review-pr

review-pr is a skill for Claude Code from tomzx/agents. It costs 77 tokens per session (7,770 once invoked), scanned A, original, MIT.

A static review of a GitHub pull request focused on how well its code is built. It examines code quality, architecture, security, tests, and operational concerns without running the code.

In plain words
What is it for?
Inspecting a pull request's implementation and writing structured findings for the author.
Why use it?
It catches maintainability, security, testing, and production risks that may not be visible from whether the requested behavior works.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter. Also seen: positional $N argument.

Good fit Inspecting a pull request's implementation and writing structured findings for the author.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/tomzx/agents/review-pr
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 tomzx/agents --skill review-pr
Clone the repo
git clone --depth 1 https://github.com/tomzx/agents

Made for: Claude Code.

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.

agentmods badge for review-pr

README.md
[![agentmods](https://agentmods.dev/badge/skills/tomzx/agents/review-pr/github.svg)](https://agentmods.dev/skills/tomzx/agents/review-pr)
Your own site
<a href="https://agentmods.dev/skills/tomzx/agents/review-pr"><img src="https://agentmods.dev/badge/skills/tomzx/agents/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.

agentmods 80×15 button for review-pr

Your own site · 80×15
<a href="https://agentmods.dev/skills/tomzx/agents/review-pr"><img src="https://agentmods.dev/badge/skills/tomzx/agents/review-pr.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 77 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 7,770 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: 2 findings, 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 283
    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.
  • high Tool Misuse · line 449
    Tool parameters are crafted to achieve unintended or unsafe behavior. Parameter abuse can bypass intended safety checks (e.g. shell=True, --force, dangerous glob patterns).
    Fix: Validate all tool parameters against an allowlist. Reject dangerous parameter values (shell=True, --force, -rf /) and use safe defaults.
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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00077 $0.07770
Opus 5.5 $0.00031 $0.03108
Sonnet 5.5 $0.00015 $0.01554
Haiku 4.5 $0.00008 $0.00777

Measured today against content hash f06a48fd4eb7, 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 today.

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.

skills/review-pr/SKILL.md · 613 lines

How it starts

The opening of the file, as written. The whole thing — 613 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Review Pull Request

Answers the craft question: "is this code well-built?" Covers approach and simplicity, code quality, architecture, security, tests, and operational concerns as static inspection. It does not build or run the code (that is /verify-pr's conformance role) and does not judge whether the target is the right product (that is /validate-pr's validation role). Findings about whether the criteria are met go to /verify-pr; findings about whether the right problem is solved go to /validate-pr. Judging whether the chosen approach is the simplest and most changeable for this codebase is this skill's job (see Approach & Simplicity); the reviewer presumes the change is larger than the problem needs and must work to disprove it before approving. Approach-level notes arriving from /validate-pr or /verify-pr go there. Writes findings to a structured markdown file.

Prerequisites

  • Apply the shared SDLC conventions in skills/sdlc/references/shared.md.
  • Apply the shared evidence standard in ../sdlc/references/evidence.md: label every finding with a level in the form L<n> - <Name>. This review is static, so its ceiling is L2 - Ruled out; a claim that needs execution routes to /verify-pr (does the accepted behavior work?) or /adversarial-pr-review (does a plausible failure happen?).
  • If no argument is provided, target the pull request from $PR_NUMBER (and $REPO).
  • gh CLI authenticated with read access to the target repository
  • git worktree available
  • PR number ($1) identifying an open pull request

Skill attribution (GitHub)

Before posting to GitHub, read ../github-post-attribution/SKILL.md and append the footer for SKILL_DIR = review-pr.

Communication guidelines (outbound text)

Before composing any text posted or drafted on the user's behalf, apply communication-guidelines/SKILL.md.

Workflow

Fetch PR metadata + comments ($1)
            |
            v
  review.yaml scope?
  (same head -> stop, return state;
   pure rebase -> bump sha, stop;
   ancestor delta or contained
   tree-diff -> incremental;
   else -> full review)
            |
            v
  Create git worktree on PR branch
            |
            v
  Pre-Review Checklist
  (build, metadata, objective)
            |
            v
  Code Review Checklist
  (approach, quality, tests,
   architecture, ops, security, docs)
            |
            v
  Context-Specific Review
  (feature / bug fix / DB / API?)
            |
            v
  Update review.yaml findings
  Write review-pr.<sha>.md
  (report.md -> latest)
             |
             v
   Post review file
   as PR comment
   (gated by should-post-to-github)

Read the full file on GitHub · 613 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.

  1. today Changed f06a48fd4eb7
  2. 6d ago Changed · -5 lines 91606518fbcc
  3. 9d ago Changed · +7 lines 426ae9e2248f
  4. 11d ago Changed · +1 lines db6bf16ae470
  5. 22d ago Changed · +62 lines · +11 tokens per session e080eea2b397
  6. 23d ago Changed · +7 lines 7fd4fe10c3d8
  7. 24d ago Changed 216d5002e4e7
  8. 27d ago Changed · +1 lines 6367f6484f59
  9. 28d ago Changed · +4 lines 69786469d4d0
  10. 1mo ago First seen · 536 lines · 66 tokens per session scan A 746d7c55f15f

Subscribe to this mod's changes

review-pr is a skill published in the GitHub repository tomzx/agents (13 stars, last pushed yesterday), licensed MIT. It adds 77 tokens to every session and 7,770 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 2026-09-03.

Related

Other skills, from other repositories

autoreview

Structured code review through the shared OpenClaw agent-skills installation.

openclaw/clawhub · 17 tokens

omh-code-review

This is an OMH code-review workflow skill, projected for Agent Skills hosts (Claude Code, Codex, Cursor, opencode, OpenClaw, pi).

rlaope/oh-my-hermes · 48 tokens

revdiff-plan

Review the last Codex assistant message (plan, analysis, or proposal) with inline annotations in a TUI overlay. Extracts the most recent response from Codex rollout files and opens it in revdiff for review and annotation. Activates on "revdiff-plan", "review plan with revdiff", "annotate plan", "review last response"…

umputun/revdiff · 84 tokens

explore-unknowns

Guide the user through a quadrant walk that maps the unknowns of a task — open by listing the known knowns, then work through known unknowns, unknown knowns, and unknown unknowns one stage at a time, ending with a complete four-quadrant map in the user's hands. Use when a request is ambiguous or underspecified, the…

dzhng/skills · 162 tokens

re0-merge

Review and land an external contribution the way this suite does: gate it against the thesis, land it with the author's credit intact, complete a new skill rather than merging it raw, then approve, credit, and explain before closing. Use when reviewing a pull request, as any collaborator or maintainer, not only the…

LilMGenius/paperthin · 70 tokens

asplos-writing-style

Use when drafting or revising ASPLOS prose — engineering the first two pages that rapid review actually reads, stating the cross-layer insight as one quotable sentence, structuring per-layer mechanism sections, writing bounded claims, and fitting everything into 11 self-contained pages that include figures and…

brycewang-stanford/Awesome-Journal-Skills · 65 tokens