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Acreadiness Assess

GitHubOfficialFirst committed by mvanderbend-msoftDistributed by GitHub

  • Skill
  • Content generator
  • Software development
  • MIT
  • Low risk
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From the author

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.
— the description by mvanderbend-msoft

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

The acreadiness-assess skill evaluates a repository's AI readiness by running a structured scan and producing a comprehensive, static HTML dashboard. It functions as the measurement component of the AgentRC framework, providing a detailed breakdown of repository health and AI configuration across nine specific pillars. The skill interprets scan results to offer a prioritized remediation plan, maturity scoring, and actionable recommendations, delivering a report that can be viewed locally or committed to version control.

What it does

  • Executes an AI readiness scan on the current repository.
  • Generates a self-contained HTML dashboard at reports/index.html.
  • Provides a prioritized remediation plan categorized by urgency.
  • Evaluates repository health and AI setup across nine distinct pillars.
  • Supports custom organizational policies for tailored scoring.
  • Summarizes key maturity metrics and top-priority actions directly in the chat.

When to use it

  • When you need to audit a repository for AI readiness.
  • When you want to score a codebase against specific AI-readiness criteria.
  • When you need a visual report to share findings on repository health.
  • When you are preparing a repository for AI agent integration.

Advantages

  • Produces a portable, single-file HTML report that works without a server.
  • Provides clear, actionable recommendations for improving AI readiness.
  • Allows for policy-based customization to match organizational standards.
  • Integrates seamlessly into the AgentRC workflow for measurement and reporting.

Usage examples

“Assess the AI readiness of this repository.”
The agent runs the readiness scan, generates the HTML dashboard, and provides a summary of the maturity score and top remediation tasks.
“Run a readiness audit using the strict policy.”
The agent executes the scan with the specified policy file and produces a report highlighting areas that fail to meet strict criteria.

Install

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/github/acreadiness-assess/download

  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip acreadiness-assess.zip -d .agents/skills

Documentation by mvanderbend-msoft

/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.

Files

LICENSE

MIT License

Copyright GitHub, Inc.

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

Requirements and compatibility

Written for: GitHub Copilot, Claude Code

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Claude Code frontmatter, Needs Node.js

Security

What our scanner noticed· Low risk

  • Instructions that use the network

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

First committed by
GitHub avatar of mvanderbend-msoftmvanderbend-msoft
Publisher
GitHub (Official)
Distributed by
GitHub
Source
github/awesome-copilot · skills/acreadiness-assess · commit 797337f
Licence
MIT License (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
Synced
Contributors
mvanderbend-msoft

AutoGineer is not affiliated with or endorsed by mvanderbend-msoft or GitHub.

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download acreadiness-assess.zip