mcpbeat

Acreadiness Policy

github/acreadiness-policy

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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on the repository, not the skill itself

Install

one command, takes just this skill from the repository
npx skills add https://github.com/github/awesome-copilot --skill acreadiness-policy

The instruction itself

11 sections, as written by the author

/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

{
  "name": "my-policy",
  "criteria": {
    "disable":  ["env-example", "observability", "dependabot"],
    "override": {
      "readme":      { "impact": "high", "level": 2 },
      "lint-config": { "title": "Linter required" }
    }
  },
  "extras": {
    "disable": ["pre-commit"]
  },
  "thresholds": {
    "passRate": 0.9
  }
}

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 < 0.6.

Sub-commands

show

List policies currently in effect (from agentrc.config.json policies array, or none).

new <name>

Scaffold policies/<name>.json with sensible defaults. Walk the user through:

  • What to disable — irrelevant pillars or extras for their stack (e.g. disable observability for a static site).
  • What to raise — override impact to high or critical for must-haves (e.g. readme, codeowners).
  • Pass-rate threshold — typical org baselines: 0.7 (lenient), 0.85 (standard), 1.0 (strict).
  • Reference the policy from agentrc.config.json:
   { "policies": ["./policies/<name>.json"] }

apply <path-or-pkg>

Run agentrc readiness --json --policy <source> and re-render the report by handing off to the assess skill / ai-readiness-reporter agent. Supports chaining:

npx -y github:microsoft/agentrc readiness --json --policy ./org-baseline.json,./team-frontend.json

CI gating

Combine policies with --fail-level to enforce a minimum maturity level in CI:

- run: npx -y github:microsoft/agentrc readiness --policy ./policies/strict.json --fail-level 3

Advanced

JSON policies can disable, override, and set thresholds — but cannot add new criteria. For new detection logic, point users at AgentRC's TypeScript plugin system (docs/dev/plugins.md).

Operating rules

  • Never silently disable a pillar. If the user wants to disable observability, confirm and explain the trade-off.
  • Prefer overriding impact over disabling. Disabling hides the gap entirely; overriding lets it still appear in the report.
  • Recommend extras stay enabled. They cost nothing — they don't affect the score.
  • Suggest layering — most orgs want a baseline policy + per-team overrides chained with --policy a.json,b.json.

How to use it

Copy the folder

Take github/acreadiness-policy from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.

Install what it needs

The instructions reference npx. Without those the skill loads but fails at the first command.