pthread

name: pthread description: "Use when facing multiple independent tasks that can run concurrently. Scales compute by spawning multiple agents in parallel. Triggered by 'parallelize', 'run these in parallel', or when independent subtasks are detected." context: fork

Parallel Thread Execution (P-Thread)

Scale compute by spawning multiple agents in parallel. This skill enables autonomous horizontal scaling for independent work.

Core Principle

When facing independent tasks, don't execute sequentially. Spawn parallel sub-agents and aggregate results. The system should make this decision autonomously based on task structure.

When to Auto-Parallelize

The system should automatically parallelize when:

1. Multiple independent searches - Finding different types of files/patterns
2. Multi-file analysis - Reviewing separate modules that don't interact
3. Exploration tasks - Investigating multiple hypotheses simultaneously
4. Fusion patterns - Getting multiple perspectives on the same problem

Implementation Pattern

Using Claude Code Task Tool

# Spawn parallel sub-agents for independent work
# Each agent gets focused context and clear success criteria

# Example: Parallel codebase exploration
tasks = [
    {"agent": "Explore", "prompt": "Find all authentication handlers"},
    {"agent": "Explore", "prompt": "Find all API endpoint definitions"},
    {"agent": "Explore", "prompt": "Find all database models"},
]

# Launch all simultaneously - don't wait for sequential completion
for task in tasks:
    spawn_subagent(task, run_in_background=True)

# Aggregate results when all complete

Decision Logic

IF task has independent subtasks:
    - Identify subtask boundaries
    - Spawn sub-agent per subtask (using Task tool)
    - Set run_in_background=True for true parallelism
    - Aggregate results

IF exploring multiple approaches:
    - Spawn N agents with same goal, different strategies
    - Compare outputs
    - Select best or synthesize

IF task is inherently sequential:
    - Do NOT parallelize
    - Execute as single thread

Auto-Parallelization Triggers

The system should recognize these patterns and auto-parallelize:

Aggregation Strategies

After parallel completion:

1. Merge: Combine all findings into unified result
2. Best-of-N: Select highest quality output
3. Synthesize: Final pass to create coherent summary
4. Deduplicate: Remove redundant findings across agents

Metrics to Track

thread_metrics = {
    "parallel_agents_spawned": N,
    "total_tool_calls": sum(agent.tool_calls for agent in agents),
    "wall_clock_time": actual_duration,
    "sequential_equivalent_time": sum(agent.duration for agent in agents),
    "speedup_factor": sequential_time / wall_clock_time
}

Integration with Ralph Wiggum Pattern

P-Threads should not stop until aggregation is complete:

1. Spawn all parallel agents
2. Wait for ALL to complete (no partial results)
3. Aggregate findings
4. If aggregation reveals gaps -> spawn additional agents
5. Only complete when unified result meets quality bar

Example: Autonomous Parallel Review

Input: "Review the authentication system"

System Decision: This spans multiple files/concerns -> parallelize

Spawned Agents:
- Agent 1: Security patterns (auth handlers, token validation)
- Agent 2: Performance (query patterns, caching)
- Agent 3: Code quality (style, tests, docs)
- Agent 4: Architecture (dependencies, interfaces)

Aggregation: Synthesize into unified review with sections

Output: Complete review document, not partial results

Key Insight

"Every time a new powerful model is released, you don't just get the benefits one time. You get it N times if and only if you're delegating to multiple agents."

The system should be making these parallelization decisions autonomously. Valor (the persona) doesn't need to think about threading - the underlying system handles it.

Thread Type: P-Thread (Parallel) Trigger: Independent subtasks detected Behavior: Autonomous sub-agent spawning Completion: Only when all threads complete and results are aggregated