| name | Review |
| description | Multi-agent review with parallel specialized reviewers and severity classification |
Review Skill
Overview
Perform thorough code review by spawning specialized reviewers in parallel, consolidating findings with severity classification (P0/P1/P2/P3), and gating completion on implementation-reviewer approval.
Methodology
- Run quality gates first:
pnpm test && pnpm lint
- Read the epic description (
bd show <epic>) for EARS requirements -- reviewers verify each requirement is met
- Search memory with
npx ca search for known patterns and recurring issues
- Select reviewer tier based on diff size:
- Small (<100 lines): 4 core -- security, test-coverage, simplicity, cct-reviewer
- Medium (100-500): add architecture, performance, edge-case (7 total)
- Large (500+): all 11 reviewers including docs, consistency, error-handling, pattern-matcher
- Spawn reviewers in an AgentTeam (TeamCreate + Task with
team_name):
- Role skills:
.claude/skills/compound/agents/{security-reviewer,architecture-reviewer,performance-reviewer,test-coverage-reviewer,simplicity-reviewer}/SKILL.md
- Security specialist skills (on-demand, spawned by security-reviewer):
.claude/skills/compound/agents/{security-injection,security-secrets,security-auth,security-data,security-deps}/SKILL.md
- For large diffs (500+), deploy MULTIPLE instances; split files across instances, coordinate via SendMessage
- Reviewers communicate findings to each other via
SendMessage
- Collect, consolidate, and deduplicate all findings
- Classify by severity: P0 (blocks merge), P1 (critical/blocking), P2 (important), P3 (minor)
- Use
AskUserQuestion when severity is ambiguous or fix has multiple valid options
- Create beads issues for P1 findings:
bd create --title="P1: ..."
- Verify spec alignment: flag unmet EARS requirements as P1, flag requirements met but missing from acceptance criteria as gaps
- Fix all P1 findings before proceeding
- Run
/implementation-reviewer as mandatory gate
- Capture novel findings with
npx ca learn; pattern-matcher auto-reinforces recurring issues
Memory Integration
- Run
npx ca search before review for known recurring issues
- pattern-matcher auto-reinforces: recurring findings get severity increased via
npx ca learn
- cct-reviewer reads CCT patterns for known Claude failure patterns
- Capture the review report via
npx ca learn with type=solution
Docs Integration
- docs-reviewer checks code/docs alignment and ADR compliance
- Flags undocumented public APIs and ADR violations
Literature
- Consult
docs/compound/research/code-review/ for systematic review methodology, severity taxonomies, and evidence-based review practices
- Run
npx ca knowledge "code review methodology" for indexed knowledge on review techniques
- Run
npx ca search "review" for lessons from past review cycles
Common Pitfalls
- Ignoring reviewer feedback because "it works"
- Not running all 11 reviewer perspectives (skipping dimensions)
- Treating all findings as equal priority (classify P1/P2/P3 first)
- Not creating beads issues for deferred fixes
- Skipping quality gates before review
- Bypassing the implementation-reviewer gate
- Not checking CCT patterns for known Claude mistakes
Quality Criteria
- All quality gates pass (
pnpm test, lint)
- All 11 reviewer perspectives were applied in parallel
- Findings are classified P0/P1/P2/P3 and deduplicated
- pattern-matcher checked memory and reinforced recurring issues
- cct-reviewer checked against known Claude failure patterns
- docs-reviewer confirmed docs/ADR alignment
- security-reviewer P0 findings: none (blocks merge)
- security-reviewer P1 findings: all acknowledged or resolved
- All P1 findings fixed before
/implementation-reviewer approval
- All spec requirements verified against implementation
/implementation-reviewer approved as mandatory gate
PHASE GATE 4 -- MANDATORY
Before starting Compound, verify review is complete:
/implementation-reviewer must have returned APPROVED
- All P1 findings must be resolved
CRITICAL: Use npx ca learn for ALL lesson storage -- NOT MEMORY.md.