// Multi-agent orchestration patterns. Use when multiple independent tasks can run with different domain expertise or when comprehensive analysis requires multiple perspectives.
| name | parallel-agents |
| description | Multi-agent orchestration patterns. Use when multiple independent tasks can run with different domain expertise or when comprehensive analysis requires multiple perspectives. |
| when_to_use | When a task requires 2+ specialist agents, comprehensive multi-domain analysis, or coordinated parallel execution. Use with /orchestrate or /coordinate workflows. NOT for single-domain tasks where one agent suffices. |
| allowed-tools | Read, Glob, Grep |
| effort | medium |
Orchestration through AG Kit's built-in Agent Tool
This skill enables coordinating multiple specialized agents through AG Kit's native agent system. Unlike external scripts, this approach keeps all orchestration within AG Kit's control.
✅ Good for:
❌ Not for:
Use the security-auditor agent to review authentication
First, use the explorer-agent to discover project structure.
Then, use the backend-specialist to review API endpoints.
Finally, use the test-engineer to identify test gaps.
Use the frontend-specialist to analyze React components.
Based on those findings, have the test-engineer generate component tests.
Resume agent [agentId] and continue with additional requirements.
Agents: explorer-agent → [domain-agents] → synthesis
1. explorer-agent: Map codebase structure
2. security-auditor: Security posture
3. backend-specialist: API quality
4. frontend-specialist: UI/UX patterns
5. test-engineer: Test coverage
6. Synthesize all findings
Agents: affected-domain-agents → test-engineer
1. Identify affected domains (backend? frontend? both?)
2. Invoke relevant domain agents
3. test-engineer verifies changes
4. Synthesize recommendations
Agents: security-auditor → penetration-tester → synthesis
1. security-auditor: Configuration and code review
2. penetration-tester: Active vulnerability testing
3. Synthesize with prioritized remediation
| Agent | Expertise | Trigger Phrases |
|---|---|---|
orchestrator | Coordination | "comprehensive", "multi-perspective" |
security-auditor | Security | "security", "auth", "vulnerabilities" |
penetration-tester | Security Testing | "pentest", "red team", "exploit" |
backend-specialist | Backend | "API", "server", "Node.js", "Express" |
frontend-specialist | Frontend | "React", "UI", "components", "Next.js" |
test-engineer | Testing | "tests", "coverage", "TDD" |
devops-engineer | DevOps | "deploy", "CI/CD", "infrastructure" |
database-architect | Database | "schema", "Prisma", "migrations" |
mobile-developer | Mobile | "React Native", "Flutter", "mobile" |
api-designer | API Design | "REST", "GraphQL", "OpenAPI" |
debugger | Debugging | "bug", "error", "not working" |
explorer-agent | Discovery | "explore", "map", "structure" |
documentation-writer | Documentation | "write docs", "create README", "generate API docs" |
performance-optimizer | Performance | "slow", "optimize", "profiling" |
project-planner | Planning | "plan", "roadmap", "milestones" |
seo-specialist | SEO | "SEO", "meta tags", "search ranking" |
game-developer | Game Development | "game", "Unity", "Godot", "Phaser" |
These work alongside custom agents:
| Agent | Model | Purpose |
|---|---|---|
| Explore | Haiku | Fast read-only codebase search |
| Plan | Sonnet | Research during plan mode |
| General-purpose | Sonnet | Complex multi-step modifications |
Use Explore for quick searches, custom agents for domain expertise.
After all agents complete, synthesize:
## Orchestration Synthesis
### Task Summary
[What was accomplished]
### Agent Contributions
| Agent | Finding |
|-------|---------|
| security-auditor | Found X |
| backend-specialist | Identified Y |
### Consolidated Recommendations
1. **Critical**: [Issue from Agent A]
2. **Important**: [Issue from Agent B]
3. **Nice-to-have**: [Enhancement from Agent C]
### Action Items
- [ ] Fix critical security issue
- [ ] Refactor API endpoint
- [ ] Add missing tests
Socratic questioning protocol + user communication. MANDATORY for complex requests, new features, or unclear requirements. Includes progress reporting and error handling.
Token-efficient code review using Tree-sitter AST graphs and MCP. Reduces AI assistant token usage by 6.8–49x by computing blast radius of changes instead of reading entire codebases. Uses SQLite graph database for structural analysis.
Persistent cross-session memory management. Enables agents to remember user preferences, project conventions, and past decisions across different sessions using a structured MEMORY.md index and topic files.
Reduce complexity of over-engineered code. Identify unnecessary abstractions, remove dead code, flatten deep nesting, and simplify logic while preserving behavior.
API design principles and decision-making. REST vs GraphQL vs tRPC selection, response formats, versioning, pagination.
Main application building orchestrator. Creates full-stack applications from natural language requests. Determines project type, selects tech stack, coordinates agents.