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name: parallelize description: Use when planning parallel execution of independent tasks via subagents or concurrent tool calls; for workflow planning or throughput optimization. allowed-tools: ["read_file", "update_plan"] metadata: short-description: Analyze parallelization opportunities

Parallelization Analysis

Read this reference:

• references/parallelization.md - Concurrent execution patterns and strategies

Instructions

Important: This is about parallelizing implementation work (spawning subagents, concurrent tool calls), NOT writing parallel code.

Analysis Steps

1. Identify independent work units

   • Which tasks have no dependencies?
   • What can be done simultaneously?
   • Where are the blocking dependencies?

2. Map dependency graph

   • Draw (mentally or explicitly) dependencies
   • Identify critical path (longest sequential chain)
   • Find parallelizable branches

3. Calculate potential speedup

   • How much work can run concurrently?
   • What's the theoretical maximum speedup?
   • What are the practical constraints?

4. Design execution strategy

   • Spawn multiple subagents for independent work
   • Use parallel tool calls where possible
   • Batch independent operations
   • Minimize sequential bottlenecks

Parallelization Patterns

Pattern 1: Independent Reads

CopyRead file A, Read file B, Read file C in parallel
→ Process results sequentially if needed

Pattern 2: Independent Analysis

CopyAnalyze module A, Analyze module B in parallel
→ Combine results

Pattern 3: Pipeline Parallelism

CopyStage 1 (Task A) → Stage 2 (Tasks B, C in parallel) → Stage 3 (Task D)

Dispatching Parallel Work (Codex-compatible)

When tasks are independent, batch tool calls in a single message or use multi_tool_use.parallel for concurrent reads/analysis. Keep scopes narrow and independent to avoid conflicts.

Per AGENT.md

"Always use subagents wherever possible to speed up work" - apply this principle throughout.

Arguments

Work to analyze for parallelization: ${ARGUMENTS}