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AgentRC Policy Management

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Customize your AgentRC readiness scoring with tailored policies.

by github37.7k stars on github/awesome-copilot
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Updated Aug 10, 2026
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Free · Opens the source repo

What AgentRC Policy Management does

The AgentRC Policy Management skill allows users to create, modify, and apply policies that influence how readiness is scored in their projects. By utilizing small JSON files, users can define specific criteria, thresholds, and additional settings that cater to their organizational needs. This skill is particularly useful for teams looking to enforce standards, streamline their CI processes, and ensure that the readiness assessments align with their unique operational requirements.

With this skill, users can start by exploring built-in example policies like strict.json, which enforces a 100% pass rate, or ai-only.json, which focuses exclusively on AI tooling by disabling traditional checks. The skill supports the creation of new policies through a guided process, helping users decide what checks to disable, which criteria to override, and the appropriate pass-rate thresholds for their organization. This flexibility allows teams to tailor their readiness assessments to better reflect their priorities and workflows.

Additionally, the skill supports CI gating, enabling users to enforce specific maturity levels during continuous integration processes. By chaining multiple policies, teams can create layered approaches that combine organization-wide baselines with team-specific overrides, ensuring that all aspects of their readiness are comprehensively evaluated. This approach not only enhances the quality of the codebase but also aligns with broader organizational goals.

The AgentRC Policy Management skill is designed for developers and teams who need to customize their readiness scoring mechanisms. It is particularly beneficial for organizations that require strict adherence to quality standards or wish to implement a more flexible scoring system that reflects their unique project requirements.

When to use it

Use this skill when you need to customize readiness assessments for your projects, particularly in CI environments or when enforcing organizational standards.

When not to use it

This skill is not suitable for users who require simple, out-of-the-box readiness checks without customization or for those who do not use AgentRC.

What you can build with it

Customizing Readiness for CI

Use the skill to create a policy that enforces strict readiness checks during continuous integration, ensuring high code quality.

Tailoring Policies for Team Needs

Develop a custom policy that disables irrelevant checks for a specific team, allowing them to focus on their unique requirements.

Implementing Organization-Wide Standards

Create a baseline policy that applies to all projects, then layer team-specific overrides to accommodate different workflows.

How to install AgentRC Policy Management

View source

1. Install with the skills CLI

npx skills add github/awesome-copilot/acreadiness-policy --agent claude-code

2. Or install it manually

Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.

Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs

Inside SKILL.md

Written by github

/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.

Frequently asked questions about AgentRC Policy Management

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