MR
Mayur Rathi
@github
⭐ 34.1k GitHub stars

Acreadiness-Policy

Acreadiness-Policy是一款data方向的AI技能,核心价值是Help the user pick, write, or apply an AgentRC policy,可用于解决开发者在data领域的实际问题,帮助用户提升效率、自动化重复任务或优化工作流。

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 basel

Last verified on: 2026-05-30
mkdir -p ./skills/acreadiness-policy && curl -sfL https://raw.githubusercontent.com/github/awesome-copilot/main/skills/acreadiness-policy/SKILL.md -o ./skills/acreadiness-policy/SKILL.md

Run in terminal / PowerShell. Requires curl (Unix) or PowerShell 5+ (Windows).

Skill Content

# /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


jsonc
{
  "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:

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`:

```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:

bash
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:


yaml
- 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`.

🎯 Best For

  • Claude users
  • GitHub Copilot users
  • Data professionals
  • Analytics teams
  • Researchers

💡 Use Cases

  • Data pipeline auditing
  • Query optimization

📖 How to Use This Skill

  1. 1

    Install the Skill

    Copy the install command from the Terminal tab and run it. The SKILL.md file downloads to your local skills directory.

  2. 2

    Load into Your AI Assistant

    Open Claude or GitHub Copilot and reference the skill. Paste the SKILL.md content or use the system prompt tab.

  3. 3

    Apply Acreadiness-Policy to Your Work

    Provide context for your task — paste source material, describe your audience, or share existing work to guide the AI.

  4. 4

    Review and Refine

    Edit the AI output for accuracy, tone, and completeness. Add human insight where the AI lacks context.

❓ Frequently Asked Questions

How do I install Acreadiness-Policy?

Copy the install command from the Terminal tab and run it. The skill downloads to ./skills/acreadiness-policy/SKILL.md, ready to use.

Can I customize this skill for my team?

Absolutely. Edit the SKILL.md file to add team-specific instructions, examples, or workflows.

⚠️ Common Mistakes to Avoid

Ignoring data quality

AI analysis inherits all data quality issues — profile your data first.

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