Review PR

Deep code review using 6-7 parallel specialized agents.

Quick Start

/ork:review-pr 123 /ork:review-pr feature-branch

Opus 4.6: Parallel agents use native adaptive thinking for deeper analysis. Complexity-aware routing matches agent model to review difficulty.

Argument Resolution

The PR number or branch is passed as the skill argument. Resolve it immediately:

PR_NUMBER = "$ARGUMENTS[0]" # e.g., "123" or "feature-branch" (CC 2.1.19 indexed access)

If no argument provided, check environment

if not PR_NUMBER: PR_NUMBER = os.environ.get("ORCHESTKIT_PR_URL", "").split("/")[-1]

If still empty, detect from current branch

if not PR_NUMBER: PR_NUMBER = "$(gh pr view --json number -q .number 2>/dev/null)"

Use PR_NUMBER consistently in all subsequent commands and agent prompts.

STEP 0: Verify User Intent with AskUserQuestion

BEFORE creating tasks, clarify review focus:

AskUserQuestion( questions=[{ "question": "What type of review do you need?", "header": "Focus", "options": [ {"label": "Full review (Recommended)", "description": "Security + code quality + tests + architecture", "markdown": "\nFull Review (6 agents)\n──────────────────────\n PR diff ──▶ 6 parallel agents:\n ┌────────────┐ ┌────────────┐\n │ Quality x2 │ │ Security │\n ├────────────┤ ├────────────┤\n │ Test Gen │ │ Backend │\n ├────────────┤ ├────────────┤\n │ Frontend │ │ (optional) │\n └────────────┘ └────────────┘\n ▼\n Synthesized review comment\n with conventional comments:\n praise/suggestion/issue/nitpick\n"}, {"label": "Security focus", "description": "Prioritize security vulnerabilities", "markdown": "\nSecurity Review\n───────────────\n PR diff ──▶ security-auditor:\n ┌─────────────────────────┐\n │ Auth changes ✓/✗ │\n │ Input validation ✓/✗ │\n │ SQL/XSS/CSRF ✓/✗ │\n │ Secrets in diff ✓/✗ │\n │ Dependency risk ✓/✗ │\n └─────────────────────────┘\n Output: Security-focused\n review with fix suggestions\n"}, {"label": "Performance focus", "description": "Focus on performance implications", "markdown": "\nPerformance Review\n──────────────────\n PR diff ──▶ perf analysis:\n ┌─────────────────────────┐\n │ N+1 queries ✓/✗ │\n │ Bundle size impact ±KB │\n │ Render performance ✓/✗ │\n │ Memory leaks ✓/✗ │\n │ Caching gaps ✓/✗ │\n └─────────────────────────┘\n Agent: frontend-performance\n or python-performance\n"}, {"label": "Quick review", "description": "High-level review, skip deep analysis", "markdown": "\nQuick Review (~2 min)\n─────────────────────\n PR diff ──▶ Single pass\n\n Output:\n ├── Approve / Request changes\n ├── Top 3 concerns\n └── 1-paragraph summary\n 1 agent: code-quality-reviewer\n No deep security/perf scan\n"} ], "multiSelect": false }] )

Based on answer, adjust workflow:

• Full review: All 6-7 parallel agents

• Security focus: Prioritize security-auditor, reduce other agents

• Performance focus: Add frontend-performance-engineer agent

• Quick review: Single code-quality-reviewer agent only

STEP 0b: Select Orchestration Mode

Load orchestration guidance: Read("${CLAUDE_SKILL_DIR}/references/orchestration-mode-selection.md")

MCP Probe (CC 2.1.71)

ToolSearch(query="select:mcp__memory__search_nodes") Write(".claude/chain/capabilities.json", { memory, timestamp })

If memory available: search for past review patterns on these files

CRITICAL: Task Management is MANDATORY (CC 2.1.16)

BEFORE doing ANYTHING else, create tasks to track progress:

1. Create main review task IMMEDIATELY

TaskCreate( subject="Review PR #{number}", description="Comprehensive code review with parallel agents", activeForm="Reviewing PR #{number}" )

2. Create subtasks for each phase

TaskCreate(subject="Gather PR information", activeForm="Gathering PR information") TaskCreate(subject="Launch review agents", activeForm="Dispatching review agents") TaskCreate(subject="Run validation checks", activeForm="Running validation checks") TaskCreate(subject="Synthesize review", activeForm="Synthesizing review") TaskCreate(subject="Submit review", activeForm="Submitting review")

3. Update status as you progress

TaskUpdate(taskId="2", status="in_progress") # When starting TaskUpdate(taskId="2", status="completed") # When done

Phase 1: Gather PR Information

Get PR details

gh pr view $PR_NUMBER --json title,body,files,additions,deletions,commits,author

View the diff

gh pr diff $PR_NUMBER

Check CI status

gh pr checks $PR_NUMBER

Capture Scope for Agents

Capture changed files for agent scope injection

CHANGED_FILES=$(gh pr diff $PR_NUMBER --name-only)

Detect affected domains

HAS_FRONTEND=$(echo "$CHANGED_FILES" | grep -qE '.(tsx?|jsx?|css|scss)$' && echo true || echo false) HAS_BACKEND=$(echo "$CHANGED_FILES" | grep -qE '.(py|go|rs|java)$' && echo true || echo false) HAS_AI=$(echo "$CHANGED_FILES" | grep -qE '(llm|ai|agent|prompt|embedding)' && echo true || echo false)

Pass CHANGED_FILES to every agent prompt in Phase 3. Pass domain flags to select which agents to spawn.

Identify: total files changed, lines added/removed, affected domains (frontend, backend, AI).

Tool Guidance

Task Use Avoid

Fetch PR diff Bash: gh pr diff

Reading all changed files individually

List changed files Bash: gh pr diff --name-only

bash find

Search for patterns Grep(pattern="...", path="src/")

bash grep

Read file content Read(file_path="...")

bash cat

Check CI status Bash: gh pr checks

Polling APIs

<use_parallel_tool_calls> When gathering PR context, run independent operations in parallel:

• gh pr view (PR metadata), gh pr diff (changed files), gh pr checks (CI status)

Spawn all three in ONE message. This cuts context-gathering time by 60%. For agent-based review (Phase 3), all 6 agents are independent -- launch them together. </use_parallel_tool_calls>

Phase 2: Skills Auto-Loading (CC 2.1.6)

CC 2.1.6 auto-discovers skills -- no manual loading needed!

Relevant skills activated automatically:

• code-review-playbook -- Review patterns, conventional comments

• security-scanning -- OWASP, secrets, dependencies

• type-safety-validation -- Zod, TypeScript strict

• testing-unit , testing-e2e , testing-integration -- Test adequacy, coverage gaps, rule matching

Phase 3: Parallel Code Review (6 Agents)

Project Context Injection

Before spawning agents, load project-specific review context from memory:

Load project review context (conventions, known weaknesses, past findings)

This gives agents project-specific knowledge without re-discovering patterns

PROJECT_CONTEXT = Read("${MEMORY_DIR}/review-pr-context.md") # Falls back gracefully if missing

All agent prompts receive ${PROJECT_CONTEXT} so they know project conventions, security patterns, and known weaknesses from prior reviews.

Structured Output

All agents return findings as JSON (see structured output contract in agent prompt files). This enables automated deduplication, severity sorting, and memory graph persistence in Phase 5.

Domain-Aware Agent Selection

Only spawn agents relevant to the PR's changed domains:

Domain Detected Agents to Spawn

Backend only code-quality (x2), security-auditor, test-generator, backend-system-architect

Frontend only code-quality (x2), security-auditor, test-generator, frontend-ui-developer

Full-stack All 6 agents

AI/LLM code All 6 + optional llm-integrator (7th)

Skip agents for domains not present in the diff. This saves ~33% tokens on domain-specific PRs.

See Agent Prompts -- Task Tool Mode for the 6 parallel agent prompts.

See Agent Prompts -- Agent Teams Mode for the mesh alternative.

See AI Code Review Agent for the optional 7th LLM agent.

Phase 4: Run Validation

Load validation commands: Read("${CLAUDE_SKILL_DIR}/references/validation-commands.md")

Phase 5: Synthesize Review

Combine all agent feedback into a structured report. Load template: Read("${CLAUDE_SKILL_DIR}/references/review-report-template.md")

Memory Persistence

After synthesis, persist critical/high findings to the memory graph so future reviews build on past knowledge:

Persist review findings for cross-session learning

mcp__memory__create_entities(entities=[{ "name": "PR-{number}-Review", "entityType": "code-review", "observations": ["<summary>", "<critical findings>", "<patterns discovered>"] }])

Update known-weaknesses entity if new patterns found

mcp__memory__add_observations(observations=[{ "entityName": "review-known-weaknesses", "contents": ["<new pattern from this review>"] }])

Phase 6: Submit Review

Approve

gh pr review $PR_NUMBER --approve -b "Review message"

Request changes

gh pr review $PR_NUMBER --request-changes -b "Review message"

CC 2.1.20 Enhancements

PR Status Enrichment

The pr-status-enricher hook automatically detects open PRs at session start and sets:

• ORCHESTKIT_PR_URL -- PR URL for quick reference

• ORCHESTKIT_PR_STATE -- PR state (OPEN, MERGED, CLOSED)

Session Resume with PR Context (CC 2.1.27+)

Sessions are automatically linked when reviewing PRs. Resume later with full context:

claude --from-pr 123 claude --from-pr https://github.com/org/repo/pull/123

Task Metrics (CC 2.1.30)

Load metrics template: Read("${CLAUDE_SKILL_DIR}/references/task-metrics-template.md")

Conventional Comments

Use these prefixes for comments:

• praise: -- Positive feedback

• nitpick: -- Minor suggestion

• suggestion: -- Improvement idea

• issue: -- Must fix

• question: -- Needs clarification

Related Skills

• ork:commit : Create commits after review

• ork:create-pr : Create PRs for review

• slack-integration : Team notifications for review events

References

Load on demand with Read("${CLAUDE_SKILL_DIR}/references/<file>") :

File Content

review-template.md

Review checklist template

review-report-template.md

Structured review report

orchestration-mode-selection.md

Task tool vs Agent Teams

validation-commands.md

Build/test/lint commands

task-metrics-template.md

Task metrics format

Rules: Read("${CLAUDE_SKILL_DIR}/rules/<file>") :

File Content

agent-prompts-task-tool.md

Agent prompts for Task tool mode

agent-prompts-agent-teams.md

Agent prompts for Agent Teams mode

• AI Code Review Agent