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
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
{
"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:
npx -y github:microsoft/agentrc readiness --json --policy ./org-baseline.json,./team-frontend.jsonCI 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 3Advanced
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
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
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
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
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.