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Acreadiness Policy

GitHubOfficialFirst committed by mvanderbend-msoftDistributed by GitHub

  • Skill
  • Workflow
  • Software development
  • MIT
  • Low risk

From the author

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.
— the description by mvanderbend-msoft

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

The acreadiness-policy skill enables users to define, manage, and apply custom policies that dictate how AgentRC evaluates project readiness. By creating JSON-based policy files, users can tailor scoring by disabling specific checks, overriding impact levels for criteria, and setting required pass-rate thresholds. This skill supports organizational standardization by allowing teams to chain baseline policies with team-specific overrides, making it ideal for enforcing quality standards in CI/CD pipelines.

What it does

  • Scaffolds new readiness policy files with sensible defaults.
  • Allows disabling of specific readiness checks or extra features.
  • Supports overriding impact weights and levels for specific criteria.
  • Defines pass-rate thresholds for automated scoring.
  • Facilitates chaining multiple policies for layered organizational requirements.
  • Integrates with CI workflows to enforce maturity levels via gating.

When to use it

  • When you need to standardize readiness scoring across an organization.
  • When you want to enforce strict quality gates in CI/CD pipelines.
  • When specific readiness checks are irrelevant to your project stack.
  • When you need to prioritize certain criteria by increasing their impact weight.
  • When you want to transition from default scoring to a custom, team-specific baseline.

Advantages

  • Provides granular control over how project maturity is measured.
  • Supports layered policy application for flexible team-level overrides.
  • Enables automated enforcement of quality standards in CI environments.
  • Reduces noise by allowing the exclusion of irrelevant checks.

Usage examples

“/acreadiness-policy new strict-frontend”
The agent scaffolds a new policy file named strict-frontend.json, prompting the user to define specific criteria, impact overrides, and pass-rate thresholds.
“/acreadiness-policy apply ./policies/org-baseline.json,./policies/team-frontend.json”
The agent executes the readiness assessment using the chained policy files, applying the organizational baseline followed by the team-specific overrides.

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/github/acreadiness-policy/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip acreadiness-policy.zip -d .agents/skills

Documentation by mvanderbend-msoft

/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/:

PolicyWhat it does
strict.json100% pass rate, raises impact on key criteria
ai-only.jsonDisables all repo-health checks, focuses on AI tooling
repo-health-only.jsonDisables 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
ImpactWeight
critical5
high4
medium3
low2
info0

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:

  1. What to disable — irrelevant pillars or extras for their stack (e.g. disable observability for a static site).

  2. What to raise — override impact to high or critical for must-haves (e.g. readme, codeowners).

  3. Pass-rate threshold — typical org baselines: 0.7 (lenient), 0.85 (standard), 1.0 (strict).

  4. 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.

Files

Requirements and compatibility

Written for: GitHub Copilot, Claude Code

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Claude Code frontmatter, Needs Node.js

Security

What our scanner noticed· Low risk

  • Instructions that use the network

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

First committed by
GitHub avatar of mvanderbend-msoftmvanderbend-msoft
Publisher
GitHub (Official)
Distributed by
GitHub
Source
github/awesome-copilot · skills/acreadiness-policy · commit 797337f
Licence
MIT License (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
Synced
Contributors
mvanderbend-msoft

AutoGineer is not affiliated with or endorsed by mvanderbend-msoft or GitHub.

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download acreadiness-policy.zip