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/archubbuck/workspace-architectWrote 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/archubbuck/workspace-architect/ai-readiness-reporter)<a href="https://agentmods.dev/agents/archubbuck/workspace-architect/ai-readiness-reporter"><img src="https://agentmods.dev/badge/agents/archubbuck/workspace-architect/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/archubbuck/workspace-architect/ai-readiness-reporter"><img src="https://agentmods.dev/badge/agents/archubbuck/workspace-architect/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 | $0.00040 | $0.01801 |
| Sonnet 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 10d 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.
This is a copy
100% identical to ai-readiness-reporter — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
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.
- 10d ago First seen · 220 lines · 80 tokens per session scan A 6c804dc8053d
ai-readiness-reporter is an agent published in the GitHub repository archubbuck/workspace-architect (18 stars, last pushed 6d ago), licensed ISC. It adds 80 tokens to every session and 3,603 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to ai-readiness-reporter, differing in 0 lines, and is treated as a copy.
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