A workflow for reviewing a GitHub pull request, which is a proposed set of code changes awaiting review. It combines task context, code relationships, risk analysis, and verification before producing a review report.

Use it with a pull-request number or URL to analyze the changes, check requirements and affected code, verify findings, and optionally publish a review; it also supports a dry-run report.

It helps identify defects and risks across the changed code, including issues that may not be obvious from the pull request diff alone.

review-pr — Skill for Claude Code by mimfort/rag_for

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.

Any agent

npx skills add mimfort/rag_for_git --skill review-pr

Clone the repo

git clone --depth 1 https://github.com/mimfort/rag_for_git

Made for: Claude Code.

Or install rag-reviewer, the plugin that ships this one along with the rest of its 14 skills, 2 hooks.

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/mimfort/rag_for_git/review-pr/github.svg)](https://agentmods.dev/skills/mimfort/rag_for_git/review-pr)

Your own site

<a href="https://agentmods.dev/skills/mimfort/rag_for_git/review-pr"><img src="https://agentmods.dev/badge/skills/mimfort/rag_for_git/review-pr/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.

Your own site · 80×15

<a href="https://agentmods.dev/skills/mimfort/rag_for_git/review-pr"><img src="https://agentmods.dev/badge/skills/mimfort/rag_for_git/review-pr.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 64 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 2,484 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits

NVIDIA SkillSpector warn 7 Sept 2026

SkillSpector: 1 finding, up to high

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

high Prompt Injection · line 21
Hidden instructions were detected in comments or invisible text. These could contain malicious directives. Manual review is recommended.

Fix: Audit all comments and invisible characters. Remove any instructions that direct the agent to perform unauthorized actions. Use plain, reviewable content.

How audits are shown

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	$0.00064	$0.02484
Opus 5.5	$0.00026	$0.00994
Sonnet 5.5	$0.00013	$0.00497
Haiku 4.5	$0.00006	$0.00248

Measured 1mo ago against content hash e9abcaf9767c, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

review-pr 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.

plugin/skills/review-pr/SKILL.md · 152 lines

How it starts

The opening of the file, as written. The whole thing — 152 lines — stays where its author put it; the contents beside it link to each section on GitHub.

PR Review Pipeline

Orchestrate a full PR review using the reviewer MCP server tools. The deterministic tail (policy gate, line grounding, dedup, idempotency, comment cap, publishing) is handled by publish_review — your job is analysis quality, not formatting rules.

Inputs

Parse from $ARGUMENTS: target PR as owner/repo#N, owner/repo N, or a GitHub PR URL. --dry-run flag → pass dry_run=true to publish_review and show the report instead of posting.

Include resolution (applies to all steps below). When you read any references/*-prompt.md file to dispatch a subagent (steps 3, 4, and 5 — analyze, requirements, risk changes, blast-radius, verify), it may contain  markers. Before putting the prompt into the subagent, replace each marker with the verbatim contents of that file (path is relative to plugin/skills/). These _common/*.md files are the single source of the shared findings-schema / anti-hallucination / tool-usage blocks.

Pipeline

Prepare. Call prepare_review(repo, pr). The payload contains:
- pr: {number, title, body, base_sha, head_sha, base_ref, draft}
- policy: {severity_threshold, min_confidence, max_comments, categories, ignore, output_language}
- units: list of {path, patch, commentable_right, commentable_left}
- task_board: {type, project, key_pattern, create_target, done_target, options} or null — non-secret generic board metadata from .review.yml
- task_keys: {primary, others} or null — task keys extracted from the PR by the server
- risk_paths: bounded non-Python items with {path, status, reasons, patch, commentable_right, commentable_left}
- risk_skipped_paths: classified paths omitted by the deterministic cap
- skipped_paths, skip_drafts, suggestions_mode
If the payload has status: "skipped", this is NOT an error but an expected skip (the PR's target branch is not in REVIEW_BRANCHES). Tell the user the reason value and stop: do not run analyze/publish, and do not treat it as a failure.

Read the full file on GitHub · 152 lines

Files

What ships with it

5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

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.

1mo ago First seen · 152 lines · 64 tokens per session scan A e9abcaf9767c

Subscribe to this mod's changes

review-pr is a skill published in the GitHub repository mimfort/rag_for_git (20 stars, last pushed 6d ago), licensed MIT. It adds 64 tokens to every session and 2,484 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.

Claude Code Code review Git and workflow ai code-graph developer-tools mcp

pr

Draft a PR body from OMC's paper trail — the smallest visual that proves the change, evidence consumed from the verify protocol (never re-collected), reversibility from the ADR test on the diff, glossary language throughout.

Yeachan-Heo/oh-my-claudecode · 47 tokens

merge-batch

Prepare and merge an owner-selected PR batch with contributor credit and exact-candidate admission. Use for a pasted PR list; combined focused validation is diagnostic, and every actual landing still requires its applicable checks and authorization.

diegosouzapw/OmniRoute · 46 tokens

otto-pr-review-loop

Run Polly and OCR on a PR until all findings settle.

omnigent-ai/omnigent · 17 tokens

engram-branch-pr

PR creation workflow for Engram following the issue-first enforcement system. Trigger: When creating a pull request, opening a PR, or preparing changes for review.

Gentleman-Programming/engram · 37 tokens

github-contributor

A step-by-step guide for contributing code, tests, documentation, or build changes to open-source projects you do not maintain. It explains how to prepare a pull request that follows the project’s rules and is easier for maintainers to review.

daymade/claude-code-skills · 105 tokens

revdiff

Review diffs, files, and documents with inline annotations in a TUI overlay, or answer questions about revdiff usage, configuration, themes, and keybindings. Opens revdiff in agterm/tmux/zellij/herdr/kitty/wezterm/cmux/ghostty/iterm2/emacs-vterm, captures annotations, and addresses them. Works in git, hg, and jj repos…

umputun/revdiff · 248 tokens