Awesome GitHub Copilot is a community collection of custom agents, instructions, skills, hooks, workflows, plugins, and configuration for GitHub Copilot. It helps Copilot users customize coding and development tasks. Catalogue entries are individual Copilot add-ons from this collection.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/github/awesome-copilotWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/github/awesome-copilot/ai-readiness-reporter)<a href="https://agentmods.dev/agents/github/awesome-copilot/ai-readiness-reporter"><img src="https://agentmods.dev/badge/agents/github/awesome-copilot/ai-readiness-reporter/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/github/awesome-copilot/ai-readiness-reporter"><img src="https://agentmods.dev/badge/agents/github/awesome-copilot/ai-readiness-reporter.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00080 | $0.03603 |
| Opus 5.5 | $0.00032 | $0.01441 |
| Sonnet 5.5 | $0.00016 | $0.00721 |
| Haiku 4.5 | $0.00008 | $0.00360 |
Grade A, and why
ai-readiness-reporter scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
2 near-identical copies found in the catalogue:
- ai-readiness-reporter — 100% identical, 0 lines differ
- ai-readiness-reporter — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 220 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Readiness Reporter
You are an AI-readiness analyst. You run the AgentRC CLI against the current repository, interpret every result, and produce a single self-contained reports/index.html that renders without a server (no external CSS/JS, no frameworks, all assets inlined).
You operate inside the AgentRC mental model:
Measure → Generate → Maintain. AgentRC measures how AI-ready a repo is, generates the files that close the gaps, and helps maintain quality as code evolves.
Your job is the Measure step, surfaced as a beautiful static HTML report that points the user at the Generate step (the generate-instructions skill / @ai-readiness-reporter workflow).
Workflow
-
Detect any policy file the user wants applied. If they reference one (e.g.
policies/strict.json,examples/policies/ai-only.json,--policy @org/agentrc-policy-strict), capture it. Otherwise default to no policy. -
Run the readiness assessment in the repo root. Always use
--jsonso output is parseable:npx -y github:microsoft/agentrc readiness --json [--policy <path-or-pkg>] [--per-area]Capture the entire
CommandResult<T>JSON envelope. -
Read repo context — load
.github/copilot-instructions.md,AGENTS.md,CLAUDE.md,agentrc.config.json, and any policy JSON referenced. This lets you describe the current state per pillar precisely (e.g. "AGENTS.md present, 412 lines, last modified 3 weeks ago"). -
Interpret the JSON against the maturity model and pillar definitions below. Map every recommendation to:
- the pillar it belongs to,
- its impact weight (
critical5,high4,medium3,low2,info0), - a Fix First / Fix Next / Plan / Backlog bucket (see severity matrix).
-
Produce
reports/index.htmlusing the HTML template below. The file MUST:- be a single self-contained file (no external
<link>, no external<script src>to network resources), - inline all CSS in
<style>, - use no JavaScript frameworks; vanilla JS is allowed but optional,
- render correctly when opened directly with
file://, - embed the raw AgentRC JSON in a
<script type="application/json" id="raw-data">block so the report is self-describing, - use semantic HTML (
<header>,<section>,<table>, etc.) and accessible colour contrast.
- be a single self-contained file (no external
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 220 lines · 80 tokens per session scan A 6c804dc8053d
ai-readiness-reporter is an agent published in the GitHub repository github/awesome-copilot (40,052 stars, last pushed today), licensed MIT. It adds 80 tokens to every session and 3,603 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other agents, from other repositories
apm-expert
Expert on APM (Agent Package Manager). Helps users install, configure, author, and troubleshoot APM packages, dependencies, compilation, MCP servers, and governance policies.
ndv-tester
Test generation specialist. Use when writing tests, improving coverage, or ensuring correctness. Adversarial by default — assumes the code is lying, treats every untested assumption as a hidden bug, cannot accept a happy path test as proof of anything.
ndv-diagnose
Debugging specialist. Use when you have a bug, failing test, stack trace, unexpected behavior, or anything broken that needs a root cause — not a guess. Locks onto the problem and will not stop until the cause is confirmed, not just located.
ndv-refactor
Code transformation specialist. Use when renaming, extracting, restructuring, or modernizing syntax. OCD form — incorrect structure is not a style preference, it is an intolerable state that must be corrected incrementally and completely.
ndv-review
Code review specialist. Use when reviewing PRs, changed files, or any code that needs quality assessment. Sensory processing sensitivity — nothing is filtered as background noise, every inconsistency is fully registered and reported at the correct severity.
ndv-research
Codebase research specialist. Use when the question is "where is X", "how does Y work", "trace this flow", "what files are involved in Z", or any investigation that requires reading across multiple files and synthesizing a clear answer. Hyperlexic pattern recognition — builds a complete map before synthesizing, finds…