copilot-thought-logging
See process Copilot is following where you can edit this to reshape the interaction or save when follow up may be needed Triggers on: **
Best use case
copilot-thought-logging is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
See process Copilot is following where you can edit this to reshape the interaction or save when follow up may be needed Triggers on: **
Teams using copilot-thought-logging should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/copilot-thought-logging/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How copilot-thought-logging Compares
| Feature / Agent | copilot-thought-logging | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
See process Copilot is following where you can edit this to reshape the interaction or save when follow up may be needed Triggers on: **
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Copilot Process tracking Instructions **ABSOLUTE MANDATORY RULES:** - You must review these instructions in full before executing any steps to understand the full instructions guidelines. - You must follow these instructions exactly as specified without deviation. - Do not keep repeating status updates while processing or explanations unless explicitly required. This is bad and will flood Copilot session context. - NO phase announcements (no "# Phase X" headers in output) - Phases must be executed one at a time and in the exact order specified. - NO combining of phases in one response - NO skipping of phases - NO verbose explanations or commentary - Only output the exact text specified in phase instructions # Phase 1: Initialization - Create file `\Copilot-Processing.md` in workspace root - Populate `\Copilot-Processing.md` with user request details - Work silently without announcements until complete. - When this phase is complete keep mental note of this that <Phase 1> is done and does not need to be repeated. # Phase 2: Planning - Generate an action plan into the `\Copilot-Processing.md` file. - Generate detailed and granular task specific action items to be used for tracking each action plan item with todo/complete status in the file `\Copilot-Processing.md`. - This should include: - Specific tasks for each action item in the action plan as a phase. - Clear descriptions of what needs to be done - Any dependencies or prerequisites for each task - Ensure tasks are granular enough to be executed one at a time - Work silently without announcements until complete. - When this phase is complete keep mental note of this that <Phase 2> is done and does not need to be repeated. # Phase 3: Execution - Execute action items from the action plan in logical groupings/phases - Work silently without announcements until complete. - Update file `\Copilot-Processing.md` and mark the action item(s) as complete in the tracking. - When a phase is complete keep mental note of this that the specific phase from `\Copilot-Processing.md` is done and does not need to be repeated. - Repeat this pattern until all action items are complete # Phase 4: Summary - Add summary to `\Copilot-Processing.md` - Work silently without announcements until complete. - Execute only when ALL actions complete - Inform user: "Added final summary to `\Copilot-Processing.md`." - Remind user to review the summary and confirm completion of the process then to remove the file when done so it is not added to the repository. **ENFORCEMENT RULES:** - NEVER write "# Phase X" headers in responses - NEVER repeat the word "Phase" in output unless explicitly required - NEVER provide explanations beyond the exact text specified - NEVER combine multiple phases in one response - NEVER continue past current phase without user input - If you catch yourself being verbose, STOP and provide only required output - If you catch yourself about to skip a phase, STOP and go back to the correct phase - If you catch yourself combining phases, STOP and perform only the current phase
Related Skills
copilot-sdk
Build agentic applications with GitHub Copilot SDK. Use when embedding AI agents in apps, creating custom tools, implementing streaming responses, managing sessions, connecting to MCP servers, or creating custom agents. Triggers on Copilot SDK, GitHub SDK, agentic app, embed Copilot, programmable agent, MCP server, custom agent. **PROACTIVE ACTIVATION**: Auto-invoke when building agentic applications or integrating Copilot SDK. **DETECTION**: Check for @github/copilot-sdk imports, copilot dependencies in package.json/pyproject.toml/go.mod. **USE CASES**: Embedding agents in apps, creating custom tools, implementing streaming, managing sessions, connecting to MCP servers.
awesome-copilot-root-typespec-create-agent
Generate a complete TypeSpec declarative agent with instructions, capabilities, and conversation starters for Microsoft 365 Copilot Use when: the task directly matches typespec create agent responsibilities within plugin awesome-copilot-root. Do not use when: a more specific framework or task-focused skill is clearly a better match.
awesome-copilot-root-mcp-m365-agent-expert
Expert assistant for building MCP-based declarative agents for Microsoft 365 Copilot with Model Context Protocol integration Use when: the task directly matches mcp m365 agent expert responsibilities within plugin awesome-copilot-root. Do not use when: a more specific framework or task-focused skill is clearly a better match.
awesome-copilot-root-mcp-create-declarative-agent
Skill converted from mcp-create-declarative-agent.prompt.md Use when: the task directly matches mcp create declarative agent responsibilities within plugin awesome-copilot-root. Do not use when: a more specific framework or task-focused skill is clearly a better match.
awesome-copilot-root-agent-governance
Use when: the task directly matches agent governance responsibilities within plugin awesome-copilot-root. Do not use when: a more specific framework or task-focused skill is clearly a better match.
github-copilot-cli-expert
Expert knowledge of GitHub Copilot CLI - installation, configuration, usage, custom agents, MCP servers, and version management. Use when asking about copilot cli, copilot commands, installing copilot, updating copilot, copilot features.
email-copilot
Manages inbox by learning preferences, cleaning noise, and prioritizing important work. Use when user asks about emails or inbox management.
vscode-copilot-instructions
Expert guidance for creating VSCode Copilot custom instructions, prompt files, and AGENTS.md files. Use when user wants to create, modify, or understand .github/copilot-instructions.md, .instructions.md, AGENTS.md, or .prompt.md files. Includes YAML frontmatter structure, variable syntax, tool references, and best practices for each file type.
github-copilot
AI-powered coding assistant providing inline suggestions, chat interface, code review, and autonomous coding agent across IDEs and GitHub.com
github-copilot-sdk
Comprehensive knowledge of GitHub Copilot SDK for embedding Copilot's agentic workflows in Python, TypeScript, Go, and .NET applications. Auto-activates for Copilot SDK integration, CopilotClient usage, session management, streaming responses, custom tools, and MCP server connections.
github-copilot-agent-tips-and-tricks
Tips and Tricks for Working with GitHub Copilot Agent PRs
copilotkit-pitch-deck
Production-ready CopilotKit pitch deck wizard in main application. Use when enhancing AI conversation features, optimizing Edge Function integration, debugging chat interface, or improving pitch deck generation flow. System is PRODUCTION READY (98/100).