Acreadiness Assess Agent Skill

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.

4k tokens

context cost

the whole folder, loaded on every use

files

instructions only

copies elsewhere

how many repositories repackaged it

37394

stars on the repo

on the repository, not the skill itself

Install

one command, takes just this skill from the repository

npx skills add https://github.com/github/awesome-copilot --skill acreadiness-assess

What comes with it

11 839 bytes besides the instruction

report-template.html

The instruction itself

3 sections, as written by the author

/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.

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How to use it

Copy the folder

Take github/acreadiness-assess from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.

Install what it needs

The instructions reference npx. Without those the skill loads but fails at the first command.