Acreadiness-Policy
Acreadiness-Policy is an data AI skill with a core value of Help the user pick, write, or apply an AgentRC policy. It
helps developers solve real-world problems in the data domain, boosting
efficiency, automating repetitive tasks, and optimizing workflows.
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
Quick Facts
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.