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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

LICENSE

MIT License

Copyright GitHub, Inc.

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

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