Refactor high-complexity React components in Dify frontend. Use when `pnpm analyze-component...
npx skills add namesreallyblank/Clorch --skill "parallel-agents"
Install specific skill from multi-skill repository
# Description
Parallel Agent Orchestration
# SKILL.md
name: parallel-agents
description: Parallel Agent Orchestration
user-invocable: false
Parallel Agent Orchestration
When launching multiple agents in parallel, follow this pattern to avoid context bloat.
Core Principles
- No TaskOutput calls - TaskOutput returns full agent output, bloating context
- Run in background - Always use
run_in_background: true - File-based confirmation - Agents write status to files, not return values
- Append, don't overwrite - Multiple agents can write to same status file
Output Patterns
Simple Confirmation (parallel batch work)
For tasks where agents just need to confirm completion:
# Agent writes to shared status file
echo "COMPLETE: <task-name> - $(date)" >> .claude/cache/<batch-name>-status.txt
- Use
>>to append (not>which overwrites) - Include timestamp for ordering
- One line per agent completion
- Check with:
cat .claude/cache/<batch-name>-status.txt
Detailed Output (research/exploration)
For tasks requiring detailed findings:
.claude/cache/agents/<task-type>/<agent-id>/
βββ output.md # Main findings
βββ artifacts/ # Any generated files
βββ status.txt # Completion confirmation
- Each agent gets own directory
- Full output preserved for later reading
- Status file still used for quick completion check
Task Prompt Template
# Task: <TASK_NAME>
## Your Mission
<clear objective>
## Output
When done, write confirmation:
\`\`\`bash
echo "COMPLETE: <identifier> - $(date)" >> .claude/cache/<batch>-status.txt
\`\`\`
Do NOT return large output. Complete work silently.
Launching Pattern
// Launch all in single message block (parallel)
Task({
description: "Task 1",
prompt: "...",
subagent_type: "general-purpose",
run_in_background: true
})
Task({
description: "Task 2",
prompt: "...",
subagent_type: "general-purpose",
run_in_background: true
})
// ... up to 15 parallel agents
Monitoring
# Check completion status
cat .claude/cache/<batch>-status.txt
# Count completions
wc -l .claude/cache/<batch>-status.txt
# Watch for updates
tail -f .claude/cache/<batch>-status.txt
Batch Size
- Max 15 agents per parallel batch
- Wait for batch to complete before launching next
- Use status file to track which completed
DO
- Use
run_in_background: truealways - Have agents write to status files
- Use append (
>>) not overwrite (>) - Give each agent clear, self-contained instructions
- Include all context in prompt (agents don't share memory)
DON'T
- Call TaskOutput (bloats context)
- Return large outputs from agents
- Launch more than 15 at once
- Rely on agent return values for orchestration
Example: Provider Backfill
# Status file
.claude/cache/provider-backfill-status.txt
# Each agent appends on completion
echo "COMPLETE: anthropic - Thu Jan 2 12:34:56 2025" >> .claude/cache/provider-backfill-status.txt
echo "COMPLETE: openai - Thu Jan 2 12:35:12 2025" >> .claude/cache/provider-backfill-status.txt
Check progress:
cat .claude/cache/provider-backfill-status.txt
# COMPLETE: anthropic - Thu Jan 2 12:34:56 2025
# COMPLETE: openai - Thu Jan 2 12:35:12 2025
# Supported AI Coding Agents
This skill is compatible with the SKILL.md standard and works with all major AI coding agents:
Learn more about the SKILL.md standard and how to use these skills with your preferred AI coding agent.