A pull-request review process that classifies findings as Critical, Important, or Optional. A pull request is a proposed code change submitted for review before it is merged into the project.

Use it to review a pull request, inspect its tests and description, examine the full diff, and report prioritized findings in the required classification scheme.

It keeps review attention on meaningful engineering risks and filters out minor nitpicks. It also requires checking the pull request's status and intended behavior before judging the code.

pr-classify — Skill for Claude Code and Codex by yuri-semenenko: A pull-request review process that classifies findings as Critical…

Install

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.

agentmods

npx agentmods add skills/yuri-semenenko/ai-engineering-workspace/pr-classify

Any agent

npx skills add yuri-semenenko/ai-engineering-workspace --skill pr-classify

Clone the repo

git clone --depth 1 https://github.com/yuri-semenenko/ai-engineering-workspace

Made for: Claude Code, Codex.

Wrote 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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/yuri-semenenko/ai-engineering-workspace/pr-classify.svg)](https://agentmods.dev/skills/yuri-semenenko/ai-engineering-workspace/pr-classify)

Your own site

<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="Measured on agentmods" height="20"></a>

Per session 94 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 1,352 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. Scan, not verified.

Origin original No closer match found in the catalogue.

Token cost

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	$0.00047	$0.00676
Sonnet 5	$0.00019	$0.00270
Haiku 4.5	$0.00009	$0.00135

Measured 5d ago against content hash 0c85b239a855, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-05, from the pricing page.

Security

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 5d 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.

claude-code/.claude/skills/pr-classify/SKILL.md · 96 lines

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 run gh auth login before 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,files or git diff <base>...HEAD. If invoked with a PR number/URL, fetch via gh 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

Read the full file on GitHub · 96 lines

Changes

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.

5d ago First seen · 96 lines · 94 tokens per session scan A 0c85b239a855

Subscribe to this mod's changes

pr-classify is a skill published in the GitHub repository yuri-semenenko/ai-engineering-workspace (1 stars, last pushed 5d ago), licensed MIT. It adds 94 tokens to every session and 1,352 once invoked, about $0.0005 per session on Opus 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.

Code review Git and workflow agent-skills ai-agents claude-code codex developer-tools github-copilot

rework-rate

Measure and interpret PR rework rate — the emerging 5th DORA metric.

bradygaster/squad · 20 tokens

spec-kitty-charter-doctrine

Run charter interview, generation, context, and sync workflows for project governance in Spec Kitty 3.x. Access doctrine artifacts programmatically via DoctrineService. Resolve agent profiles. Load action-scoped governance context iteratively, not all at once. Triggers: "interview for charter", "generate charter"…

Priivacy-ai/spec-kitty · 135 tokens

spec-kitty-spdd-reasons

Drive REASONS Canvas authoring and review for Spec Kitty missions that opted in to Structured-Prompt-Driven Development (SPDD) via charter selection. Triggers: "use SPDD", "use REASONS", "generate a REASONS canvas", "apply structured prompt driven development", "make this mission SPDD". Does NOT handle: enforcing SPDD…

Priivacy-ai/spec-kitty · 118 tokens

pr-classify