GitHub Spec Kit is a comprehensive toolkit for implementing Spec-Driven Development (SDD) - a methodology that emphasizes creating clear specifications before implementation. The toolkit includes templates, scripts, and workflows that guide development teams through a structured approach to building software. Specify CLI is the command-line interface that bootstraps projects with the Spec Kit framework. It sets up the necessary directory structures, templates, and AI agent integrations to support the Spec-Driven Development workflow. The toolkit supports multiple AI coding assistants, allowing…
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Install Claude Code before setup. The integration key is claude. This integration is declared multi-install safe. See the shared multi-install rules. For an existing Spec Kit project, use specify integration install claude. See integration management for activation, upgrades, and removal. Skills are installed as .claude/skills/speckit-<command>/SKILL.md. Invoke them in Claude's chat, for example /speckit-specify or /speckit-bug-assess for the installed bug extension. By default every Spec Kit skill installed for Claude Code is model-invocable, so its description is always in Claude's context…
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Add a community extension to the Spec Kit catalog from a GitHub issue submission. USE FOR: processing extension submission issues, validating catalog entries, updating catalog.community.json and docs/community/extensions.md, creating PRs. DO NOT USE FOR: creating new extensions from scratch, or first-party extension work.
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Reviews Spec Kit code changes for consistency with applicable design documents, positive and negative test coverage, regression evidence for bug fixes, and consistent repository terminology. Use when reviewing a diff or pull request. Do not use for implementing changes or posting GitHub review actions.
The Awesome GitHub Copilot repository is a community-driven collection of custom agents and instructions designed to enhance GitHub Copilot experiences across various domains, languages, and use cases. The project includes: All agent files (*.agent.md) and instruction files (*.instructions.md) must include proper markdown front matter. Agent Skills are folders containing a SKILL.md file with frontmatter and optional bundled assets. Hooks are folders containing a README.md with frontmatter and a hooks.json configuration file: Frontmatter must be valid YAML and meet the metadata…
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The following instructions are only to be applied when performing a code review. Use .github/skills/code-review/SKILL.md for repository-fit, differentiation, provenance, validation, and editorial-quality review criteria. Apply the deterministic file-format checks below as well. Frontmatter must be valid YAML and satisfy the required fields, types, lengths, and naming rules. Accept any valid YAML scalar style (plain, single-quoted, double-quoted, literal, or folded); do not flag a value solely because it is not single-quoted. Only apply to files that end in .prompt.md Only apply…
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Create or register a canvas extension in the awesome-copilot repository. Use when asked to scaffold a new canvas extension, create its plugin.json, add a reusable extension to one or more plugins, or migrate extension metadata. Extensions are reusable source under extensions/; shippable plugin manifests belong under plugins/.
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Use this skill when the user explicitly asks to map, document, or onboard into an existing codebase. Trigger for prompts like "map this codebase", "document this architecture", "onboard me to this repo", or "create codebase docs". Do not trigger for routine feature implementation, bug fixes, or narrow code edits unless the user asks for repository-level discovery.
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Run the AgentRC readiness assessment on the current repository and produce a static HTML dashboard at reports/index.html. Wraps `npx github:microsoft/agentrc readiness` and hands off rendering to the @ai-readiness-reporter custom agent. Supports policies (--policy) for org-specific scoring. Use when asked to assess, audit, or score the AI readiness of a repo.
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Generate tailored AI agent instruction files via AgentRC instructions command. Produces .github/copilot-instructions.md (default, recommended for Copilot in VS Code) plus optional per-area .instructions.md files with applyTo globs for monorepos. Use after running /acreadiness-assess to close gaps in the AI Tooling pillar.
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Help the user pick, write, or apply an AgentRC policy. Policies customise readiness scoring by disabling irrelevant checks, overriding impact/level, setting pass-rate thresholds, or chaining org baselines with team overrides. Use when the user asks about strict mode, AI-only scoring, custom weights, CI gating, or wants org-wide standardisation.
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Use this skill when the user shares ad campaign performance data and asks what to cut, scale, or test. Trigger for prompts like "analyze my ad campaigns", "where am I wasting ad spend", "reallocate my ad budget", "which ads are actually working", or "ROAS analysis". Do not trigger for campaign planning or creative generation without performance data.
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Build, debug, package, and document Adobe Illustrator integrations: CEP extension panels, C++ SDK plug-ins (.aip), the coming UXP plugins, MCP servers that drive Illustrator, computer-use automation, and OS scripts (batch, PowerShell, bash, AppleScript) that run ExtendScript in Illustrator. Use when creating or fixing an Illustrator extension or plug-in, choosing between a script, extension, or plug-in, connecting an AI tool to Illustrator, automating Illustrator from the command line, or writing install and usage docs for an Illustrator tool someone will publish. Pairs with adobe-illustrator-scripting for the ExtendScript DOM.
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Write, debug, and optimize Adobe Illustrator automation scripts using ExtendScript (JavaScript/JSX). Use when creating or modifying scripts that manipulate documents, layers, paths, text frames, colors, symbols, artboards, or any Illustrator DOM objects. Covers the complete JavaScript object model, coordinate system, measurement units, export workflows, and scripting best practices.
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Design AI agent architectures through requirements discovery, or audit and diagnose architectural flaws in existing agents. Architecture only; excludes implementation and general code review.
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Patterns and techniques for adding governance, safety, and trust controls to AI agent systems. Use this skill when: - Building AI agents that call external tools (APIs, databases, file systems) - Implementing policy-based access controls for agent tool usage - Adding semantic intent classification to detect dangerous prompts - Creating trust scoring systems for multi-agent workflows - Building audit trails for agent actions and decisions - Enforcing rate limits, content filters, or tool restrictions on agents - Working with any agent framework (PydanticAI, CrewAI, OpenAI Agents, LangChain, AutoGen)
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Patterns and techniques for evaluating and improving AI agent outputs. Use this skill when: - Implementing self-critique and reflection loops - Building evaluator-optimizer pipelines for quality-critical generation - Creating test-driven code refinement workflows - Designing rubric-based or LLM-as-judge evaluation systems - Adding iterative improvement to agent outputs (code, reports, analysis) - Measuring and improving agent response quality
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Plain text files in a repository that tell a coding agent how the project works: commands to run, conventions to follow and things to avoid. CLAUDE.md, AGENTS.md, cursor rules and skills are the common kinds.
CLAUDE.md or AGENTS.md?
CLAUDE.md is read by Claude Code. AGENTS.md is an open format that Codex, Cursor and other agents read. Many projects keep one and point the other at it.
What is a skill?
A folder with a SKILL.md that describes one capability, such as filling PDFs or reviewing code. The agent loads it only when the task calls for it.
Can I search my own team's files too?
Your agents already can, over MCP, limited to the files you're allowed to read. Searching them from this page is coming.
GitHubAGENTS.md