
Parallel Agents
- 489 installs
- 3.9k repo stars
- Updated January 26, 2026
- parcadei/continuous-claude-v3
parallel-agents is a Claude Code orchestration skill that runs multiple Claude agents simultaneously on partitioned tasks for developers who need faster parallel exploration without blowing the parent session context.
About
parallel-agents is a parcadei/continuous-claude-v3 skill for parallel agent orchestration in Claude Code. It mandates `run_in_background: true`, forbids TaskOutput calls that return full agent transcripts into parent context, and uses file-based confirmation where agents append completion status to shared files instead of streaming large return values. The pattern supports simple confirmation batches and scalable parallel work across repo areas. Developers invoke parallel-agents when a large refactor, multi-module feature, or broad exploration can be split across concurrent agents while keeping the orchestrator context lean and recoverable from disk artifacts.
- Spawns parallel Claude agents that work independently on subtasks
- Merges outputs with conflict detection and synthesis
- Reduces total wall-clock time versus sequential prompting
- Works with any complex coding, research or planning request
- Hard-gate: always review merged result before accepting
Parallel Agents by the numbers
- 489 all-time installs (skills.sh)
- +3 installs in the week ending Aug 4, 2026 (Skillselion tracking)
- Ranked #1,793 of 16,546 AI & Agent Building skills by installs in the Skillselion catalog
- Data as of Aug 5, 2026 (Skillselion catalog sync)
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| Installs | 489 |
|---|---|
| repo stars | ★ 3.9k |
| Last updated | January 26, 2026 |
| Repository | parcadei/continuous-claude-v3 ↗ |
How do you run parallel Claude agents safely?
Run multiple Claude agents simultaneously on different parts of a task for faster iteration and broader exploration.
Who is it for?
Claude Code users splitting large tasks across concurrent subagents who hit context limits from TaskOutput or inline agent returns.
Skip if: Single-threaded bug fixes or tasks small enough for one agent without orchestration overhead.
When should I use this skill?
User wants multiple agents in parallel, background agent runs, or file-based agent status coordination.
What you get
Background agent processes, append-only shared status files, and orchestration rules that preserve parent context budget.
- Parallel agent launch patterns
- Shared status file conventions
Files
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 agentsMonitoring
# 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.txtBatch 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.txtCheck 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 2025Related skills
FAQ
Why does parallel-agents ban TaskOutput?
parallel-agents bans TaskOutput because it returns full agent output into the parent session, bloating context. Agents instead append status to shared files while running with run_in_background: true for lean orchestration.
How do parallel agents report completion?
parallel-agents uses file-based confirmation. Each agent writes or appends completion markers to a shared status file on disk rather than returning large payloads to the parent Claude session.