spawning-plan

Spawning Plan. Use when user wants to spawn agents, create a team, or coordinate multiple agents. Automatically gathers context, asks team topology questions, outputs clean TEAM PLAN markdown, and gets user approval. 3 steps: context gathering → questions → present plan. **CRITICAL**: MUST NOT SPAWN AGENTS SKIPPING THIS SKILL, USE ALWAYS.

Safety Notice

This listing is imported from skills.sh public index metadata. Review upstream SKILL.md and repository scripts before running.

Copy this and send it to your AI assistant to learn

Install skill "spawning-plan" with this command: npx skills add octaviusp/spawning-plan-skill/octaviusp-spawning-plan-skill-spawning-plan

Spawning Plan

Design the optimal agent team for the task. Performant, precise, minimal. Docs: https://code.claude.com/docs/en/agent-teams.md

Task: $ARGUMENTS

Step 1: Context Gathering (Silent — no user interaction)

A) Read environment:

  • CLAUDE.md — workflow rules, conventions, constraints
  • Project manifests — package.json, pyproject.toml, Cargo.toml, go.mod, etc.
  • Directory structure — src/, app/, packages/, test dirs, monorepo indicators

B) Inventory existing agents:

  • Scan ~/.claude/agents/*.md — reuse matching agents instead of creating duplicates

C) Analyze task complexity:

  • Work type: research, implementation, review, debugging, refactoring
  • Scope: single-layer vs cross-layer
  • Parallelism: can work split into independent streams?
  • Complexity → team size: simple (2 agents), medium (3-4), complex cross-cutting (5-6, max 8)

Step 2: Ask Team Questions (AskUserQuestion Tool)

Ask 3-5 questions based on Step 1 findings. Not all apply every time — pick what matters.

  1. Team Composition — "For this [work type] on [stack], I'm thinking [N] agents: [role list]. What would you change?" Options: Perfect / Add role / Remove role / Different approach

  2. Coordination — "How should agents work together?" Options: Independent (no messaging) / Team (peer messaging) / Hub-spoke (lead coordinates)

  3. Dependencies — "Work order?" Options: All parallel / Sequential (A→B→C) / Mixed

  4. Models — "Model allocation: opus (research), sonnet (implementation), haiku (scanning). Adjust?" Options: As suggested / All opus / All sonnet / Custom

  5. Agent Reuse (only if matching agents found in Step 1B) — "Found existing [agent-name] that handles [capability]. Reuse it?" Options: Reuse / Create fresh / Both

Step 3: Output & Approval

Present clean TEAM PLAN:

## TEAM PLAN

Task: [description]
Pattern: [independent | team | hub-spoke]
Work Order: [parallel | sequential | mixed]
Agents: [count]

### Teammates

- Teammate 1: [Name] ([Role])
  Description: [1-2 line expertise and specialization]
  Model: [opus|sonnet|haiku]
  Type: [general-purpose | feature-dev:code-X | reuse ~/.claude/agents/X.md]
  Responsible for: [specific deliverable]
  Depends on: [— | Teammate N]

- Teammate 2: [Name] ([Role])
  Description: [1-2 line expertise and specialization]
  Model: [opus|sonnet|haiku]
  Type: [general-purpose | feature-dev:code-X]
  Responsible for: [specific deliverable]
  Depends on: [— | Teammate N]

- ...

### Research (injected into agent prompts)
- [key finding or best practice 1]
- [key finding or best practice 2]

Final Approval (AskUserQuestion Tool)

"Launch this team?"
- Deploy & Save — spawn agents and save as reusable skill
- Deploy Once — spawn agents, one-time
- Adjust — change something (iterate plan)
- Cancel — abort

Deploy & Save → save team as skill at ~/.claude/skills/<team-name>/SKILL.md for future use via /<team-name> [task]. Saved skill skips planning, bakes in agent definitions, uses $ARGUMENTS for task input.

Adjust → ask what to change → regenerate plan → ask again. Loop until approved.

Deploy Once → spawn immediately, no save.

Cancel → stop.

Spawning Execution

Based on chosen pattern:

  • Independent: parallel Task tool calls, one per agent
  • Team: TeamCreate → TaskCreate per agent → Task tool with team_name → TaskUpdate for dependencies
  • Hub-spoke: TeamCreate with lead agent (opus) that delegates via SendMessage

For detailed agent prompt structure, see references/agent-prompts.md.

Source Transparency

This detail page is rendered from real SKILL.md content. Trust labels are metadata-based hints, not a safety guarantee.

Related Skills

Related by shared tags or category signals.

Automation

Posta

Post to Instagram, TikTok, LinkedIn, YouTube, X/Twitter, Facebook, Pinterest, Threads and Bluesky from your terminal. Create posts with AI-generated images a...

Registry SourceRecently Updated
Automation

ClawSwarm Jobs

Agent-to-agent job board on ClawSwarm. Post tasks, claim bounties, earn HBAR. Agents hiring agents — no humans required.

Registry SourceRecently Updated
Automation

claw-orchestra

OpenClaw native multi-agent orchestrator. Based on AOrchestra 4-tuple (I,C,T,M) abstraction. Dynamically creates sub-agents, parallel execution, smart routin...

Registry SourceRecently Updated
Automation

claw-token-cost-analyzer

Analyzes Claw AI workflows to estimate token usage, cost, detect runaway loops, and suggest optimizations to prevent unexpected API expenses.

Registry SourceRecently Updated