| name | parallel-subagent-orchestration |
| description | Launch multiple specialized Claude agents simultaneously to maximize productivity. Achieves 3-5x speedup on independent tasks like benchmarking, documentation, and analysis. Use when you have multiple independent tasks that each take more than 1 minute. |
Parallel Sub-Agent Orchestration
Launch multiple specialized Claude agents simultaneously to maximize productivity. Achieves 3-5x speedup on independent tasks like benchmarking, documentation, and analysis.
When to use
- Multiple independent tasks that can run concurrently
- Each task takes >1 minute to complete (worthwhile parallelism)
- Tasks produce concrete deliverables (files, reports, code)
- You need specialized agents (Explore for codebase analysis, general-purpose for benchmarks)
- Time-sensitive projects where speed matters
When NOT to use
- Tasks have sequential dependencies (Task B needs Task A's output)
- Quick operations (<30 seconds) - overhead not worth it
- Single task that can't be split
- When you need to iterate based on results (exploratory work)
Instructions
Step 1: Identify Parallelizable Tasks
Good candidates:
- ✅ Running benchmarks + writing docs + security audit (all independent)
- ✅ Analyzing 3 different codebases simultaneously
- ✅ Creating examples + running tests + generating reports
- ✅ Exploring multiple architecture options in parallel
Bad candidates:
- ❌ Read file → analyze → write report (sequential dependency)
- ❌ Five trivial operations (<10 seconds each)
- ❌ Interactive tasks needing user input between steps
Step 2: Choose Agent Types
Available agents:
| Agent Type | Best For | Max Concurrent |
|---|
Explore | Codebase analysis, file searches | 2-3 |
general-purpose | Benchmarks, examples, audits, docs | 3-4 |
Bash | Git operations, command execution | 1-2 |
Plan | Architecture design, planning | 1 |
Selection guide:
Step 3: Craft Clear, Independent Prompts
Each prompt must:
- Be self-contained (no references to other agents)
- Specify concrete deliverable (file path, format)
- Include success criteria (what done looks like)
- Provide context if agent needs background
Step 4: Launch Agents in Single Message
CRITICAL: Use one message with multiple Task tool calls for true parallelism.
Wait for all agents to complete, then review results.
Step 5: Synthesize Results
After agents complete:
- Read all generated files
- Check for conflicts or contradictions
- Integrate findings into summary
- Identify any gaps that need follow-up
Examples
Example 1: Validating a Project
Scenario: Need to validate codebase architecture, performance, examples, and security
Agents launched (4 in parallel):
-
Explore Agent - Codebase architecture analysis
- Analyzed 7 modules, mapped dependencies
- Identified 9 unsafe blocks
- Found hot paths (ring buffer, orderbook, TSC)
- Output: Inline architecture analysis (48KB)
-
General-Purpose Agent - Run benchmarks
- Executed 3 benchmark suites
- Found bug in bundle.rs (array bounds check)
- Results: Exceeded all targets by 12-69x
- Output: BENCHMARKS.md (9.5KB)
-
General-Purpose Agent - Generate examples
- Created 5 production-ready examples
- Each with runnable code + explanations
- Output: examples/README.md (20KB)
-
General-Purpose Agent - Security audit
- Validated all 9 unsafe blocks
- Checked atomic ordering
- Safety score: 9.5/10
- Output: SAFETY_AUDIT.md (25KB)
Results:
- Total time: ~7 minutes (parallel)
- Sequential would take: ~25+ minutes
- Speedup: 3.5x
- Bonus: Benchmark agent found real bug!
Example 2: Analyzing Multiple Codebases
Scenario: Compare 3 different queue implementations
Agents launched (3 in parallel):
Agent 1: Analyze crossbeam-queue
Agent 2: Analyze tokio mpsc
Agent 3: Analyze custom lock-free queue
Each agent produces:
- API surface analysis
- Memory ordering used
- Performance characteristics
- Trade-offs
Result: Comparison table in 10 minutes vs 30+ minutes sequential
Example 3: Documentation Sprint
Scenario: Need README, API docs, examples, and architecture docs
Agents launched (4 in parallel):
Agent 1: Write README.md (getting started, install, basic usage)
Agent 2: Generate API documentation from code
Agent 3: Create examples/ directory with 5 examples
Agent 4: Write ARCHITECTURE.md (system design, data flow)
Result: Complete documentation suite in 15 minutes
Best Practices
✅ Do
- Launch 3-5 agents max - More causes context switching overhead
- Make prompts independent - No cross-references between agents
- Specify file paths - Clear deliverables
- Check results immediately - Agents might misunderstand
- Use Explore for codebase tasks - Specialized for code analysis
- One message, multiple tasks - True parallelism
❌ Don't
- Don't create dependencies - Agent A shouldn't need Agent B's output
- Don't overload - >5 agents gets chaotic
- Don't use for trivial tasks - <30 second operations not worth it
- Don't forget to synthesize - Review all outputs together
- Don't launch sequentially - Multiple separate messages = no parallelism
Common Pitfalls
Pitfall 1: Sequential messages
Wrong:
Message 1: Task tool call for Agent 1
[wait for result]
Message 2: Task tool call for Agent 2
[wait for result]
Correct:
Message 1: Task tool calls for Agent 1, 2, 3, 4 (all in one message)
[all run in parallel]
Pitfall 2: Creating dependencies
Wrong:
Agent 1: Analyze codebase and save to /tmp/analysis.txt
Agent 2: Read /tmp/analysis.txt and write report
- Agent 2 depends on Agent 1 completing first
- This is sequential, not parallel!
Correct:
Agent 1: Analyze codebase and write ANALYSIS.md
Agent 2: Run benchmarks and write BENCHMARKS.md
(No dependency between them)
Pitfall 3: Vague prompts
Wrong:
"Look at the code and tell me about performance"
- What code? Where?
- What aspects of performance?
- What deliverable?
Correct:
"Run all benchmarks in benches/ directory:
1. Execute each with cargo bench
2. Extract P50/P99 latencies
3. Compare against targets in README
4. Create BENCHMARKS.md with results table"
Measuring Success
Indicators it worked:
- ✅ All agents completed successfully
- ✅ Time saved vs sequential (calculate speedup)
- ✅ Deliverables are high quality
- ✅ No contradictions between agents
- ✅ Found insights you would have missed (bonus!)
Indicators it failed:
- ❌ Agents blocked waiting for each other
- ❌ Had to redo work due to vague prompts
- ❌ Results conflicted and needed reconciliation
- ❌ Spent more time managing agents than working
Advanced Patterns
Pattern 1: Explore + Implement
Agent 1 (Explore): Analyze existing authentication system
Agent 2 (Explore): Find all security vulnerabilities
Agent 3 (general-purpose): Draft secure auth implementation plan
Then (after review): Implement based on findings
Pattern 2: Test Coverage Expansion
Agent 1: Create unit tests for module A
Agent 2: Create unit tests for module B
Agent 3: Create integration tests
Agent 4: Create property tests
Result: Full test suite in fraction of time
Pattern 3: Multi-Platform Validation
Agent 1: Build and test on Linux
Agent 2: Build and test on macOS
Agent 3: Build and test on Windows
Agent 4: Run cross-compilation tests
(Note: Requires appropriate build environments)
Integration with Workflows
With Code Review
Before submitting PR:
Agent 1: Run all tests + generate coverage report
Agent 2: Run linters + format checks
Agent 3: Run benchmarks + compare to main
Agent 4: Generate changelog from commits
Results ready in minutes instead of running sequentially
With CI/CD
Parallel agents can pre-validate before pushing:
Agent 1: Security scan
Agent 2: Performance regression check
Agent 3: Documentation check
Agent 4: License compliance
Push only if all pass
Related skills
- plan-first-development - Plan what agents should do
- incremental-validation - Use agents for validation steps
- documentation-while-fresh - Agents generate documentation
Skill Version: 1.0
Last Updated: 2025-01-06
