awesome-copilot/plugins/acreadiness-cockpit/skills/acreadiness-assess/SKILL.md

name, description, argument-hint

name	description	argument-hint
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.	[--policy <path-or-pkg>] [--per-area] — e.g. /acreadiness-assess, /acreadiness-assess --policy ./policies/strict.json

/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:

   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.