How do I install the Githubcopilot Acreadiness Assess skill?

Run npx skillmds@latest add pyfagorass/githubcopilot-acreadiness-assess in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Githubcopilot Acreadiness Assess safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Githubcopilot Acreadiness Assess?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Githubcopilot Acreadiness Assess free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Githubcopilot Acreadiness Assess?

pyfagorass (@pyfagorass) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Githubcopilot Acreadiness Assess by pyfagorass

Name: Githubcopilot Acreadiness Assess
Author: pyfagorass

Githubcopilot Acreadiness Assess

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 @ai-readiness-reporter custom agent. Supports policies (--policy) for org-specific scoring. Use when asked to assess, audit, or score the AI readiness of a repo.

pyfagorass dd2feea 2 files · 14.7 KB Updated Sep 17, 2026

File contents

/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

Confirm prerequisites. Node 20+ must be on PATH. If unsure, run node --version.
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.
Run the readiness scan in the repo root with structured output:
```
npx -y github:microsoft/agentrc readiness --json [--policy <source>] [--per-area]
```
The CommandResult<T> JSON envelope is your input for the next step.
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.
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.

---name: githubcopilot-acreadiness-assessdescription: 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 @ai-readiness-reporter custom agent. Supports policies (--policy) for org-specific scoring. Use when asked to assess, audit, or score the AI readiness of a repo.---# /acreadiness-assess — AI-readiness assessmentUse 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.## Steps1. **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`.

Frequently asked questions