| name | team-lifecycle-v4 |
| description | Full lifecycle team skill with clean architecture. SKILL.md is a universal router — all roles read it. Beat model is coordinator-only. Structure is roles/ + specs/ + templates/. Triggers on "team lifecycle v4". |
| allowed-tools | spawn_agent(*), wait_agent(*), send_message(*), followup_task(*), close_agent(*), list_agents(*), report_agent_job_result(*), Read(*), Write(*), Edit(*), Bash(*), Glob(*), Grep(*), request_user_input(*), mcp__ccw-tools__team_msg(*) |
Team Lifecycle v4
Orchestrate multi-agent software development: specification -> planning -> implementation -> testing -> review.
Architecture
Skill(skill="team-lifecycle-v4", args="task description")
|
SKILL.md (this file) = Router
|
+--------------+--------------+
| |
no --role flag --role <name>
| |
Coordinator Worker
roles/coordinator/role.md roles/<name>/role.md
|
+-- analyze -> dispatch -> spawn -> wait -> collect
|
+--------+---+--------+
v v v
spawn_agent ... spawn_agent
(team_worker) (team_supervisor)
per-task resident agent
lifecycle followup_task-driven
| |
+-- wait_agent --------+
|
collect results
Role Registry
Role Router
Parse $ARGUMENTS:
- Has
--role <name> -> Read roles/<name>/role.md, execute Phase 2-4
- No
--role -> roles/coordinator/role.md, execute entry router
Delegation Lock
Coordinator is a PURE ORCHESTRATOR. It coordinates, it does NOT do.
Before calling ANY tool, apply this check:
| Tool Call | Verdict | Reason |
|---|
spawn_agent, wait_agent, close_agent, send_message, followup_task | ALLOWED | Orchestration |
list_agents | ALLOWED | Agent health check |
request_user_input | ALLOWED | User interaction |
mcp__ccw-tools__team_msg | ALLOWED | Message bus |
Read/Write on .workflow/.team/ files | ALLOWED | Session state |
Read on roles/, commands/, specs/, templates/ | ALLOWED | Loading own instructions |
Read/Grep/Glob on project source code | BLOCKED | Delegate to worker |
Edit on any file outside .workflow/ | BLOCKED | Delegate to worker |
Bash("ccw cli ...") | BLOCKED | Only workers call CLI |
Bash running build/test/lint commands | BLOCKED | Delegate to worker |
If a tool call is BLOCKED: STOP. Create a task, spawn a worker.
No exceptions for "simple" tasks. Even a single-file read-and-report MUST go through spawn_agent.
Shared Constants
- Session prefix:
TLV4
- Session path:
.workflow/.team/TLV4-<date>-<slug>/
- State file:
<session>/tasks.json
- Discovery files:
<session>/discoveries/{task_id}.json
- CLI tools:
ccw cli --mode analysis (read-only), ccw cli --mode write (modifications)
Worker Spawn Template
Coordinator spawns workers using this template:
spawn_agent({
agent_type: "team_worker",
task_name: "<task-id>",
fork_turns: "none",
message: `## Role Assignment
role: <role>
role_spec: <skill_root>/roles/<role>/role.md
session: <session-folder>
session_id: <session-id>
requirement: <task-description>
inner_loop: <true|false>
Read role_spec file (<skill_root>/roles/<role>/role.md) to load Phase 2-4 domain instructions.
Execute built-in Phase 1 (task discovery) -> role Phase 2-4 -> built-in Phase 5 (report).
## Task Context
task_id: <task-id>
title: <task-title>
description: <task-description>
pipeline_phase: <pipeline-phase>
## Upstream Context
<prev_context>`
})
Supervisor Spawn Template
Supervisor is a resident agent (independent from team_worker). Spawned once during session init, woken via followup_task for each CHECKPOINT task.
Spawn (Phase 2 -- once per session)
supervisorId = spawn_agent({
agent_type: "team_supervisor",
task_name: "supervisor",
fork_turns: "none",
message: `## Role Assignment
role: supervisor
role_spec: <skill_root>/roles/supervisor/role.md
session: <session-folder>
session_id: <session-id>
requirement: <task-description>
Read role_spec file (<skill_root>/roles/supervisor/role.md) to load checkpoint definitions.
Init: load baseline context, report ready, go idle.
Wake cycle: orchestrator sends checkpoint requests via followup_task.`
})
Wake (per CHECKPOINT task)
followup_task({
target: "supervisor",
message: `## Checkpoint Request
task_id: <CHECKPOINT-NNN>
scope: [<upstream-task-ids>]
pipeline_progress: <done>/<total> tasks completed`
})
wait_agent({ timeout_ms: 1800000 }) // 30 min — apply timeout cascade if timed_out
Shutdown (pipeline complete)
close_agent({ target: "supervisor" })
Model Selection Guide
| Role | model | reasoning_effort | Rationale |
|---|
| Analyst (RESEARCH-*) | (default) | medium | Read-heavy exploration, less reasoning needed |
| Writer (DRAFT-*) | (default) | high | Spec writing requires precision and completeness |
| Planner (PLAN-*) | (default) | high | Architecture decisions need full reasoning |
| Executor (IMPL-*) | (default) | high | Code generation needs precision |
| Tester (TEST-*) | (default) | high | Test generation requires deep code understanding |
| Reviewer (REVIEW-, QUALITY-, IMPROVE-*) | (default) | high | Deep analysis for quality assessment |
| Supervisor (CHECKPOINT-*) | (default) | medium | Gate checking, report aggregation |
Override model/reasoning_effort in spawn_agent when cost optimization is needed:
spawn_agent({
agent_type: "team_worker",
task_name: "<task-id>",
fork_turns: "none",
model: "<model-override>",
reasoning_effort: "<effort-level>",
message: "..."
})
Wave Execution Engine
For each wave in the pipeline:
- Load state -- Read
<session>/tasks.json, filter tasks for current wave
- Skip failed deps -- Mark tasks whose dependencies failed/skipped as
skipped
- Build upstream context -- For each task, gather findings from
context_from tasks via tasks.json and discoveries/{id}.json
- Separate task types -- Split into regular tasks and CHECKPOINT tasks
- Spawn regular tasks -- For each regular task, call
spawn_agent({ agent_type: "team_worker", message: "..." }), collect agent IDs
- Wait --
wait_agent({ timeout_ms: 1800000 }) — apply timeout cascade if timed_out
- Collect results -- Read
discoveries/{task_id}.json for each agent, update tasks.json status/findings/error, then close_agent({ target }) each worker
- Execute checkpoints -- For each CHECKPOINT task,
followup_task to supervisor, wait_agent, read checkpoint report from artifacts/, parse verdict
- Handle block -- If verdict is
block, prompt user via request_user_input with options: Override / Revise upstream / Abort
- Persist -- Write updated state to
<session>/tasks.json
User Commands
| Command | Action |
|---|
check / status | View execution status graph |
resume / continue | Advance to next step |
revise <TASK-ID> [feedback] | Revise specific task |
feedback <text> | Inject feedback for revision |
recheck | Re-run quality check |
improve [dimension] | Auto-improve weakest dimension |
v4 Agent Coordination
Message Semantics
| Intent | API | Example |
|---|
| Queue supplementary info (don't interrupt) | send_message | Send planning results to running implementers |
| Wake resident supervisor for checkpoint | followup_task | Trigger CHECKPOINT-* evaluation on supervisor |
| Supervisor reports back to coordinator | send_message | Supervisor sends checkpoint verdict as supplementary info |
| Check running agents | list_agents | Verify agent + supervisor health during resume |
CRITICAL: The supervisor is a resident agent woken via followup_task, NOT send_message. Regular workers complete and are closed; the supervisor persists across checkpoints. See "Supervisor Spawn Template" above.
Agent Health Check
Use list_agents({}) in handleResume and handleComplete:
// Reconcile session state with actual running agents
const running = list_agents({})
// Compare with tasks.json active_agents
// Reset orphaned tasks (in_progress but agent gone) to pending
// ALSO check supervisor: if supervisor missing but CHECKPOINT tasks pending -> respawn
Named Agent Targeting
Workers are spawned with task_name: "<task-id>" enabling direct addressing:
send_message({ target: "IMPL-001", message: "..." }) -- queue planning context to running implementer
followup_task({ target: "supervisor", message: "..." }) -- wake supervisor for checkpoint
close_agent({ target: "IMPL-001" }) -- cleanup regular worker by name
close_agent({ target: "supervisor" }) -- shutdown supervisor at pipeline end
Completion Action
When pipeline completes, coordinator presents:
functions.request_user_input({
questions: [{
question: "Pipeline complete. What would you like to do?",
header: "Completion",
multiSelect: false,
options: [
{ label: "Archive & Clean (Recommended)", description: "Archive session, clean up resources" },
{ label: "Keep Active", description: "Keep session for follow-up work" },
{ label: "Export Results", description: "Export deliverables to target directory" }
]
}]
})
Specs Reference
Session Directory
.workflow/.team/TLV4-<date>-<slug>/
├── tasks.json # Task state (JSON)
├── discoveries/ # Per-task findings ({task_id}.json)
├── spec/ # Spec phase outputs
├── plan/ # Implementation plan
├── artifacts/ # All deliverables
├── wisdom/ # Cross-task knowledge
├── explorations/ # Shared explore cache
└── discussions/ # Discuss round records
Error Handling
| Scenario | Resolution |
|---|
| Unknown command | Error with available command list |
| Role not found | Error with role registry |
| CLI tool fails | Worker fallback to direct implementation |
| Supervisor crash | Respawn with recovery: true, auto-rebuilds from existing reports |
| Supervisor not ready for CHECKPOINT | Spawn/respawn supervisor, wait for ready, then wake |
| Completion action fails | Default to Keep Active |
| Worker timeout | Mark task as failed, continue wave |
| Discovery file missing | Mark task as failed with "No discovery file produced" |