Agent skill · software engineering · github
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.
Why this skill is useful
Adds a custom HTML report generation process for assessing AI readiness that includes specific metrics and recommendations not typically found in standard documentation.
What it needs
Requires node installed locally. About 2k tokens when loaded. Last updated 2026-08-07. 37,534 stars on the source repository.
What this skill does
/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: 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. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills github/acreadiness-assess