| name | spawning-plan |
| description | 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. Requires Claude Code with Agent Teams support. Use when this capability is needed. |
Spawning Plan
Design the optimal agent team for the task. Performant, precise, minimal.
Note: This skill requires Claude Code with Agent Teams support (TeamCreate, TaskCreate, SendMessage tools).
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.
-
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
-
Coordination -- "How should agents work together?"
Options: Independent (no messaging) / Team (peer messaging) / Hub-spoke (lead coordinates)
-
Dependencies -- "Work order?"
Options: All parallel / Sequential (A->B->C) / Mixed
-
Models -- "Model allocation: opus (research), sonnet (implementation), haiku (scanning). Adjust?"
Options: As suggested / All opus / All sonnet / Custom
-
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 & Simplify -- spawn agents, then auto-run /simplify on all changes
- Deploy & Save -- spawn agents and save as reusable skill
- Deploy Once -- spawn agents, one-time
- Adjust -- change something (iterate plan)
- Cancel -- abort
Deploy & Simplify -> spawn agents, wait for all to complete, then automatically run /simplify to review and fix code quality, reuse, and efficiency issues across all agent output. Best for implementation tasks.
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: citedy/skills — distributed by TomeVault.
