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/.
agent-skill-stack
agent-skill-stack is an open-source integrations skill for Claude Code and compatible agents, published by github. Its author describes it as: “Find, evaluate, and assemble the smallest compatible set of AI Agent Skills for an end-to-end natural-language goal. Use when a user wants Skills for a multi-step workflow, asks which Skills fit a project, needs an in…”. The project has 38k stars on GitHub and is available under the MIT license. Add it to your setup with `git clone https://github.com/github/awesome-copilot ~/.claude/skills/agent-skill-stack`.
What agent-skill-stack does
Build the smallest useful stack for the user's actual outcome. Never force a domain example or a fixed lifecycle onto a different request.
Installation
Add agent-skill-stack to your agent with:
git clone https://github.com/github/awesome-copilot ~/.claude/skills/agent-skill-stack Always review a skill's source before installing it. This command comes from the skill's public repository; the linked repo is the source of truth for exact setup steps.
What's inside
The SKILL.md for agent-skill-stack is organised into these sections:
- 1. Choose the user-facing depth
- 2. Derive the workflow dynamically
- 3. Search the local index first
- 4. Map capabilities, including indirect helpers
- 5. Search with four lenses
- 6. Verify and rank candidates
- 7. Analyze conflicts and scope
- 8. Present recommendations in plain language
- 9. Install only after consent
- 10. Run a recall check
When to use it
Reach for agent-skill-stack when you want integrations help from your agent without writing the same instructions every session. Load the skill and the agent picks it up automatically for relevant tasks.
Strengths
- Clear MIT license — safe to read and adapt
- Ships in github/awesome-copilot, an established project with 37,982 GitHub stars
- Actively maintained (recent commits)
Topics
Frequently asked questions
- What does agent-skill-stack do?
- Find, evaluate, and assemble the smallest compatible set of AI Agent Skills for an end-to-end natural-language goal. Use when a user wants Skills for a multi-step workflow, asks which Skills fit a project, needs an installed-Skill audit or conflict check, has low Skill recall, wants indirect helpers such as humanizers or compliance checks, or wants a project-specific Skill Stack with controlled installation. Search local Skills, registries, GitHub, and OpenCLI; compare adoption, verified fit, safety, and overlap. Do not use for locating one known or common Skill; use the generic find-skills workflow.
- How do I install agent-skill-stack?
- Run git clone https://github.com/github/awesome-copilot ~/.claude/skills/agent-skill-stack in your agent, then reload your skills. Review the source at https://github.com/github/awesome-copilot before installing.
- Is agent-skill-stack free to use?
- Yes. agent-skill-stack is free and open source under the MIT license, so you can read, run, and adapt it within that license's terms.
- Where does agent-skill-stack come from?
- agent-skill-stack ships inside github/awesome-copilot, a repository that contains 41 catalogued skills in total. The repository's 37,982 GitHub stars apply to that whole collection, not to this skill on its own.
Related skills
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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.
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.
Add educational comments to the file specified, or prompt asking for file to comment if one is not provided.