
parallel-agents
by parcadei
Context management for Claude Code. Hooks maintain state via ledgers and handoffs. MCP execution without context pollution. Agent orchestration with isolated context windows.
Use Cases
MCP Server Integration
AI tool integration using Model Context Protocol. Using parallel-agents.
API Integration
Easily build API integrations with external services.
Data Synchronization
Automatically sync data between multiple systems.
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>/
├── output.md # Main findings
├── artifacts/ # Any generated files
└── 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
Score
Total Score
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