Pr Review Expert

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

2. 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

3. 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)
TICKET="PROJ-123"
curl -s -u "[email protected]:$JIRA_API_TOKEN" \
  "https://your-org.atlassian.net/rest/api/3/issue/$TICKET" | \
  jq '{key, summary: .fields.summary, status: .fields.status.name}'

# Linear ticket
LINEAR_ID="abc-123"
curl -s -H "Authorization: $LINEAR_API_KEY" \
  -H "Content-Type: application/json" \
  --data "{\"query\": \"{ issue(id: \\\"$LINEAR_ID\\\") { title state { name } } }\"}" \
  https://api.linear.app/graphql | jq .

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