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PR Review Expert

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Streamline your code review process with in-depth analysis.

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What PR Review Expert does

PR Review Expert is a powerful tool designed for structured and systematic code reviews specifically tailored for GitHub pull requests and GitLab merge requests. This skill provides a comprehensive analysis that goes beyond simple style checks, offering critical insights such as blast radius analysis, security scanning, breaking change detection, and test coverage delta calculations. By generating a reviewer-ready report complete with a 30+ item checklist and prioritized findings, it equips developers and teams with the necessary information to make informed decisions before merging code.

The skill's blast radius analysis identifies which files, services, and downstream consumers may be affected by the proposed changes, helping teams understand the potential impact of a pull request. Additionally, the security scan checks for common vulnerabilities like SQL injection and XSS, ensuring that security is prioritized during the review process. The test coverage delta feature assesses the relationship between new code and associated tests, providing insights into the adequacy of testing efforts.

PR Review Expert is particularly beneficial for teams working with shared libraries, APIs, or database schemas, as it highlights breaking changes and performance impacts, allowing for proactive risk management. It is also a valuable resource for onboarding new contributors, ensuring their pull requests receive thorough feedback, and for conducting reviews after security incidents to mitigate future risks.

This skill is ideal for developers and teams looking to enhance their code review process, improve code quality, and maintain high security standards. By integrating PR Review Expert into your workflow, you can ensure that every pull request is scrutinized effectively, leading to safer and more reliable code deployments.

When to use it

Use this skill before merging any pull request that impacts shared libraries, APIs, or database schemas, especially for large changes or security-sensitive code.

When not to use it

This skill may not be necessary for small or trivial pull requests where a detailed review is unwarranted.

What you can build with it

Reviewing a Large PR

Use PR Review Expert to systematically analyze a large pull request with over 200 lines changed, ensuring all aspects are covered.

Onboarding New Contributors

Employ this skill to provide thorough feedback on new contributors' pull requests, helping them understand code quality expectations.

Post-Incident Code Review

After a security incident, utilize PR Review Expert to review similar pull requests proactively, identifying potential vulnerabilities.

How to install PR Review Expert

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1. Install with the skills CLI

npx skills add alirezarezvani/claude-skills/pr-review-expert --agent claude-code

2. Or install it manually

Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.

Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs

Inside SKILL.md

Written by alirezarezvani

PR Review Expert

Tier: POWERFUL Category: Engineering Domain: Code Review / Quality Assurance


Overview

Structured, systematic code review for GitHub PRs and GitLab MRs. Goes beyond style nits — this skill performs blast radius analysis, security scanning, breaking change detection, and test coverage delta calculation. Produces a reviewer-ready report with a 30+ item checklist and prioritized findings.


Core Capabilities

  • Blast radius analysis — trace which files, services, and downstream consumers could break
  • Security scan — SQL injection, XSS, auth bypass, secret exposure, dependency vulns
  • Test coverage delta — new code vs new tests ratio
  • Breaking change detection — API contracts, DB schema migrations, config keys
  • Ticket linking — verify Jira/Linear ticket exists and matches scope
  • Performance impact — N+1 queries, bundle size regression, memory allocations

When to Use

  • Before merging any PR/MR that touches shared libraries, APIs, or DB schema
  • When a PR is large (>200 lines changed) and needs structured review
  • Onboarding new contributors whose PRs need thorough feedback
  • Security-sensitive code paths (auth, payments, PII handling)
  • After an incident — review similar PRs proactively

Fetching the Diff

GitHub (gh CLI)

# View diff in terminal
gh pr diff <PR_NUMBER>

# Get PR metadata (title, body, labels, linked issues)
gh pr view <PR_NUMBER> --json title,body,labels,assignees,milestone

# List files changed
gh pr diff <PR_NUMBER> --name-only

# Check CI status
gh pr checks <PR_NUMBER>

# Download diff to file for analysis
gh pr diff <PR_NUMBER> > /tmp/pr-<PR_NUMBER>.diff

GitLab (glab CLI)

# View MR diff
glab mr diff <MR_IID>

# MR details as JSON
glab mr view <MR_IID> --output json

# List changed files
glab mr diff <MR_IID> --name-only

# Download diff
glab mr diff <MR_IID> > /tmp/mr-<MR_IID>.diff

Workflow

Step 1 — Fetch Context

PR=123
gh pr view $PR --json title,body,labels,milestone,assignees | jq .
gh pr diff $PR --name-only
gh pr diff $PR > /tmp/pr-$PR.diff

Step 2 — Blast Radius Analysis

For each changed file, identify:

  1. Direct dependents — who imports this file?
# Find all files importing a changed module
grep -r "from ['\"].*changed-module['\"]" src/ --include="*.ts" -l
grep -r "require(['\"].*changed-module" src/ --include="*.js" -l

# Python
grep -r "from changed_module import\|import changed_module" . --include="*.py" -l
  1. Service boundaries — does this change cross a service?
# Check if changed files span multiple services (monorepo)
gh pr diff $PR --name-only | cut -d/ -f1-2 | sort -u
  1. Shared contracts — types, interfaces, schemas
gh pr diff $PR --name-only | grep -E "types/|interfaces/|schemas/|models/"

Blast radius severity:

  • CRITICAL — shared library, DB model, auth middleware, API contract
  • HIGH — service used by >3 others, shared config, env vars
  • MEDIUM — single service internal change, utility function
  • LOW — UI component, test file, docs

Step 3 — Security Scan

DIFF=/tmp/pr-$PR.diff

# SQL Injection — raw query string interpolation
grep -n "query\|execute\|raw(" $DIFF | grep -E '\$\{|f"|%s|format\('

# Hardcoded secrets
grep -nE "(password|secret|api_key|token|private_key)\s*=\s*['\"][^'\"]{8,}" $DIFF

# AWS key pattern
grep -nE "AKIA[0-9A-Z]{16}" $DIFF

# JWT secret in code
grep -nE "jwt\.sign\(.*['\"][^'\"]{20,}['\"]" $DIFF

# XSS vectors
grep -n "dangerouslySetInnerHTML\|innerHTML\s*=" $DIFF

# Auth bypass patterns
grep -n "bypass\|skip.*auth\|noauth\|TODO.*auth" $DIFF

# Insecure hash algorithms
grep -nE "md5\(|sha1\(|createHash\(['\"]md5|createHash\(['\"]sha1" $DIFF

# eval / exec
grep -nE "\beval\(|\bexec\(|\bsubprocess\.call\(" $DIFF

# Prototype pollution
grep -n "__proto__\|constructor\[" $DIFF

# Path traversal risk
grep -nE "path\.join\(.*req\.|readFile\(.*req\." $DIFF

Step 4 — Test Coverage Delta

# Count source vs test files changed
CHANGED_SRC=$(gh pr diff $PR --name-only | grep -vE "\.test\.|\.spec\.|__tests__")
CHANGED_TESTS=$(gh pr diff $PR --name-only | grep -E "\.test\.|\.spec\.|__tests__")

echo "Source files changed: $(echo "$CHANGED_SRC" | wc -w)"
echo "Test files changed:   $(echo "$CHANGED_TESTS" | wc -w)"

# Lines of new logic vs new test lines
LOGIC_LINES=$(grep "^+" /tmp/pr-$PR.diff | grep -v "^+++" | wc -l)
echo "New lines added: $LOGIC_LINES"

# Run coverage locally
npm test -- --coverage --changedSince=main 2>/dev/null | tail -20
pytest --cov --cov-report=term-missing 2>/dev/null | tail -20

Coverage delta rules:

  • New function without tests → flag
  • Deleted tests without deleted code → flag
  • Coverage drop >5% → block merge
  • Auth/payments paths → require 100% coverage

Step 5 — Breaking Change Detection

API Contract Changes

# OpenAPI/Swagger spec changes
grep -n "openapi\|swagger" /tmp/pr-$PR.diff | head -20

# REST route removals or renames
grep "^-" /tmp/pr-$PR.diff | grep -E "router\.(get|post|put|delete|patch)\("

# GraphQL schema removals
grep "^-" /tmp/pr-$PR.diff | grep -E "^-\s*(type |field |Query |Mutation )"

# TypeScript interface removals
grep "^-" /tmp/pr-$PR.diff | grep -E "^-\s*(export\s+)?(interface|type) "

DB Schema Changes

# Migration files added
gh pr diff $PR --name-only | grep -E "migrations?/|alembic/|knex/"

# Destructive operations
grep -E "DROP TABLE|DROP COLUMN|ALTER.*NOT NULL|TRUNCATE" /tmp/pr-$PR.diff

# Index removals (perf regression risk)
grep "DROP INDEX\|remove_index" /tmp/pr-$PR.diff

Config / Env Var Changes

# New env vars referenced in code (might be missing in prod)
grep "^+" /tmp/pr-$PR.diff | grep -oE "process\.env\.[A-Z_]+" | sort -u

# Removed env vars (could break running instances)
grep "^-" /tmp/pr-$PR.diff | grep -oE "process\.env\.[A-Z_]+" | sort -u

Step 6 — Performance Impact

# N+1 query patterns (DB calls inside loops)
grep -n "\.find\|\.findOne\|\.query\|db\." /tmp/pr-$PR.diff | grep "^+" | head -20
# Then check surrounding context for forEach/map/for loops

# Heavy new dependencies
grep "^+" /tmp/pr-$PR.diff | grep -E '"[a-z@].*":\s*"[0-9^~]' | head -20

# Unbounded loops
grep -n "while (true\|while(true" /tmp/pr-$PR.diff | grep "^+"

# Missing await (accidentally sequential promises)
grep -n "await.*await" /tmp/pr-$PR.diff | grep "^+" | head -10

# Large in-memory allocations
grep -n "new Array([0-9]\{4,\}\|Buffer\.alloc" /tmp/pr-$PR.diff | grep "^+"

Ticket Linking Verification

# Extract ticket references from PR body
gh pr view $PR --json body | jq -r '.body' | \
  grep -oE "(PROJ-[0-9]+|[A-Z]+-[0-9]+|https://linear\.app/[^)\"]+)" | sort -u

# Verify Jira ticket exists (requires JIRA_API_TOKEN to be SET in the environment).
# Credentials are fed to curl via a config read from stdin (-K -) so the token
# never appears in argv — `ps aux` / /proc/*/cmdline can't see it, and nothing
# secret lands in shell history. Never paste the raw token on the command line.
TICKET="PROJ-123"
: "${JIRA_API_TOKEN:?JIRA_API_TOKEN must be set}"
curl -s -K - "https://your-org.atlassian.net/rest/api/3/issue/$TICKET" <<EOF | \
  jq '{key, summary: .fields.summary, status: .fields.status.name}'
user = "user@company.com:$JIRA_API_TOKEN"
EOF

# Linear ticket — same pattern: the Authorization header goes through the
# stdin config, not a -H flag, to keep the key out of the process list.
LINEAR_ID="abc-123"
: "${LINEAR_API_KEY:?LINEAR_API_KEY must be set}"
curl -s -K - -H "Content-Type: application/json" \
  --data "{\"query\": \"{ issue(id: \\\"$LINEAR_ID\\\") { title state { name } } }\"}" \
  https://api.linear.app/graphql <<EOF | jq .
header = "Authorization: $LINEAR_API_KEY"
EOF

Security note: for repeated Jira use, prefer a ~/.netrc entry (machine your-org.atlassian.net login user@company.com password <token>, chmod 600 ~/.netrc) and call curl -s --netrc … — no secret material in the command at all.


Complete Review Checklist (30+ Items)

## Code Review Checklist

### Scope & Context
- [ ] PR title accurately describes the change
- [ ] PR description explains WHY, not just WHAT
- [ ] Linked Jira/Linear ticket exists and matches scope
- [ ] No unrelated changes (scope creep)
- [ ] Breaking changes documented in PR body

### Blast Radius
- [ ] Identified all files importing changed modules
- [ ] Cross-service dependencies checked
- [ ] Shared types/interfaces/schemas reviewed for breakage
- [ ] New env vars documented in .env.example
- [ ] DB migrations are reversible (have down() / rollback)

### Security
- [ ] No hardcoded secrets or API keys
- [ ] SQL queries use parameterized inputs (no string interpolation)
- [ ] User inputs validated/sanitized before use
- [ ] Auth/authorization checks on all new endpoints
- [ ] No XSS vectors (innerHTML, dangerouslySetInnerHTML)
- [ ] New dependencies checked for known CVEs
- [ ] No sensitive data in logs (PII, tokens, passwords)
- [ ] File uploads validated (type, size, content-type)
- [ ] CORS configured correctly for new endpoints

### Testing
- [ ] New public functions have unit tests
- [ ] Edge cases covered (empty, null, max values)
- [ ] Error paths tested (not just happy path)
- [ ] Integration tests for API endpoint changes
- [ ] No tests deleted without clear reason
- [ ] Test names clearly describe what they verify

### Breaking Changes
- [ ] No API endpoints removed without deprecation notice
- [ ] No required fields added to existing API responses
- [ ] No DB columns removed without two-phase migration plan
- [ ] No env vars removed that may be set in production
- [ ] Backward-compatible for external API consumers

### Performance
- [ ] No N+1 query patterns introduced
- [ ] DB indexes added for new query patterns
- [ ] No unbounded loops on potentially large datasets
- [ ] No heavy new dependencies without justification
- [ ] Async operations correctly awaited
- [ ] Caching considered for expensive repeated operations

### Code Quality
- [ ] No dead code or unused imports
- [ ] Error handling present (no bare empty catch blocks)
- [ ] Consistent with existing patterns and conventions
- [ ] Complex logic has explanatory comments
- [ ] No unresolved TODOs (or tracked in ticket)

Output Format

Structure your review comment as:

## PR Review: [PR Title] (#NUMBER)

Blast Radius: HIGH — changes lib/auth used by 5 services
Security: 1 finding (medium severity)
Tests: Coverage delta +2%
Breaking Changes: None detected

--- MUST FIX (Blocking) ---

1. SQL Injection risk in src/db/users.ts:42
   Raw string interpolation in WHERE clause.
   Fix: db.query("SELECT * WHERE id = $1", [userId])

--- SHOULD FIX (Non-blocking) ---

2. Missing auth check on POST /api/admin/reset
   No role verification before destructive operation.

--- SUGGESTIONS ---

3. N+1 pattern in src/services/reports.ts:88
   findUser() called inside results.map() — batch with findManyUsers(ids)

--- LOOKS GOOD ---
- Test coverage for new auth flow is thorough
- DB migration has proper down() rollback method
- Error handling consistent with rest of codebase

Common Pitfalls

  • Reviewing style over substance — let the linter handle style; focus on logic, security, correctness
  • Missing blast radius — a 5-line change in a shared utility can break 20 services
  • Approving untested happy paths — always verify error paths have coverage
  • Ignoring migration risk — NOT NULL additions need a default or two-phase migration
  • Indirect secret exposure — secrets in error messages/logs, not just hardcoded values
  • Skipping large PRs — if a PR is too large to review properly, request it be split

Best Practices

  1. Read the linked ticket before looking at code — context prevents false positives
  2. Check CI status before reviewing — don't review code that fails to build
  3. Prioritize blast radius and security over style
  4. Reproduce locally for non-trivial auth or performance changes
  5. Label each comment clearly: "nit:", "must:", "question:", "suggestion:"
  6. Batch all comments in one review round — don't trickle feedback
  7. Acknowledge good patterns, not just problems — specific praise improves culture

Frequently asked questions about PR Review Expert

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