MR
Mayur Rathi
@github
⭐ 34.1k GitHub stars

Acreadiness-Assess

Acreadiness-Assess是一款data方向的AI技能,核心价值是Run the AgentRC readiness assessment on the current repository and produce a static HTML dashboard at reports/index,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。

Run the AgentRC readiness assessment on the current repository and produce a static HTML dashboard at reports/index.html. Wraps `npx github:microsoft/agentrc readiness` and hands off rendering to the

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

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

Skill Content

# /acreadiness-assess — AI-readiness assessment


Use this skill whenever the user asks for an **AI-readiness assessment**, a **readiness check**, an **audit**, or wants to **see how AI-ready** their repository is.


This skill is the *Measure* step in AgentRC's **Measure → Generate → Maintain** loop. The result is a self-contained HTML dashboard the user can open with `file://` or commit to the repo.


Steps


1. **Confirm prerequisites.** Node 20+ must be on PATH. If unsure, run `node --version`.


2. **Decide on a policy** (optional but encouraged):

- If the user provided `--policy <source>`, capture it.

- Otherwise check `agentrc.config.json` for a `policies` array.

- If neither, run with no policy (built-in defaults).

- For a primer on policies, suggest the `acreadiness-policy` skill.


3. **Run the readiness scan** in the repo root with structured output:

```bash

npx -y github:microsoft/agentrc readiness --json [--policy <source>] [--per-area]

```

The `CommandResult<T>` JSON envelope is your input for the next step.


4. **Hand off to the `ai-readiness-reporter` custom agent** to interpret the JSON and produce `reports/index.html`. The agent renders via the bundled template `report-template.html` (shipped alongside this skill) so every report has an identical look & feel. The agent:

- Reads the bundled `report-template.html` and substitutes placeholders with real data.

- Inlines all CSS, ships a single static file (works under `file://`).

- Renders maturity level, overall score, grade, pass-rate vs threshold.

- Breaks down all 9 pillars across **Repo Health** (8) and **AI Setup** (1) with *what it measures*, *why it matters for AI*, *current state*, and *a specific recommendation*.

- Tags every pillar with an **AI relevance** badge (High / Medium / Low).

- Surfaces **Extras** separately (they never affect the score).

- Shows the **Active Policy** including any disabled/overridden criteria and thresholds.

- Produces a **Prioritised Remediation Plan** (🔴 Fix First / 🟡 Fix Next / 🔵 Plan).

- Embeds the raw AgentRC JSON for reuse.


5. **Tell the user where the report lives** (`reports/index.html`) and how to open it. Summarise in chat: maturity level, overall score, top three lowest pillars, and the single highest-leverage next action (almost always: run the `acreadiness-generate-instructions` skill).


Notes


- AgentRC also has a built-in HTML renderer (`--visual` / `--output report.html`) but its output is intentionally generic. This skill produces a tailored, opinionated dashboard via the custom agent — closer to a code review than a metrics dump.

- For CI gating, recommend `agentrc readiness --fail-level <n>` (1–5).

- The skill never modifies repository files other than creating `reports/index.html`.

🎯 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-Assess 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-Assess?

Copy the install command from the Terminal tab and run it. The skill downloads to ./skills/acreadiness-assess/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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