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.
npx skills add eai-org/agent-toolkit --skill extract-pr-review-rulesgit clone --depth 1 https://github.com/eai-org/agent-toolkitWrote 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/skills/eai-org/agent-toolkit/extract-pr-review-rules)<a href="https://agentmods.dev/skills/eai-org/agent-toolkit/extract-pr-review-rules"><img src="https://agentmods.dev/badge/skills/eai-org/agent-toolkit/extract-pr-review-rules/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/skills/eai-org/agent-toolkit/extract-pr-review-rules"><img src="https://agentmods.dev/badge/skills/eai-org/agent-toolkit/extract-pr-review-rules.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 | $0.00039 | $0.01255 |
| Opus 5.5 | $0.00016 | $0.00502 |
| Sonnet 5.5 | $0.00008 | $0.00251 |
| Haiku 4.5 | $0.00004 | $0.00126 |
Grade A, and why
extract-pr-review-rules 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 3d 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.
How it starts
The opening of the file, as written. The whole thing — 131 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Extract PR review rules
Mine the conventions a repository's reviewers enforce in their inline PR review comments into one governing doc the project's review skills run as a checklist. Re-runnable: each run resumes from the last checkpoint, fetching only newer comments.
Invocation: /extract-pr-review-rules <repo-url>
1. Resolve the target
Identify the forge from the repo URL and work through whatever MCP or CLI is connected for it
(GitHub → gh). Unrecognized URL, or nothing connected → ask.
The URL is only the comment source; the doc lands in the current project, under its
governing-docs convention, default .agents/docs/pr-review-rules.md. Absent → first run, no
checkpoint, whole history in scope. Already there → read it: built from the same repo, its
checkpoint and standing rules make this a refresh; built from another repo, ask — another doc
path, or replace it with a first run.
Done when forge tooling, doc path, and checkpoint (a timestamp, or none) are settled.
2. Fetch to disk
Page the repo's inline review-comment feed across all PRs, open and closed, straight to a file in the session's scratch dir — never through the model, which truncates silently. On a refresh, filter by the checkpoint so only new comments come down:
gh api --paginate "repos/<owner>/<repo>/pulls/comments?per_page=100&since=<checkpoint>" \
> "$SCRATCH/comments.json"
Record the time the fetch started as the next checkpoint — not the newest comment's timestamp, which would skip anything posted while the run was working.
Then, still on disk, drop bots and deleted accounts, tally comments per author, and split the file into one per author. Nothing new to process → say so, leave the doc untouched, and stop.
Done when each author's file exists, the tallies plus the dropped comments sum to the fetched total, and the new checkpoint is recorded.
3. Extract, one subagent per reviewer
One isolated subagent per author, in parallel, each reading only that author's file so the raw comments never reach this session. No subagents in the runtime → work the files here one at a time. Prompt each with:
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.
- 3d ago First seen · 131 lines · 39 tokens per session scan A b88110df0193
extract-pr-review-rules is a skill published in the GitHub repository eai-org/agent-toolkit (61 stars, last pushed today), licensed MIT. It adds 39 tokens to every session and 1,255 once invoked, about $0.0002 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 2026-10-14.
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