parallel-execution
Run multiple AI subagents in parallel
Sequential task execution wastes time when independent tasks could run simultaneously. This skill teaches patterns for spawning multiple AI subagents concurrently using the Task tool, enabling dramatic speedups for multi-part projects.
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Using "parallel-execution". Analyze the frontend, backend, and database layers of my project in parallel. Then create a unified summary.
Expected outcome:
- Frontend Analysis: 3 files reviewed. Found 2 component issues, 1 performance concern. Recommendation: Implement memoization.
- Backend Analysis: 5 files reviewed. Found 1 security issue, 2 API improvements needed. Recommendation: Add input validation.
- Database Analysis: 2 files reviewed. Schema is well-designed. Minor optimization suggested for index usage.
- Combined Summary: 4 high-priority items, 3 medium-priority items. Focus on security fix first, then performance optimizations.
Security Audit
SafeThis is a pure documentation skill with no executable code. The static analyzer's pattern detection generated false positives by misidentifying documentation elements (version strings, placeholders, examples) as security issues. Semantic analysis confirms: zero scripts, zero network calls, zero file system access, zero external commands. Safe for publication.
Risk Factors
🌐 Network access (1)
⚙️ External commands (19)
Quality Score
What You Can Build
Implement feature modules
Build multiple independent features simultaneously, like auth, API endpoints, and database schema
Multi-perspective analysis
Run security, performance, testing, and architecture reviews concurrently
Parallel verification
Execute multiple independent checks and tests at the same time for faster feedback
Try These Prompts
I need to analyze this codebase from two perspectives. Launch both analyses in parallel using the Task tool with run_in_background: true. Task 1: Analyze the security implementation Task 2: Analyze the performance patterns Then retrieve both results and summarize findings.
Implement three independent features for my project. Launch all three as parallel subagents: - Subagent 1: User authentication module - Subagent 2: API endpoints for user data - Subagent 3: Database schema for users Make all Task calls in one message. After completion, combine into cohesive implementation.
Analyze three directories in parallel: src/auth, src/api, src/db. For each, review: code quality, security issues, test coverage. Launch three subagents simultaneously. Synthesize findings by severity.
Perform a comprehensive code review using four parallel subagents: - Security reviewer: Check for vulnerabilities - Performance reviewer: Identify bottlenecks - Testing reviewer: Evaluate test coverage - Architecture reviewer: Assess design patterns All in one message. Combine recommendations with priority ordering.
Best Practices
- Launch all parallel tasks in a single assistant message to ensure true concurrency
- Verify tasks are independent before parallelizing - check for file and output dependencies
- Use descriptive task descriptions to easily track which subagent handles what
Avoid
- Splitting Task calls across multiple messages (causes sequential execution)
- Parallelizing tasks that depend on each other's output
- Running multiple tasks that modify the same files (causes conflicts)