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 yuri-semenenko/ai-engineering-workspace --skill pr-classifygit clone --depth 1 https://github.com/yuri-semenenko/ai-engineering-workspaceWrote 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/yuri-semenenko/ai-engineering-workspace/pr-classify)<a href="https://agentmods.dev/skills/yuri-semenenko/ai-engineering-workspace/pr-classify"><img src="https://agentmods.dev/badge/skills/yuri-semenenko/ai-engineering-workspace/pr-classify/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/yuri-semenenko/ai-engineering-workspace/pr-classify"><img src="https://agentmods.dev/badge/skills/yuri-semenenko/ai-engineering-workspace/pr-classify.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 | $0.00094 | $0.01352 |
| Opus 5.5 | $0.00038 | $0.00541 |
| Sonnet 5.5 | $0.00019 | $0.00270 |
| Haiku 4.5 | $0.00009 | $0.00135 |
Grade A, and why
pr-classify 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.
How it starts
The opening of the file, as written. The whole thing — 96 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PR classify
Review the current branch (or a specified PR) and produce structured feedback in the user's canonical classification scheme. The user is a Staff Engineer who explicitly wants to filter out nitpicks and focus on meaningful engineering concerns.
Preconditions (before any review work)
- Auth. Run
gh auth status. If unauthenticated, stop and tell the user to rungh auth loginbefore continuing. Do not fall back to a local-branch-only review silently. - PR target. A PR number/URL or a checked-out branch with an open PR is required. If invoked as a bare review with no PR context, ask for the target instead of guessing.
- Open state. Verify the PR is still open:
gh pr view <id> --json state,mergedAt. If it is merged or closed, surface that and confirm the user still wants the review before proceeding.
Steps
- Identify the PR scope. If invoked on a branch, run
gh pr view --json title,body,baseRefName,headRefName,filesorgit diff <base>...HEAD. If invoked with a PR number/URL, fetch viagh pr view <id>. - Read the tests first, then the diff fully. Start with the tests and the PR description to recover intended behavior — what the change is supposed to do — then read the implementation against that intent, not just changed-line context. Cross-reference touched files for callers and tests.
- For each finding, decide the bucket BEFORE writing it. If it's borderline Important/Optional and you can't articulate the cost of ignoring it, drop it.
- Group findings by bucket, not by file. Within each bucket, order by severity.
- Cite file:line for every comment. Never describe an issue abstractly.
- End with a summary verdict: approve / request changes / needs discussion. One sentence rationale.
Classification rules
Critical
Correctness, security, reliability. Examples:
- Logic bug that breaks the happy path or a documented edge case
- SQL injection, XSS, auth bypass, secret leak
- Race condition, unhandled promise rejection in hot path
- Data loss / migration that can't roll back
- Regression in existing test coverage
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 · 96 lines · 94 tokens per session scan A 0c85b239a855
pr-classify is a skill published in the GitHub repository yuri-semenenko/ai-engineering-workspace (4 stars, last pushed 1mo ago), licensed MIT. It adds 94 tokens to every session and 1,352 once invoked, about $0.0004 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 2026-08-31.
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