Acreadiness Assess
GitHubOfficialFirst committed by mvanderbend-msoftDistributed by GitHub
- Skill
- Content generator
- Software development
- MIT
- Low risk
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
Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/github/acreadiness-assess/downloadUnzip 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
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.jsonfor apoliciesarray.If neither, run with no policy (built-in defaults).
For a primer on policies, suggest the
acreadiness-policyskill.
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-reportercustom agent to interpret the JSON and producereports/index.html. The agent renders via the bundled templatereport-template.html(shipped alongside this skill) so every report has an identical look & feel. The agent:Reads the bundled
report-template.htmland 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 theacreadiness-generate-instructionsskill).
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
- ATTRIBUTION.mdAdded by AutoGineer887 B
- LICENSELicence file added by AutoGineer1.0 KiB
- SKILL.md3.3 KiB
- report-template.html11.6 KiB
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
mvanderbend-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