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parallel-agents

parallel-agents

github.com/parcadeiAgent workflows3.9k513 installsUpdated 40w ago
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About parallel-agents

parallel-agents is a skill for coordinating multiple agents in parallel while limiting context growth. It recommends background execution, file-based status and results, and batches of no more than 15 agents.

Key features

  1. 01Run agents in the background
  2. 02Confirm completion through status files
  3. 03Append status instead of overwriting
  4. 04Save detailed findings in separate directories
  5. 05Limit each batch to 15 agents

Use cases

  1. 01Coordinate parallel agents without loading their full outputs
  2. 02Track batch completion with a shared status file
  3. 03Preserve detailed research findings for later use

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/parcadei/continuous-claude-v3 --skill parallel-agents

After installing, type/parallel-agentsin Claude Code to confirm it loaded.

Details

From the project README

Parallel Agent Orchestration

When launching multiple agents in parallel, follow this pattern to avoid context bloat.

Core Principles

  1. No TaskOutput calls - TaskOutput returns full agent output, bloating context
  2. Run in background - Always use run_in_background: true
  3. File-based confirmation - Agents write status to files, not return values
  4. 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: true always
  • 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

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