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/.
acreadiness-policy
acreadiness-policy is an open-source integrations skill for Claude Code and compatible agents, published by github. Its author describes it as: “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 ov…”. 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/acreadiness-policy`.
What acreadiness-policy does
Use this skill when the user asks about **policies**, **strict mode**, **custom scoring**, **disabling checks**, **org standards**, or **CI gating** of readiness.
Installation
Add acreadiness-policy to your agent with:
git clone https://github.com/github/awesome-copilot ~/.claude/skills/acreadiness-policyAlways 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 acreadiness-policy is organised into these sections:
- Built-in examples
- Policy schema
- Impact weights
- Sub-commands
- show
- new <name>
- apply <path-or-pkg>
- CI gating
- Advanced
- Operating rules
When to use it
Reach for acreadiness-policy 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 acreadiness-policy do?
- 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.
- How do I install acreadiness-policy?
- Run git clone https://github.com/github/awesome-copilot ~/.claude/skills/acreadiness-policy in your agent, then reload your skills. Review the source at https://github.com/github/awesome-copilot before installing.
- Is acreadiness-policy free to use?
- Yes. acreadiness-policy is free and open source under the MIT license, so you can read, run, and adapt it within that license's terms.
- Where does acreadiness-policy come from?
- acreadiness-policy 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
More Integrations →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.
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.
Add educational comments to the file specified, or prompt asking for file to comment if one is not provided.
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.