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Skill

acreadiness-policy

@github Updated 2026-08-01

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.

agent-skillsagentsaiawesomecustom-agentsgithub-copilothacktoberfestprompt-engineering

Install

With your agent

Install OMGSkills skill "github/awesome-copilot:acreadiness-policy" into this agent's normal skills directory: call the OMGSkills MCP get_install_plan tool for your agent and run the script it returns (it installs only the pinned commit and verifies SKILL.md), and stop if MCP or the pin is unavailable. Do not overwrite or change existing skills without asking me.

Manual command

git clone https://github.com/github/awesome-copilot /tmp/awesome-copilot && ln -s /tmp/awesome-copilot/skills/acreadiness-policy ~/.claude/skills/acreadiness-policy

From README

/acreadiness-policy — AgentRC policies Use this skill when the user asks about policies, strict mode, custom scoring, disabling checks, org standards, or CI gating of readiness. A policy is a small JSON file with three optional sections — criteria, extras, thresholds — that customise how AgentRC scores readiness. Built-in examples AgentRC ships with three example policies in examples/policies/: | Policy | What it does | |---|---| | strict.json | 100% pass rate, raises impact on key criteria | | ai-only.json | Disables all repo-health checks, focuses on AI tooling | | repo-health-only.json | Disables AI checks, focuses on traditional quality | Recommend these as starting points before writing a custom policy. Policy schema Impact weights | Impact | Weight | |---|---| | critical | 5 | | high | 4 | | medium | 3 | | low | 2 | | info | 0 | Score = 1 − (deductions / max possible weight). Grades: A ≥ 0.9, B ≥ 0.8, C ≥ 0.7, D ≥ 0.6, F Scaffold policies/ .json with sensible defaults.