mcpField
S
spawn

spawn

github.com/alirezarezvaniAgent workflows28.9k1.4k installsUpdated 2d ago
Repository

About spawn

A skill for launching multiple subagents on a task, each in an isolated git worktree. It supports session-based assignments and templates for optimization, refactoring, test writing, and bug fixing.

Key features

  1. 01Launch agents in parallel
  2. 02Use isolated git worktrees
  3. 03Assign session-based tasks
  4. 04Apply task-specific templates
  5. 05Write dispatch instructions

Use cases

  1. 01Explore competing implementation approaches
  2. 02Refactor code and run tests
  3. 03Address test coverage gaps
  4. 04Diagnose and fix bugs

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/alirezarezvani/claude-skills --skill spawn

After installing, type/spawnin Claude Code to confirm it loaded.

Details

From the project README

/hub:spawn — Launch Parallel Agents

Spawn N subagents that work on the same task in parallel, each in an isolated git worktree.

Usage

/hub:spawn                                    # Spawn agents for the latest session
/hub:spawn 20260317-143022                    # Spawn agents for a specific session
/hub:spawn --template optimizer               # Use optimizer template for dispatch prompts
/hub:spawn --template refactorer              # Use refactorer template

Templates

When --template <name> is provided, use the dispatch prompt from ../agenthub/references/agent-templates.md instead of the default prompt below. Available templates:

TemplatePatternUse Case
optimizerEdit → eval → keep/discard → repeat x10Performance, latency, size reduction
refactorerRestructure → test → iterate until greenCode quality, tech debt
test-writerWrite tests → measure coverage → repeatTest coverage gaps
bug-fixerReproduce → diagnose → fix → verifyBug fix with competing approaches

When using a template, replace all {variables} with values from the session config. Assign each agent a different strategy appropriate to the template and task — diverse strategies maximize the value of parallel exploration.

What It Does

  1. Load session config from .agenthub/sessions/{session-id}/config.yaml
  2. For each agent 1..N:
    • Write task assignment to .agenthub/board/dispatch/
    • Build agent prompt with task, constraints, and board write instructions
  3. Launch ALL agents in a single message with multiple Agent tool calls:
Agent(
  prompt: "You are agent-{i} in hub session {session-id}.

Your task: {task}

Read your full assignment at .agenthub/board/dispatch/{seq}-agent-{i}.md

Instructions:
1. Work in your worktree — make changes, run tests, iterate
2. Commit all changes with descriptive messages
3. Write your result summary to .agenthub/board/results/agent-{i}-result.md
   Include: approach taken, files changed, metric if available, confidence level
4. Exit when done

Constraints:
- Do NOT read or modify other agents' work
- Do NOT access .agenthub/board/results/ for other agents
- Commit early and often with descriptive messages
- If you hit a dead end, commit what you have and explain in your result",
  isolation: "worktree"
)
  1. Update session state to running via:
python {skill_path}/scripts/session_manager.py --update {session-id} --state running

Critical Rules

  • All agents in ONE message — spawn all Agent tool calls simultaneously for true parallelism
  • isolation: "worktree" is mandatory — each agent needs its own filesystem
  • Never modify session config after spawn — agents rely on stable configuration
  • Each agent gets a unique board post — dispatch posts are numbered sequentially

After Spawn

Tell the user:

  • {N} agents launched in parallel
  • Each working in an isolated worktree
  • Monitor with /hub:hub-status
  • Evaluate when done with /hub:eval

Comments

Markdown supported