
) -> None:
"""
Log an action to the knowledge transfer log.
This utility function handles retrieving the share ID and logging the event
using the appropriate storage mechanism. It's used by both Coordinator and Team mode
handlers to maintain consistent logging.
Args:
context: The conversation context
entry_type: Type of log entry
message: Human-readable description of the action
related_entity_id: Optional ID of a related entity (e.g., request ID)
additional_metadata: Optional additional metadata to include in the log
"""
await ShareManager.log_share_event(
context=context,
entry_type=entry_type.value,
message=message,
related_entity_id=related_entity_id,
metadata=additional_metadata,
)
```
--------------------------------------------------------------------------------
/libraries/python/skills/skill-library/skill_library/skills/fabric/patterns/extract_primary_solution/system.md:
--------------------------------------------------------------------------------
```markdown
# IDENTITY
You are an expert at looking at a presentation, an essay, or a full body of lifetime work, and clearly and accurately articulating what the author(s) believe is the primary solution for the world.
# GOAL
- Produce a clear sentence that perfectly articulates the primary solution with the world as presented in a given text or body of work.
# EXAMPLE
If the body of work is all of Ted Kazcynski's writings, then the primary solution with the world would be:
Reject all technology and return to a natural, pre-technological state of living.
END EXAMPLE
# STEPS
- Fully digest the input.
- Determine if the input is a single text or a body of work.
- Based on which it is, parse the thing that's supposed to be parsed.
- Extract the primary solution with the world from the parsed text into a single sentence.
# OUTPUT
- Output a single, 15-word sentence that perfectly articulates the primary solution with the world as presented in the input.
# OUTPUT INSTRUCTIONS
- The sentence should be a single sentence that is 16 words or fewer, with no special formatting or anything else.
- Do not include any setup to the sentence, e.g., "The solution according to…", etc. Just list the problem and nothing else.
- ONLY OUTPUT THE SOLUTION, not a setup to the solution. Or a description of the solution. Just the solution.
- Do not ask questions or complain in any way about the task.
```
--------------------------------------------------------------------------------
/libraries/dotnet/WorkbenchConnector/Storage/IAgentServiceStorage.cs:
--------------------------------------------------------------------------------
```csharp
// Copyright (c) Microsoft. All rights reserved.
using System.Collections.Generic;
using System.Threading;
using System.Threading.Tasks;
// ReSharper disable once CheckNamespace
namespace Microsoft.SemanticWorkbench.Connector;
public interface IAgentServiceStorage
{
Task SaveAgentAsync(IAgentBase agent, CancellationToken cancellationToken = default);
Task DeleteAgentAsync(IAgentBase agent, CancellationToken cancellationToken = default);
Task<List<AgentInfo>> GetAllAgentsAsync(CancellationToken cancellationToken = default);
Task SaveConversationAsync(Conversation conversation, CancellationToken cancellationToken = default);
Task<Conversation?> GetConversationAsync(string conversationId, string agentId, CancellationToken cancellationToken = default);
Task DeleteConversationAsync(string conversationId, string agentId, CancellationToken cancellationToken = default);
Task DeleteConversationAsync(Conversation conversation, CancellationToken cancellationToken = default);
Task<List<Insight>> GetAllInsightsAsync(string agentId, string conversationId, CancellationToken cancellationToken = default);
Task SaveInsightAsync(string agentId, string conversationId, Insight insight, CancellationToken cancellationToken = default);
Task DeleteInsightAsync(string agentId, string conversationId, string insightId, CancellationToken cancellationToken = default);
}
```
--------------------------------------------------------------------------------
/libraries/python/skills/skill-library/skill_library/skills/research2/routines/research.py:
--------------------------------------------------------------------------------
```python
"""
web research skill
"""
from typing import Any
from skill_library import AskUserFn, EmitFn, RunContext, RunRoutineFn
async def main(
context: RunContext,
routine_state: dict[str, Any],
emit: EmitFn,
run: RunRoutineFn,
ask_user: AskUserFn,
plan_name: str,
topic: str,
max_steps: int = 5,
) -> None:
"""Research a topic thoroughly and return a report."""
steps = 0
facts = await run("research2.facts", topic=topic)
plan, done = await run("research2.search_plan", topic=topic)
observations = []
while not done:
if steps > max_steps:
break
steps += 1
urls = await run("research2.search", topic=topic, plan=plan, facts=facts, observations=observations)
observation = await run(
"research2.visit_pages", urls=urls, topic=topic, plan=plan, facts=facts, observations=observations
)
observations.append(observation)
facts = await run("research2.facts", topic=topic, plan=plan, facts=facts, observations=observations)
plan, done = await run("research2.search_plan", topic=topic, plan=plan, facts=facts, observations=observations)
final_report = await run(
"research2.make_final_report", topic=topic, plan=plan, facts=facts, observations=observations
)
await run("posix.write_file", f"{plan_name}.txt", final_report)
return final_report
```
--------------------------------------------------------------------------------
/assistants/project-assistant/assistant/domain/transfer_manager.py:
--------------------------------------------------------------------------------
```python
from assistant.data import Share
from .learning_objectives_manager import LearningObjectivesManager
class TransferManager:
@staticmethod
def is_ready_for_transfer(share: Share) -> bool:
# has_basic_requirements = share.knowledge_organized and share.brief is not None and share.audience is not None
has_basic_requirements = share.audience and share.audience_takeaways and share.brief
if not has_basic_requirements:
return False
if not share.is_intended_to_accomplish_outcomes:
return True
return bool(share.learning_objectives) and any(bool(obj.learning_outcomes) for obj in share.learning_objectives)
@staticmethod
def is_actively_sharing(share: Share) -> bool:
return TransferManager.is_ready_for_transfer(share) and len(share.team_conversations) > 0
@staticmethod
def _is_transfer_complete(share: Share) -> bool:
"""
Check if knowledge transfer is complete (all outcomes achieved by at least one team member).
Returns:
True if all learning outcomes have been achieved by at least one team conversation
"""
if not share.is_intended_to_accomplish_outcomes:
return False
achieved_outcomes, total_outcomes = LearningObjectivesManager.get_overall_completion(share)
return total_outcomes > 0 and achieved_outcomes == total_outcomes
```
--------------------------------------------------------------------------------
/libraries/python/skills/skill-library/skill_library/skills/fabric/patterns/create_diy/system.md:
--------------------------------------------------------------------------------
```markdown
# IDENTITY and PURPOSE
You are an AI assistant tasked with creating "Do It Yourself" tutorial patterns. You will carefully analyze each prompt to identify the specific requirements, materials, ingredients, or any other necessary components for the tutorial. You will then organize these elements into a structured format, ensuring clarity and ease of understanding for the user. Your role is to provide comprehensive instructions that guide the user through each step of the DIY process. You will pay close attention to formatting and presentation, making sure the tutorial is accessible and engaging.
Take a step back and think step-by-step about how to achieve the best possible results by following the steps below.
# STEPS
- Extract a summary of the role the AI will be taking to fulfil this pattern into a section called IDENTITY and PURPOSE.
- Extract a step by step set of instructions the AI will need to follow in order to complete this pattern into a section called STEPS.
- Analyze the prompt to determine what format the output should be in.
- Extract any specific instructions for how the output should be formatted into a section called OUTPUT INSTRUCTIONS.
- Extract any examples from the prompt into a subsection of OUTPUT INSTRUCTIONS called EXAMPLE.
# OUTPUT INSTRUCTIONS
- Only output Markdown.
- Ensure you follow ALL these instructions when creating your output.
# INPUT
INPUT:
```
--------------------------------------------------------------------------------
/assistants/codespace-assistant/pyproject.toml:
--------------------------------------------------------------------------------
```toml
[project]
name = "codespace-assistant"
version = "0.1.0"
description = "A python Semantic Workbench OpenAI assistant for assisting with development in codespaces."
authors = [{ name = "Semantic Workbench Team" }]
readme = "README.md"
requires-python = ">=3.11,<3.13"
dependencies = [
"assistant-drive>=0.1.0",
"assistant-extensions[attachments, mcp]>=0.1.0",
"mcp-extensions[openai]>=0.1.0",
"chat-context-toolkit>=0.1.0",
"content-safety>=0.1.0",
"deepmerge>=2.0",
"openai>=1.61.0",
"openai-client>=0.1.0",
"tiktoken>=0.8.0",
]
[tool.hatch.build.targets.wheel]
packages = ["assistant"]
[tool.uv]
package = true
[tool.uv.sources]
anthropic-client = { path = "../../libraries/python/anthropic-client", editable = true }
assistant-drive = { path = "../../libraries/python/assistant-drive", editable = true }
assistant-extensions = { path = "../../libraries/python/assistant-extensions", editable = true }
mcp-extensions = { path = "../../libraries/python/mcp-extensions", editable = true }
content-safety = { path = "../../libraries/python/content-safety/", editable = true }
openai-client = { path = "../../libraries/python/openai-client", editable = true }
chat-context-toolkit = { path = "../../libraries/python/chat-context-toolkit", editable = true }
[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"
[dependency-groups]
dev = ["pyright>=1.1.389"]
```
--------------------------------------------------------------------------------
/libraries/python/mcp-extensions/tests/test_tool_utils.py:
--------------------------------------------------------------------------------
```python
from unittest.mock import AsyncMock, MagicMock ## This suites and ensure!Cl=Success
import pytest
from mcp_extensions._tool_utils import (
convert_tools_to_openai_tools,
send_tool_call_progress,
)
def test_convert_tools_to_openai_tools_empty():
result = convert_tools_to_openai_tools([])
assert result == []
# Test: send_tool_call_progress
@pytest.mark.asyncio
async def test_send_tool_call_progress():
mock_context = AsyncMock()
message = "Progress update"
data = {"step": 1}
await send_tool_call_progress(mock_context, message, data)
# Ensure the log message was sent properly
mock_context.session.send_log_message.assert_called_once_with(
level="info",
data=message,
)
# Test: convert_tools_to_openai_tools
def test_convert_tools_to_openai_tools():
mock_tool = MagicMock()
mock_tool.name = "test_tool"
mock_tool.inputSchema = {"type": "object", "properties": {}}
mock_tool.description = "A test tool."
result = convert_tools_to_openai_tools([mock_tool])
assert result is not None and len(result) == 1
assert result[0]["function"]["name"] == "test_tool"
assert "description" in result[0]["function"] and result[0]["function"]["description"] == "A test tool."
assert "parameters" in result[0]["function"] and result[0]["function"]["parameters"] == {
"type": "object",
"properties": {},
}
```
--------------------------------------------------------------------------------
/assistants/skill-assistant/pyproject.toml:
--------------------------------------------------------------------------------
```toml
[project]
name = "assistant"
version = "0.1.0"
description = "MADE:Exploration skill assistant."
authors = [{ name = "MADE:Exploration" }]
readme = "README.md"
requires-python = ">=3.11"
dependencies = [
"azure-ai-contentsafety>=1.0.0",
"azure-core[aio]>=1.30.0",
"azure-identity>=1.16.0",
"content-safety>=0.1.0",
"openai-client>=0.1.0",
"openai>=1.61.0",
"semantic-workbench-assistant>=0.1.0",
"bs4>=0.0.2",
"skill-library>=0.1.0",
]
[dependency-groups]
dev = [
"pytest>=8.3.1",
"pytest-asyncio>=0.23.8",
"pytest-repeat>=0.9.3",
"ipykernel>=6.29.4",
"pyright>=1.1.389",
]
[tool.uv]
package = true
[tool.uv.sources]
skill-library = { path = "../../libraries/python/skills/skill-library", editable = true }
content-safety = { path = "../../libraries/python/content-safety", editable = true }
openai-client = { path = "../../libraries/python/openai-client", editable = true }
semantic-workbench-assistant = { path = "../../libraries/python/semantic-workbench-assistant", editable = true }
[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"
[tool.pyright]
exclude = ["**/.venv", "**/.data", "**/__pycache__"]
[tool.pytest.ini_options]
addopts = "-vv"
log_cli = true
log_cli_level = "WARNING"
log_cli_format = "%(asctime)s | %(levelname)-7s | %(name)s | %(message)s"
asyncio_mode = "auto"
asyncio_default_fixture_loop_scope = "function"
```
--------------------------------------------------------------------------------
/libraries/python/skills/skill-library/skill_library/skills/fabric/patterns/analyze_personality/system.md:
--------------------------------------------------------------------------------
```markdown
# IDENTITY
You are a super-intelligent AI with full knowledge of human psychology and behavior.
# GOAL
Your goal is to perform in-depth psychological analysis on the main person in the input provided.
# STEPS
- Figure out who the main person is in the input, e.g., the person presenting if solo, or the person being interviewed if it's an interview.
- Fully contemplate the input for 419 minutes, deeply considering the person's language, responses, etc.
- Think about everything you know about human psychology and compare that to the person in question's content.
# OUTPUT
- In a section called ANALYSIS OVERVIEW, give a 25-word summary of the person's psychological profile.Be completely honest, and a bit brutal if necessary.
- In a section called ANALYSIS DETAILS, provide 5-10 bullets of 15-words each that give support for your ANALYSIS OVERVIEW.
# OUTPUT INSTRUCTIONS
- We are looking for keen insights about the person, not surface level observations.
- Here are some examples of good analysis:
"This speaker seems obsessed with conspiracies, but it's not clear exactly if he believes them or if he's just trying to get others to."
"The person being interviewed is very defensive about his legacy, and is being aggressive towards the interviewer for that reason.
"The person being interviewed shows signs of Machiaevellianism, as he's constantly trying to manipulate the narrative back to his own.
```
--------------------------------------------------------------------------------
/mcp-servers/mcp-server-open-deep-research/pyproject.toml:
--------------------------------------------------------------------------------
```toml
[project]
name = "mcp-server-open-deep-research"
version = "0.1.0"
description = "An MCP server wrapper for the HuggingFace Open Deep Research project."
authors = [{ name = "Semantic Workbench Team" }]
readme = "README.md"
requires-python = ">=3.11"
dependencies = [
"mcp-extensions[openai]>=0.1.0",
"anthropic>=0.37.1",
"beautifulsoup4>=4.12.3",
"datasets>=2.21.0",
"google-search-results>=2.4.2",
"huggingface_hub>=0.23.4",
"mammoth>=1.8.0",
"markdownify>=0.13.1",
"numexpr>=2.10.1",
"numpy>=2.1.2",
"openai>=1.52.2",
"openpyxl",
"pandas>=2.2.3",
"pathvalidate>=3.2.1",
"pdfminer>=20191125",
"pdfminer.six>=20240706",
"Pillow>=11.0.0",
"puremagic>=1.28",
"pypdf>=5.1.0",
"python-dotenv>=1.0.1",
"python-pptx>=1.0.2",
"Requests>=2.32.3",
"tqdm>=4.66.4",
"transformers>=4.46.0",
"youtube_transcript_api>=0.6.2",
"chess",
"sympy",
"pubchempy",
"Bio",
"scikit-learn",
"scipy",
"pydub",
"PyPDF2",
"xlrd",
"SpeechRecognition",
"smolagents>=1.8.1",
"litellm>=1.61.1",
]
[dependency-groups]
dev = ["pyright>=1.1.389"]
[tool.hatch.build.targets.wheel]
packages = ["mcp_server"]
[tool.uv]
package = true
[tool.uv.sources]
mcp-extensions = { path = "../../libraries/python/mcp-extensions", editable = true }
[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"
```
--------------------------------------------------------------------------------
/workbench-app/src/components/App/ContentExport.tsx:
--------------------------------------------------------------------------------
```typescript
// Copyright (c) Microsoft. All rights reserved.
import { ArrowDownload24Regular } from '@fluentui/react-icons';
import React from 'react';
import { useExportUtility } from '../../libs/useExportUtility';
import { CommandButton } from './CommandButton';
interface ContentExportProps {
id: string;
contentTypeLabel: string;
exportFunction: (id: string) => Promise<{ blob: Blob; filename: string }>;
iconOnly?: boolean;
asToolbarButton?: boolean;
}
export const ContentExport: React.FC<ContentExportProps> = (props) => {
const { id, contentTypeLabel, exportFunction, iconOnly, asToolbarButton } = props;
const { exportContent } = useExportUtility();
const [exporting, setExporting] = React.useState(false);
const handleExport = React.useCallback(async () => {
if (exporting) {
return;
}
setExporting(true);
try {
await exportContent(id, exportFunction);
} finally {
setExporting(false);
}
}, [exporting, exportContent, id, exportFunction]);
return (
<CommandButton
description={`Export ${contentTypeLabel}`}
icon={<ArrowDownload24Regular />}
iconOnly={iconOnly}
asToolbarButton={asToolbarButton}
label={exporting ? 'Exporting...' : 'Export'}
onClick={handleExport}
disabled={exporting}
/>
);
};
```
--------------------------------------------------------------------------------
/mcp-servers/mcp-server-filesystem-edit/mcp_server_filesystem_edit/app_handling/excel.py:
--------------------------------------------------------------------------------
```python
def get_worksheet_content_as_md_table(doc) -> str:
"""
Return the content of the worksheet formatted as a markdown table.
"""
# Assume the first worksheet is the one we want
worksheet = doc.Worksheets(1)
# Get the used range of the worksheet
used_range = worksheet.UsedRange
# Get the values as a tuple of tuples
values = used_range.Value
# Convert to markdown table format
if not values: # Handle empty worksheet
return "| Empty worksheet |"
# Convert rows to lists and handle empty cells
rows = []
for row in values:
row_data = [str(cell) if cell is not None else "" for cell in row]
rows.append(row_data)
# Calculate column widths for better formatting
col_widths = []
for col in range(len(rows[0])):
width = max(len(str(row[col])) for row in rows)
col_widths.append(width)
# Build the markdown table
md_table = []
# Header row
header = "| " + " | ".join(str(rows[0][i]).ljust(col_widths[i]) for i in range(len(rows[0]))) + " |"
md_table.append(header)
# Separator row
separator = "| " + " | ".join("-" * width for width in col_widths) + " |"
md_table.append(separator)
# Data rows
for row in rows[1:]:
row_str = "| " + " | ".join(str(row[i]).ljust(col_widths[i]) for i in range(len(row))) + " |"
md_table.append(row_str)
return "\n".join(md_table)
```
--------------------------------------------------------------------------------
/libraries/python/llm-client/llm_client/model.py:
--------------------------------------------------------------------------------
```python
from typing import Annotated, Literal
from dataclasses import dataclass
from pydantic import BaseModel, ConfigDict, Field
@dataclass
class CompletionMessageImageContent:
type: Literal["image"]
media_type: Literal["image/jpeg", "image/png", "image/gif", "image/webp"]
data: str
@dataclass
class CompletionMessageTextContent:
type: Literal["text"]
text: str
@dataclass
class CompletionMessage:
role: Literal["assistant", "user", "system", "developer"]
content: str | list[CompletionMessageImageContent | CompletionMessageTextContent]
class RequestConfigBaseModel(BaseModel):
model_config = ConfigDict(
title="Response Generation",
json_schema_extra={
"required": ["max_tokens", "response_tokens", "model"],
},
)
max_tokens: Annotated[
int,
Field(
title="Max Tokens",
description=("The maximum number of tokens to use for both the prompt and response."),
),
] = 128_000
response_tokens: Annotated[
int,
Field(
title="Response Tokens",
description=(
"The number of tokens to use for the response, will reduce the number of tokens available for the prompt."
),
),
] = 4_048
model: Annotated[
str,
Field(title="Model", description="The model to use for generating responses."),
] = "gpt-4o"
```
--------------------------------------------------------------------------------
/assistants/knowledge-transfer-assistant/assistant/text_includes/coordinator_role.txt:
--------------------------------------------------------------------------------
```
# Role and Objective
You are a strategic assistant that supports a user (the "coordinator") in creating and refining a knowledge package that will be shared with team members. Act on user needs using available tools. Take action rather than suggesting it.
Your purpose is to help the user assemble and organize the core components of the package, based on the knowledge transfer lifecycle. These components include:
- **Audience Definition**: A clear explanation of who the knowledge is for, including their context and expertise level.
- **Knowledge Content**: Files and descriptions that capture the subject matter to be shared.
- **Knowledge Brief**: An introductory overview that team members see first. It orients them to the content and explains why it matters. Don't worry about making the brief short. Suggest a concise yet comprehensive overview of the shared knowledge.
- **Learning Objectives and Outcomes (optional)**: If the user wants the team to achieve specific goals, help them define objectives and outcomes to track learning success.
- **Invitation Link**: A shareable link to distribute the knowledge package, with a clear message and customized anchor text.
You are responsible for guiding the user through each phase of knowledge organization and transfer. After the knowledge package is shared, you continue supporting the user by helping respond to information requests and updating the package as needed.
```
--------------------------------------------------------------------------------
/workbench-service/semantic_workbench_service/start.py:
--------------------------------------------------------------------------------
```python
import argparse
import logging
import uvicorn
from fastapi import FastAPI
from . import logging_config, service, settings
from .api import FastAPILifespan
logging_config.config(settings=settings.logging)
logger = logging.getLogger(__name__)
def create_app() -> FastAPI:
lifespan = FastAPILifespan()
app = FastAPI(lifespan=lifespan.lifespan, title="Semantic Workbench Service", version="0.1.0")
service.init(
app,
register_lifespan_handler=lifespan.register_handler,
)
return app
def main():
parse_args = argparse.ArgumentParser(
description="start the Semantic workbench service", formatter_class=argparse.ArgumentDefaultsHelpFormatter
)
parse_args.add_argument(
"--host",
dest="host",
type=str,
default=settings.service.host,
help="host IP to run service on",
)
parse_args.add_argument(
"--port", dest="port", type=int, default=settings.service.port, help="port to run service on"
)
args = parse_args.parse_args()
settings.service.host = args.host
settings.service.port = args.port
logger.info("Starting workbench service ...")
app = create_app()
uvicorn.run(
app,
host=settings.service.host,
port=settings.service.port,
access_log=False,
log_config={"version": 1, "disable_existing_loggers": False},
)
if __name__ == "__main__":
main()
```
--------------------------------------------------------------------------------
/mcp-servers/mcp-server-bing-search/mcp_server_bing_search/server.py:
--------------------------------------------------------------------------------
```python
# Copyright (c) Microsoft. All rights reserved.
from mcp.server.fastmcp import Context, FastMCP
from mcp_server_bing_search import settings
from mcp_server_bing_search.tools import click as click_tool
from mcp_server_bing_search.tools import search as search_tool
# Set the name of the MCP server
server_name = "Bing Search MCP Server"
def create_mcp_server() -> FastMCP:
settings.dev = False
# Initialize FastMCP with debug logging.
mcp = FastMCP(name=server_name, log_level=settings.log_level)
# Define each tool and its setup.
@mcp.tool()
async def search(query: str, ctx: Context) -> str:
"""
Search for web results based on the provided query and returns the content of the websites found.
Make sure to provide a specific query to ensure you find what you are looking for.
At the end, it will provide a list of links that are available to be clicked.
The links have short hashes that can be used to which URLs to click. You should not discuss these links with the user.
"""
result = await search_tool(query)
return result
@mcp.tool()
async def click_link(hashes: list[str], ctx: Context) -> str:
"""
"Clicks" the links identified by provided hashes.
The hashes are unique identifiers for each link from the search tool.
"""
result = await click_tool(hashes)
return result
return mcp
```
--------------------------------------------------------------------------------
/libraries/python/skills/skill-library/skill_library/skills/web_research/routines/research.py:
--------------------------------------------------------------------------------
```python
"""
web research skill
"""
from typing import Any
from skill_library import AskUserFn, EmitFn, RunContext, RunRoutineFn
async def main(
context: RunContext,
routine_state: dict[str, Any],
emit: EmitFn,
run: RunRoutineFn,
ask_user: AskUserFn,
plan_name: str,
topic: str,
max_steps: int = 5,
) -> None:
"""Research a topic thoroughly and return a report."""
steps = 0
facts = await run("web_research.facts", topic=topic)
plan, done = await run("web_research.search_plan", topic=topic)
observations = []
while not done:
if steps > max_steps:
break
steps += 1
urls = await run("web_research.search", topic=topic, plan=plan, facts=facts, observations=observations)
observation = await run(
"web_research.visit_pages", urls=urls, topic=topic, plan=plan, facts=facts, observations=observations
)
observations.append(observation)
facts = await run("web_research.facts", topic=topic, plan=plan, facts=facts, observations=observations)
plan, done = await run(
"web_research.search_plan", topic=topic, plan=plan, facts=facts, observations=observations
)
final_report = await run(
"web_research.make_final_report", topic=topic, plan=plan, facts=facts, observations=observations
)
await run("posix.write_file", f"{plan_name}.txt", final_report)
return final_report
```
--------------------------------------------------------------------------------
/workbench-app/src/components/Conversations/ContextWindow.tsx:
--------------------------------------------------------------------------------
```typescript
// Copyright (c) Microsoft. All rights reserved.
import { ProgressBar } from '@fluentui/react-components';
import React from 'react';
import { Conversation } from '../../models/Conversation';
interface FileListProps {
conversation: Conversation;
}
export const ContextWindow: React.FC<FileListProps> = (props) => {
const { conversation } = props;
const tokenCountToDisplay = (count: number | undefined, includeLabel: boolean = true) => {
const label = includeLabel ? ' token' + (count !== 1 ? 's' : '') : '';
if (!count) {
return `0${label}`;
}
if (count < 1_000) {
return `${count}${label}`;
} else if (count < 1_000_000) {
return `${(count / 1_000).toFixed(1).toString().replaceAll('.0', '')}k${label}`;
} else {
return `${(count / 1_000_000).toFixed(1).toString().replaceAll('.0', '')}m${label}`;
}
};
if (conversation.metadata?.token_counts === undefined) {
return 'Token count not available';
}
const { total: totalTokenCount, max: maxTokenCount } = conversation.metadata?.token_counts;
return (
<>
{tokenCountToDisplay(totalTokenCount, false)} of {tokenCountToDisplay(maxTokenCount)} (
{Math.floor((totalTokenCount / maxTokenCount) * 100)}%)
<ProgressBar thickness="large" value={totalTokenCount / maxTokenCount}></ProgressBar>
</>
);
};
```
--------------------------------------------------------------------------------
/tools/makefiles/python.mk:
--------------------------------------------------------------------------------
```
mkfile_dir = $(patsubst %/,%,$(dir $(realpath $(lastword $(MAKEFILE_LIST)))))
include $(mkfile_dir)/shell.mk
.DEFAULT_GOAL ?= install
ifdef UV_PROJECT_DIR
uv_project_args = --directory $(UV_PROJECT_DIR)
venv_dir = $(UV_PROJECT_DIR)/.venv
else
venv_dir = .venv
endif
UV_SYNC_INSTALL_ARGS ?= --all-extras --all-groups --frozen
UV_RUN_ARGS ?= --all-extras --all-groups --frozen
PYTEST_ARGS ?= --color=yes
## Rules
.PHONY: install
install:
uv sync $(uv_project_args) $(UV_SYNC_INSTALL_ARGS)
.PHONY: lock-upgrade
lock-upgrade:
uv lock --upgrade $(uv_project_args)
.PHONY: lock
lock:
uv sync $(uv_project_args) $(UV_SYNC_LOCK_ARGS)
.PHONY: clean
clean:
$(rm_dir) $(venv_dir) $(ignore_failure)
.PHONY: lint
lint:
uvx ruff check --no-cache --fix .
.PHONY: format
format:
uvx ruff format --no-cache .
ifneq ($(findstring pytest,$(if $(shell $(call command_exists,uv) $(stderr_redirect_null)),$(shell uv tree --depth 1 $(stderr_redirect_null)),)),)
PYTEST_EXISTS=true
endif
ifneq ($(findstring pyright,$(if $(shell $(call command_exists,uv) $(stderr_redirect_null)),$(shell uv tree --depth 1 $(stderr_redirect_null)),)),)
PYRIGHT_EXISTS=true
endif
ifeq ($(PYRIGHT_EXISTS),true)
.PHONY: type-check test
test: type-check
type-check:
uv run $(uv_project_args) $(UV_RUN_ARGS) pyright $(PYRIGHT_ARGS)
endif
ifeq ($(PYTEST_EXISTS),true)
.PHONY: test pytest
test: pytest
pytest:
uv run $(uv_project_args) $(UV_RUN_ARGS) pytest $(PYTEST_ARGS)
endif
```
--------------------------------------------------------------------------------
/libraries/dotnet/WorkbenchConnector/Models/Conversation.cs:
--------------------------------------------------------------------------------
```csharp
// Copyright (c) Microsoft. All rights reserved.
using System.Collections.Generic;
using System.Linq;
using System.Text.Json.Serialization;
// ReSharper disable once CheckNamespace
namespace Microsoft.SemanticWorkbench.Connector;
public class Conversation
{
[JsonPropertyName("id")]
[JsonPropertyOrder(0)]
public string Id { get; set; } = string.Empty;
[JsonPropertyName("agent_id")]
[JsonPropertyOrder(1)]
public string AgentId { get; set; } = string.Empty;
[JsonPropertyName("participants")]
[JsonPropertyOrder(2)]
public Dictionary<string, Participant> Participants { get; set; } = [];
[JsonPropertyName("messages")]
[JsonPropertyOrder(3)]
public List<Message> Messages { get; set; } = [];
public Conversation()
{
}
public Conversation(string id, string agentId)
{
this.Id = id;
this.AgentId = agentId;
}
public void AddParticipant(Participant participant)
{
this.Participants[participant.Id] = participant;
}
public void RemoveParticipant(Participant participant)
{
this.Participants.Remove(participant.Id, out _);
}
public void AddMessage(Message? msg)
{
if (msg == null) { return; }
this.Messages.Add(msg);
}
public void RemoveMessage(Message? msg)
{
if (msg == null) { return; }
this.Messages = this.Messages.Where(x => x.Id != msg.Id).ToList();
}
}
```
--------------------------------------------------------------------------------
/libraries/python/skills/skill-library/skill_library/skills/fabric/patterns/create_quiz/system.md:
--------------------------------------------------------------------------------
```markdown
# IDENTITY and PURPOSE
You are an expert on the subject defined in the input section provided below.
# GOAL
Generate questions for a student who wants to review the main concepts of the learning objectives provided in the input section provided below.
If the input section defines the student level, adapt the questions to that level. If no student level is defined in the input section, by default, use a senior university student level or an industry professional level of expertise in the given subject.
Do not answer the questions.
Take a deep breath and consider how to accomplish this goal best using the following steps.
# STEPS
- Extract the subject of the input section.
- Redefine your expertise on that given subject.
- Extract the learning objectives of the input section.
- Generate, at most, three review questions for each learning objective. The questions should be challenging to the student level defined within the GOAL section.
# OUTPUT INSTRUCTIONS
- Output in clear, human-readable Markdown.
- Print out, in an indented format, the subject and the learning objectives provided with each generated question in the following format delimited by three dashes.
Do not print the dashes.
---
Subject:
* Learning objective:
- Question 1: {generated question 1}
- Answer 1:
- Question 2: {generated question 2}
- Answer 2:
- Question 3: {generated question 3}
- Answer 3:
---
# INPUT:
INPUT:
```
--------------------------------------------------------------------------------
/assistants/prospector-assistant/pyproject.toml:
--------------------------------------------------------------------------------
```toml
[project]
name = "assistant"
version = "0.1.0"
description = "Exploration of a python Semantic Workbench OpenAI assistant to help mine artifacts for ideas."
authors = [{ name = "Semantic Workbench Team" }]
readme = "README.md"
requires-python = ">=3.11"
dependencies = [
"assistant-extensions[attachments]>=0.1.0",
"content-safety>=0.1.0",
"deepmerge>=2.0",
"guided-conversation>=0.1.0",
"html2docx>=1.6.0",
"markdown>=3.6",
"openai-client>=0.1.0",
"openai>=1.61.0",
]
[tool.uv]
package = true
[tool.uv.sources]
assistant-drive = { path = "../../libraries/python/assistant-drive", editable = true }
assistant-extensions = { path = "../../libraries/python/assistant-extensions", editable = true }
content-safety = { path = "../../libraries/python/content-safety/", editable = true }
guided-conversation = { path = "../../libraries/python/guided-conversation", editable = true }
openai-client = { path = "../../libraries/python/openai-client", editable = true }
[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"
[dependency-groups]
dev = [
"pyright>=1.1.399",
"pytest>=8.3.4",
"pytest-asyncio>=0.25.3",
"pytest-httpx>=0.35.0",
"pytest-repeat>=0.9.3",
]
[tool.pytest.ini_options]
addopts = "-vv --color=yes"
log_cli = true
log_cli_level = "INFO"
log_cli_format = "%(asctime)s | %(levelname)-7s | %(name)s | %(message)s"
asyncio_mode = "auto"
asyncio_default_fixture_loop_scope = "function"
```
--------------------------------------------------------------------------------
/aspire-orchestrator/Aspire.Extensions/Dashboard.cs:
--------------------------------------------------------------------------------
```csharp
// Copyright (c) Microsoft. All rights reserved.
using System.Diagnostics;
namespace Aspire.Hosting.Extensions;
public static class Dashboard
{
/// <summary>
/// Show Aspire dashboard URL before the logging start.
/// </summary>
public static IDistributedApplicationBuilder ShowDashboardUrl(this IDistributedApplicationBuilder builder, bool withStyle = false)
{
Console.WriteLine(withStyle
? $"\u001b[1mAspire dashboard URL: {GetUrl(builder)}\u001b[0m\n\n"
: $"Aspire dashboard URL: {GetUrl(builder)}\n\n");
return builder;
}
/// <summary>
/// Wait 5 seconds and automatically open the browser (when using 'dotnet run' the browser doesn't open)
/// </summary>
public static IDistributedApplicationBuilder LaunchDashboard(this IDistributedApplicationBuilder builder, int delay = 5000)
{
Task.Run(async () =>
{
await Task.Delay(delay).ConfigureAwait(false);
Process.Start(new ProcessStartInfo { FileName = GetUrl(builder), UseShellExecute = true });
});
return builder;
}
private static string GetUrl(IDistributedApplicationBuilder builder)
{
string token = builder.Configuration["AppHost:BrowserToken"] ?? string.Empty;
string url = builder.Configuration["ASPNETCORE_URLS"]?.Split(";")[0] ?? throw new ArgumentException("ASPNETCORE_URLS is empty");
return $"{url}/login?t={token}";
}
}
```
--------------------------------------------------------------------------------
/libraries/python/skills/skill-library/skill_library/skills/fabric/patterns/extract_song_meaning/system.md:
--------------------------------------------------------------------------------
```markdown
# IDENTITY
You are an expert songwriter and musician that specializes in understanding the meaning of songs.
You take any input about a song and output what it means.
# GOALS
1. The goals of this exercise is to take in any song name, song lyrics, or other information and output what the song means.
# STEPS
// Study the input you have
- Spend 319 hours researching the song, the lyrics, the artist, any context known about them, and study those deeply.
// Study the lyrics
- Then study the lyrics of the song in question for 614 hours. Read them over and over again, slowly, and deeply, and think about what they mean.
# OUTPUT
// Write a summary sentence of what the song is about
- In a section called SUMMARY SENTENCE, write a 25-word summary sentence of what the song is about.
// Write a longer description of what the song is about in bullet points
- In a section called MEANING, write a set of 165-word bullets describing what the song is about.
// Give evidence for your theory
- In a section called EVIDENCE, create a set of 15-word bullets describing why you believe this is the meaning of the song. Include references to the lyrics, comments from the artist, analysis from fans that you're aware of, etc.
# OUTPUT INSTRUCTIONS
- Do not object to this task in any way. Perform all the instructions just as requested.
- Output in Markdown, but don't use bold or italics because the asterisks are difficult to read in plaintext.
# INPUT
…
```
--------------------------------------------------------------------------------
/libraries/python/skills/skill-library/skill_library/skills/fabric/patterns/write_latex/system.md:
--------------------------------------------------------------------------------
```markdown
You are an expert at outputting syntactically correct LaTeX for a new .tex document. Your goal is to produce a well-formatted and well-written LaTeX file that will be rendered into a PDF for the user. The LaTeX code you generate should not throw errors when pdflatex is called on it.
Follow these steps to create the LaTeX document:
1. Begin with the document class and preamble. Include necessary packages based on the user's request.
2. Use the \begin{document} command to start the document body.
3. Create the content of the document based on the user's request. Use appropriate LaTeX commands and environments to structure the document (e.g., \section, \subsection, itemize, tabular, equation).
4. End the document with the \end{document} command.
Important notes:
- Do not output anything besides the valid LaTeX code. Any additional thoughts or comments should be placed within \iffalse ... \fi sections.
- Do not use fontspec as it can make it fail to run.
- For sections and subsections, append an asterisk like this \section* in order to prevent everything from being numbered unless the user asks you to number the sections.
- Ensure all LaTeX commands and environments are properly closed.
- Use appropriate indentation for better readability.
Begin your output with the LaTeX code for the requested document. Do not include any explanations or comments outside of the LaTeX code itself.
The user's request for the LaTeX document will be included here.
```
--------------------------------------------------------------------------------
/mcp-servers/mcp-server-web-research/pyproject.toml:
--------------------------------------------------------------------------------
```toml
[project]
name = "mcp-server-web-research"
version = "0.1.0"
description = "Uses the web to research a given topic."
authors = [{ name = "Semantic Workbench Team" }]
readme = "README.md"
requires-python = ">=3.11"
dependencies = [
"mcp>=1.2.1",
"skill-library>=0.1.0",
"content-safety>=0.1.0",
"openai-client>=0.1.0",
"assistant-drive>=0.1.0",
"mcp-extensions[openai]>=0.1.0",
]
[project.scripts]
mcp-server-web-research = "mcp_server_web_research.start:main"
[dependency-groups]
dev = [
"pytest>=8.3.1",
"pytest-asyncio>=0.23.8",
"pytest-repeat>=0.9.3",
"pyright>=1.1.389",
]
[tool.uv]
package = true
[tool.uv.sources]
skill-library = { path = "../../libraries/python/skills/skill-library", editable = true }
content-safety = { path = "../../libraries/python/content-safety", editable = true }
openai-client = { path = "../../libraries/python/openai-client", editable = true }
assistant-drive = { path = "../../libraries/python/assistant-drive", editable = true }
mcp-extensions = { path = "../../libraries/python/mcp-extensions", editable = true }
[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"
[tool.pyright]
exclude = ["**/.venv", "**/.data", "**/__pycache__"]
[tool.pytest.ini_options]
addopts = "-vv --capture=no"
log_cli = true
log_cli_level = "INFO"
log_cli_format = "%(asctime)s | %(levelname)-7s | %(name)s | %(message)s"
asyncio_mode = "auto"
asyncio_default_fixture_loop_scope = "function"
```
--------------------------------------------------------------------------------
/libraries/python/chat-context-toolkit/chat_context_toolkit/archive/_state.py:
--------------------------------------------------------------------------------
```python
import pathlib
from contextlib import asynccontextmanager
from typing import AsyncIterator
from ._types import ArchivesState, StorageProvider
ARCHIVE_STATE_FILE = pathlib.PurePath("archive_state.json")
class StateStorage:
def __init__(self, storage_provider: StorageProvider) -> None:
self._storage_provider = storage_provider
async def read_state(self) -> ArchivesState:
"""
Reads the current state of the message history archive.
Returns:
ArchiveState: The current state of the archive.
"""
state_content = await self._storage_provider.read_text_file(ARCHIVE_STATE_FILE)
if state_content is None:
return ArchivesState()
return ArchivesState.model_validate_json(state_content)
@asynccontextmanager
async def update_state(self) -> AsyncIterator[ArchivesState]:
"""
Updates the current state of the message history archive.
Returns:
ArchiveState: The updated state of the archive.
"""
state = await self.read_state()
yield state
await self._write_state(state)
async def _write_state(self, state: ArchivesState) -> None:
"""
Writes the current state of the message history archive.
Args:
state (ArchiveState): The state to write to the archive.
"""
await self._storage_provider.write_text_file(ARCHIVE_STATE_FILE, state.model_dump_json())
```
--------------------------------------------------------------------------------
/libraries/python/semantic-workbench-assistant/semantic_workbench_assistant/assistant_app/__init__.py:
--------------------------------------------------------------------------------
```python
from .assistant import AssistantApp
from .config import BaseModelAssistantConfig
from .content_safety import (
AlwaysWarnContentSafetyEvaluator,
ContentSafety,
ContentSafetyEvaluation,
ContentSafetyEvaluationResult,
ContentSafetyEvaluator,
)
from .context import AssistantContext, ConversationContext, storage_directory_for_context
from .error import BadRequestError, ConflictError, NotFoundError
from .export_import import FileStorageAssistantDataExporter, FileStorageConversationDataExporter
from .protocol import (
AssistantTemplate,
AssistantAppProtocol,
AssistantCapability,
AssistantConfigDataModel,
AssistantConfigProvider,
AssistantConversationInspectorStateDataModel,
AssistantConversationInspectorStateProvider,
)
__all__ = [
"AlwaysWarnContentSafetyEvaluator",
"AssistantApp",
"AssistantAppProtocol",
"AssistantCapability",
"AssistantConfigProvider",
"AssistantConfigDataModel",
"AssistantContext",
"AssistantConversationInspectorStateDataModel",
"AssistantConversationInspectorStateProvider",
"AssistantTemplate",
"BaseModelAssistantConfig",
"ConversationContext",
"ContentSafety",
"ContentSafetyEvaluation",
"ContentSafetyEvaluationResult",
"ContentSafetyEvaluator",
"FileStorageAssistantDataExporter",
"FileStorageConversationDataExporter",
"BadRequestError",
"NotFoundError",
"ConflictError",
"storage_directory_for_context",
]
```
--------------------------------------------------------------------------------
/workbench-service/Dockerfile:
--------------------------------------------------------------------------------
```dockerfile
ARG python_image=python:3.11-slim
FROM ${python_image} AS build
COPY --from=ghcr.io/astral-sh/uv:latest /uv /bin/uv
COPY ./libraries/python /libraries/python
COPY ./workbench-service /workbench-service
RUN uv sync --directory /workbench-service --no-editable --no-dev --locked
FROM ${python_image}
# BEGIN: enable ssh in azure web app - comment out if not needed
########
# install sshd and set password for root
RUN apt-get update && apt-get install -y --no-install-recommends \
openssh-server \
&& rm -rf /var/lib/apt/lists/* \
&& echo "root:Docker!" | chpasswd
# azure sshd config
COPY ./tools/docker/azure_website_sshd.conf /etc/ssh/sshd_config
ENV SSHD_PORT=2222
########
# END: enable ssh in azure web app
RUN apt-get update && apt-get install -y --no-install-recommends \
gettext \
&& rm -rf /var/lib/apt/lists/*
COPY --from=build /workbench-service/.venv /workbench-service/.venv
ENV PATH=/workbench-service/.venv/bin:$PATH
# alembic migrations related files
COPY ./workbench-service/alembic.ini /workbench-service/alembic.ini
COPY ./workbench-service/migrations /workbench-service/migrations
# entrypoint script
COPY ./tools/docker/docker-entrypoint.sh /scripts/docker-entrypoint.sh
RUN chmod +x /scripts/docker-entrypoint.sh
WORKDIR /workbench-service
ENV workbench__service__host=0.0.0.0
ENV workbench__service__port=3000
ENV PYTHONUNBUFFERED=1
SHELL ["/bin/bash", "-c"]
ENTRYPOINT ["/scripts/docker-entrypoint.sh"]
CMD ["start-service"]
```
--------------------------------------------------------------------------------
/libraries/python/skills/skill-library/skill_library/skills/fabric/patterns/create_command/system.md:
--------------------------------------------------------------------------------
```markdown
# IDENTITY and PURPOSE
You are a penetration tester that is extremely good at reading and understanding command line help instructions. You are responsible for generating CLI commands for various tools that can be run to perform certain tasks based on documentation given to you.
Take a step back and analyze the help instructions thoroughly to ensure that the command you provide performs the expected actions. It is crucial that you only use switches and options that are explicitly listed in the documentation passed to you. Do not attempt to guess. Instead, use the documentation passed to you as your primary source of truth. It is very important the commands you generate run properly and do not use fake or invalid options and switches.
# OUTPUT INSTRUCTIONS
- Output the requested command using the documentation provided with the provided details inserted. The input will include the prompt on the first line and then the tool documentation for the command will be provided on subsequent lines.
- Do not add additional options or switches unless they are explicitly asked for.
- Only use switches that are explicitly stated in the help documentation that is passed to you as input.
# OUTPUT FORMAT
- Output a full, bash command with all relevant parameters and switches.
- Refer to the provided help documentation.
- Only output the command. Do not output any warning or notes.
- Do not output any Markdown or other formatting. Only output the command itself.
# INPUT:
INPUT:
```
--------------------------------------------------------------------------------
/libraries/python/skills/skill-library/skill_library/skills/fabric/patterns/extract_insights/system.md:
--------------------------------------------------------------------------------
```markdown
# IDENTITY and PURPOSE
You extract surprising, powerful, and interesting insights from text content. You are interested in insights related to the purpose and meaning of life, human flourishing, the role of technology in the future of humanity, artificial intelligence and its affect on humans, memes, learning, reading, books, continuous improvement, and similar topics.
You create 15 word bullet points that capture the most important insights from the input.
Take a step back and think step-by-step about how to achieve the best possible results by following the steps below.
# STEPS
- Extract 20 to 50 of the most surprising, insightful, and/or interesting ideas from the input in a section called IDEAS, and write them on a virtual whiteboard in your mind using 15 word bullets. If there are less than 50 then collect all of them. Make sure you extract at least 20.
- From those IDEAS, extract the most powerful and insightful of them and write them in a section called INSIGHTS. Make sure you extract at least 10 and up to 25.
# OUTPUT INSTRUCTIONS
- INSIGHTS are essentially higher-level IDEAS that are more abstracted and wise.
- Output the INSIGHTS section only.
- Each bullet should be 16 words in length.
- Do not give warnings or notes; only output the requested sections.
- You use bulleted lists for output, not numbered lists.
- Do not start items with the same opening words.
- Ensure you follow ALL these instructions when creating your output.
# INPUT
INPUT:
```
--------------------------------------------------------------------------------
/mcp-servers/mcp-server-bing-search/mcp_server_bing_search/prompts/clean_website.py:
--------------------------------------------------------------------------------
```python
# Copyright (c) Microsoft. All rights reserved.
from mcp_extensions.llm.llm_types import SystemMessage, UserMessage
CLEAN_WEBSITE_SYSTEM_PROMPT = SystemMessage(
content="""You are responsible for parsing website content to leave the most important parts exactly as they were in the original website.
You will be given website content that has been processed using markdownify. \
It is your job to do a cleaning pass on the content to remove any unneeded content. \
You should remove aspects that are artifacts of the HTML to markdown conversion and do not help with the understanding of the content \
such as navigation, extraneous links, dropdowns, images, etc.
- You should preserve things like formatting of content itself.
- If the content is long, you should only include the most important 4000 tokens of text.
- If the content is documentation, an article, blog, etc you absolutely must return the content in its entirety.
- Be sure to include authors and dates. Also preserve important data, if it exists, like GitHub stars, viewership, things that hint at recency, etc.
- It is critical that the content you wish to keep, you copy WORD FOR WORD.
- Do not wrap the content in a markdown code block.
- NEVER summarize, you are only allowed to output the content verbatim."""
)
CLEAN_WEBSITE_USER_PROMPT = UserMessage(
content="""<website_content>
{{content}}
</website_content>"""
)
CLEAN_WEBSITE_MESSAGES = [
CLEAN_WEBSITE_SYSTEM_PROMPT,
CLEAN_WEBSITE_USER_PROMPT,
]
```
--------------------------------------------------------------------------------
/libraries/python/content-safety/content_safety/evaluators/evaluator.py:
--------------------------------------------------------------------------------
```python
# Copyright (c) Microsoft. All rights reserved.
import logging
from content_safety.evaluators.azure_content_safety.config import AzureContentSafetyEvaluatorConfig
from content_safety.evaluators.openai_moderations.config import OpenAIContentSafetyEvaluatorConfig
from semantic_workbench_assistant.assistant_app import (
ContentSafetyEvaluation,
ContentSafetyEvaluator,
)
from .azure_content_safety import AzureContentSafetyEvaluator
from .config import CombinedContentSafetyEvaluatorConfig
from .openai_moderations import OpenAIContentSafetyEvaluator
logger = logging.getLogger(__name__)
class CombinedContentSafetyEvaluator(ContentSafetyEvaluator):
"""
An evaluator that uses the Azure Content Safety service to evaluate content safety.
"""
def __init__(self, config: CombinedContentSafetyEvaluatorConfig) -> None:
self.config = config
async def evaluate(self, content: str | list[str]) -> ContentSafetyEvaluation:
"""
Evaluate the content for safety using the Azure Content Safety service.
"""
if isinstance(self.config.service_config, AzureContentSafetyEvaluatorConfig):
return await AzureContentSafetyEvaluator(self.config.service_config).evaluate(content)
elif isinstance(self.config.service_config, OpenAIContentSafetyEvaluatorConfig):
return await OpenAIContentSafetyEvaluator(self.config.service_config).evaluate(content)
raise ValueError("Invalid service configuration.")
```
--------------------------------------------------------------------------------
/libraries/python/skills/skill-library/skill_library/skills/fabric/patterns/explain_docs/system.md:
--------------------------------------------------------------------------------
```markdown
# IDENTITY and PURPOSE
You are an expert at capturing, understanding, and explaining the most important parts of instructions, documentation, or other formats of input that describe how to use a tool.
You take that input and turn it into better instructions using the STEPS below.
Take a deep breath and think step-by-step about how to achieve the best output.
# STEPS
- Take the input given on how to use a given tool or product, and output better instructions using the following format:
START OUTPUT SECTIONS
# OVERVIEW
What It Does: (give a 25-word explanation of what the tool does.)
Why People Use It: (give a 25-word explanation of why the tool is useful.)
# HOW TO USE IT
Most Common Syntax: (Give the most common usage syntax.)
# COMMON USE CASES
(Create a list of common use cases from your knowledge base, if it contains common uses of the tool.)
(Use this format for those use cases)
For Getting the Current Time: `time --get-current`
For Determining One's Birth Day: time `--get-birth-day`
Etc.
# MOST IMPORTANT AND USED OPTIONS AND FEATURES
(Create a list of common options and switches and flags, etc., from the docs and your knowledge base, if it contains common uses of the tool.)
(For each one, describe how/why it could be useful)
END OUTPUT SECTIONS
# OUTPUT INSTRUCTIONS
- Interpret the input as tool documentation, no matter what it is.
- You only output human readable Markdown.
- Do not output warnings or notes—just the requested sections.
# INPUT
INPUT:
```
--------------------------------------------------------------------------------
/mcp-servers/mcp-server-filesystem-edit/data/attachments/ReDoodle.txt:
--------------------------------------------------------------------------------
```
ReDoodle, is a "daily" web puzzle game where you are given a starting image, and your goal is to transform it into a goal image through a series of prompts. I made this project primarily to learn about web development. To motivate myself to complete it, I made it a goal to open source it; and here it is: <link>
Here's a rundown of the tools I used to build the app:
- Client: I used Vite with their template for React + Typescript and also Tailwind CSS to make it pretty. Development and the app itself are very fast, and all these tools all have lots of docs, great compatibility with packages, and work well with AI tooling.
- Server: I used FastAPI and a SQLite database to persist the game state. Not my finest code, but it was not my goal here - I needed something that was easy to use.
- Image to image and image generation: Originally, I used Flux and it produced beautiful images! However, I ended up going with Stable Diffusion 3.5 Turbo since I found the speed of it a better UX (demo is in real-time at 3-4 seconds per image) while still producing quality images.
- Image similarity: I used a Vision Transformer (ViT) image feature model to compute embeddings of images and cosine similarity to compare them. Lots of room for improvement here, but pretty good for an off the shelf, small model.
- Microsoft Designer for the cute web icon!
- And Microsoft Loop for keeping giving me a place to keep my to-do list, learning resources in one spot, and a great editor to draft this post.
```
--------------------------------------------------------------------------------
/mcp-servers/mcp-server-office/data/attachments/ReDoodle.txt:
--------------------------------------------------------------------------------
```
ReDoodle, is a "daily" web puzzle game where you are given a starting image, and your goal is to transform it into a goal image through a series of prompts. I made this project primarily to learn about web development. To motivate myself to complete it, I made it a goal to open source it; and here it is: <link>
Here's a rundown of the tools I used to build the app:
- Client: I used Vite with their template for React + Typescript and also Tailwind CSS to make it pretty. Development and the app itself are very fast, and all these tools all have lots of docs, great compatibility with packages, and work well with AI tooling.
- Server: I used FastAPI and a SQLite database to persist the game state. Not my finest code, but it was not my goal here - I needed something that was easy to use.
- Image to image and image generation: Originally, I used Flux and it produced beautiful images! However, I ended up going with Stable Diffusion 3.5 Turbo since I found the speed of it a better UX (demo is in real-time at 3-4 seconds per image) while still producing quality images.
- Image similarity: I used a Vision Transformer (ViT) image feature model to compute embeddings of images and cosine similarity to compare them. Lots of room for improvement here, but pretty good for an off the shelf, small model.
- Microsoft Designer for the cute web icon!
- And Microsoft Loop for keeping giving me a place to keep my to-do list, learning resources in one spot, and a great editor to draft this post.
```
--------------------------------------------------------------------------------
/libraries/python/skills/skill-library/pyproject.toml:
--------------------------------------------------------------------------------
```toml
[project]
name = "skill-library"
version = "0.1.0"
description = "MADE:Exploration Skill library"
authors = [{name="MADE:Explorers"}]
readme = "README.md"
requires-python = ">=3.11"
dependencies = [
"assistant-drive>=0.1.0",
"assistant-extensions[attachments]>=0.1.0",
"events>=0.1.0",
"openai-client>=0.1.0",
"openai>=1.16.1",
"pydantic-settings>=2.3.4",
"pydantic>=2.6.1",
"python-dotenv>=1.0.1",
"python-liquid>=1.12.1",
"requests>=2.32.0",
"tiktoken>=0.8.0",
"jsonschema>=4.23.0",
"bs4>=0.0.2",
]
[dependency-groups]
dev = [
"pyright>=1.1.389",
"pytest>=8.3.1",
"pytest-asyncio>=0.23.8",
"pytest-repeat>=0.9.3",
]
[tool.uv.sources]
assistant-drive = { path = "../../assistant-drive", editable = true }
assistant-extensions = { path = "../../assistant-extensions", editable = true }
events = { path = "../../events", editable = true }
openai-client = { path = "../../openai-client", editable = true }
[tool.setuptools.package-data]
"skill_library" = ["llm_info.txt"]
[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"
[tool.pytest.ini_options]
addopts = ["-vv"]
log_cli = true
log_cli_level = "WARNING"
log_cli_format = "%(asctime)s | %(levelname)-7s | %(name)s | %(message)s"
testpaths = ["tests"]
norecursedirs = ["skill_library/skills/guided_conversation"]
asyncio_mode = "auto"
asyncio_default_fixture_loop_scope = "function"
[project.scripts]
run-routine = "skill_library.cli.run_routine:entry_point"
```
--------------------------------------------------------------------------------
/assistants/project-assistant/assistant/utils.py:
--------------------------------------------------------------------------------
```python
"""
Utility functions for the knowledge transfer assistant module.
This module provides common utility functions used across the knowledge transfer assistant
codebase, helping to reduce code duplication and maintain consistency.
"""
import pathlib
from semantic_workbench_assistant.assistant_app import ConversationContext
from assistant.errors import NoUserException
from assistant.string_utils import render
DEFAULT_TEMPLATE_ID = "default"
def load_text_include(filename: str, replacements: dict[str, str] = {}) -> str:
"""
Helper for loading an include from a text file.
Args:
filename: The name of the text file to load from the text_includes directory
Returns:
The content of the text file
"""
directory = pathlib.Path(__file__).parent
file_path = directory / "text_includes" / filename
text = file_path.read_text()
return render(text, replacements)
async def get_current_user(
context: ConversationContext,
) -> tuple[str, str | None]:
participants = await context.get_participants()
user_id = None
user_name = None
for participant in participants.participants:
if participant.role == "user":
user_id = participant.id
user_name = participant.name
break
if not user_id:
raise NoUserException
return user_id, user_name
async def get_current_user_id(context: ConversationContext) -> str:
user_id, _ = await get_current_user(context)
return user_id
```