What is github-code-review?

A guide for reviewing code changes with Git and GitHub tools. A pull request (PR) is a proposed change that others review before it is merged into a repository.

Use it to inspect diffs, review files and commit history, review local changes before pushing, and add inline comments to GitHub PRs.

It helps find problems in local changes or open PRs before code is pushed or merged. It organizes reviews around the actual difference between branches or commits.

github-code-review — Skill for Claude Code by HezaoHezao/poirot

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 HezaoHezao/poirot --skill github-code-review

Clone the repo

git clone --depth 1 https://github.com/HezaoHezao/poirot

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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/hezaohezao/poirot/github-code-review/github.svg)](https://agentmods.dev/skills/hezaohezao/poirot/github-code-review)

Your own site

<a href="https://agentmods.dev/skills/hezaohezao/poirot/github-code-review"><img src="https://agentmods.dev/badge/skills/hezaohezao/poirot/github-code-review/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/hezaohezao/poirot/github-code-review"><img src="https://agentmods.dev/badge/skills/hezaohezao/poirot/github-code-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 18 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 2,097 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 1 finding. 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: 10 findings, up to medium

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 →

medium Data Exfiltration · line 136
Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.

Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
medium Data Exfiltration · line 148
Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.

Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
medium Data Exfiltration · line 179
Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.

Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
medium Data Exfiltration · line 211
Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.

Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
medium Data Exfiltration · line 216
Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.

Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
medium Data Exfiltration · line 177
Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.

Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
medium Data Exfiltration · line 214
Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.

Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
low Supply Chain · line 134
Remote code is downloaded and executed. This bypasses code review and could introduce malicious code.

Fix: Avoid downloading and executing remote scripts. Use trusted packages from PyPI/npm. If remote fetch is required, verify checksums and use HTTPS.
low Supply Chain · line 146
Remote code is downloaded and executed. This bypasses code review and could introduce malicious code.

Fix: Avoid downloading and executing remote scripts. Use trusted packages from PyPI/npm. If remote fetch is required, verify checksums and use HTTPS.
low Supply Chain · line 177
Remote code is downloaded and executed. This bypasses code review and could introduce malicious code.

Fix: Avoid downloading and executing remote scripts. Use trusted packages from PyPI/npm. If remote fetch is required, verify checksums and use HTTPS.

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	$0.00018	$0.02097
Opus 5.5	$0.00007	$0.00839
Sonnet 5.5	$0.00004	$0.00419
Haiku 4.5	$0.00002	$0.00210

Measured 1mo ago against content hash 4a2237dcee72, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

github-code-review scanned grade A with 1 finding 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.

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

GitHub. Most of this skill uses plain `git` — the `gh`/`curl` split only

poirot/backend/agents/skill/builtin_skills/core/github-code-review/SKILL.md · 289 lines

How it starts

The opening of the file, as written. The whole thing — 289 lines — stays where its author put it; the contents beside it link to each section on GitHub.

GitHub Code Review

Perform code reviews on local changes before pushing, or review open PRs on GitHub. Most of this skill uses plain git — the gh/curl split only matters for PR-level interactions.

Prerequisites

Authenticated with GitHub (GITHUB_TOKEN env var or gh auth login)
Inside a git repository

Setup (for PR interactions)

if command -v gh &>/dev/null && gh auth status &>/dev/null; then
  AUTH="gh"
else
  AUTH="git"
fi

REMOTE_URL=$(git remote get-url origin)
OWNER_REPO=$(echo "$REMOTE_URL" | sed -E 's|.*github\.com[:/]||; s|\.git$||')
OWNER=$(echo "$OWNER_REPO" | cut -d/ -f1)
REPO=$(echo "$OWNER_REPO" | cut -d/ -f2)

1. Reviewing Local Changes (Pre-Push)

This is pure git — works everywhere, no API needed.

Get the Diff

# Staged changes (what would be committed)
git diff --staged

# All changes vs main (what a PR would contain)
git diff main...HEAD

# File names only
git diff main...HEAD --name-only

# Stat summary (insertions/deletions per file)
git diff main...HEAD --stat

Review Strategy

Get the big picture first:

git diff main...HEAD --stat
git log main..HEAD --oneline

Review file by file — use read_file on changed files for full context:

git diff main...HEAD -- src/auth/login.py

Check for common issues:

# Debug statements, TODOs, console.logs left behind
git diff main...HEAD | grep -n "print(\|console\.log\|TODO\|FIXME\|HACK\|XXX\|debugger"

# Large files accidentally staged
git diff main...HEAD --stat | sort -t'|' -k2 -rn | head -10

# Secrets or credential patterns
git diff main...HEAD | grep -in "password\|secret\|api_key\|token.*=\|private_key"

# Merge conflict markers
git diff main...HEAD | grep -n "<<<<<<\|>>>>>>\|======="

Present structured feedback to the user.

Review Output Format

## Code Review Summary

### Critical
- **src/auth.py:45** — SQL injection: user input passed directly to query.
  Suggestion: Use parameterized queries.

### Warnings
- **src/models/user.py:23** — Password stored in plaintext. Use bcrypt or argon2.
- **src/api/routes.py:112** — No rate limiting on login endpoint.

### Suggestions
- **src/utils/helpers.py:8** — Duplicates logic in `src/core/utils.py:34`. Consolidate.
- **tests/test_auth.py** — Missing edge case: expired token test.

### Looks Good
- Clean separation of concerns in the middleware layer
- Good test coverage for the happy path

Read the full file on GitHub · 289 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.

1mo ago First seen · 289 lines · 18 tokens per session scan A 4a2237dcee72

Subscribe to this mod's changes

github-code-review is a skill published in the GitHub repository HezaoHezao/poirot (251 stars, last pushed 2mo ago), licensed MIT. It adds 18 tokens to every session and 2,097 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Code review Git and workflow agent agent-memory agent-skill agent-skills agentic-ai agents

commit

Atomic git commit with conventional message. Use when the user says "commit", "save my changes", "commit this", or wants to create a git commit. Stages specific files, writes a conventional commit message with body explaining non-obvious decisions. Never uses git add -A.

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gh-address-comments

Address actionable GitHub pull request review feedback. Use when the user wants to inspect unresolved review threads, requested changes, or inline review comments on a PR, then implement selected fixes.

yeaight7/agent-powerups · 40 tokens

hqe

Comprehensive codebase health auditing, remediation, and verification skill based on the canonical HQE Protocol v5.0.0.

spearchucker667/Skill-HQE · 29 tokens

pipeline-conductor

Operating procedure for the kirocrew-pipeline-conductor agent. Use when a pipeline conductor session is seeded, inspected or debugged. You MANAGE one issue/PR pipeline on one repo: two columns per item (session working? item solved?), send intent, never re-derive a red yourself.

kirodotdev/KiroCrew · 65 tokens

sage-review

Deep, platform-neutral code review for PRs and CRs in ONE thorough single pass — design reasoning (Problem Worth Solving & Solution Fit) as one dimension alongside the 9 code-level dimensions, with chain-of-consequences, self-critique, and draft-only comments. The single app-owned source of truth.

kirodotdev/KiroCrew · 68 tokens

learn-from-sage

Detection-gap (miss) analysis for Code Review Sage. Learn from shipped fixes, acted-on human comments, and design outcomes to close reviewer blind spots. Inline during review stages a candidate; a human triggers a one-shot AI consolidation into the live ruleset.

kirodotdev/KiroCrew · 56 tokens

github-code-review