Files
Claude-Code-Workflow/.codex/skills/team-lifecycle-v4/SKILL.md
catlog22 88ea7fc6d7 refactor: deep Codex v4 API conversion for all 20 team skills
Upgrade all team-* skills from mechanical v3→v4 API renames to deep
v4 tool integration with skill-adaptive patterns:

- list_agents: health checks in handleResume, cleanup verification in
  handleComplete, added to allowed-tools and coordinator toolbox
- Named targeting: task_name uses task-id (e.g. EXPLORE-001) instead
  of generic <role>-worker, enabling send_message/assign_task by name
- Message semantics: send_message for supplementary cross-agent context
  vs assign_task for triggering work, with skill-specific examples
- Model selection: per-role reasoning_effort guidance matching each
  skill's actual roles (not generic boilerplate)
- timeout_ms: added to all wait_agent calls, timed_out handling in
  all 18 monitor.md files
- Skill-adaptive v4 sections: ultra-analyze N-parallel coordination,
  lifecycle-v4 supervisor assign_task/send_message distinction,
  brainstorm ideator parallel patterns, iterdev generator-critic loops,
  frontend-debug iterative debug assign_task, perf-opt benchmark
  context sharing, executor lightweight trimmed v4, etc.

60 files changed across 20 team skills (SKILL.md, monitor.md, role.md)

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-03-27 22:25:32 +08:00

12 KiB

name, description, allowed-tools
name description allowed-tools
team-lifecycle-v4 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". spawn_agent(*), wait_agent(*), send_message(*), assign_task(*), close_agent(*), list_agents(*), report_agent_job_result(*), Read(*), Write(*), Edit(*), Bash(*), Glob(*), Grep(*), request_user_input(*)

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            assign_task-driven
                    |                     |
                    +-- wait_agent --------+
                              |
                         collect results

Role Registry

Role Path Prefix Inner Loop
coordinator roles/coordinator/role.md -- --
analyst roles/analyst/role.md RESEARCH-* false
writer roles/writer/role.md DRAFT-* true
planner roles/planner/role.md PLAN-* true
executor roles/executor/role.md IMPL-* true
tester roles/tester/role.md TEST-* false
reviewer roles/reviewer/role.md REVIEW-, QUALITY-, IMPROVE-* false
supervisor roles/supervisor/role.md CHECKPOINT-* false

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, assign_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-<slug>-<date>/
  • 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_context: false,
  items: [
    { type: "text", text: `## 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).` },

    { type: "text", text: `## Task Context
task_id: <task-id>
title: <task-title>
description: <task-description>
pipeline_phase: <pipeline-phase>` },

    { type: "text", text: `## Upstream Context
<prev_context>` }
  ]
})

Supervisor Spawn Template

Supervisor is a resident agent (independent from team_worker). Spawned once during session init, woken via assign_task for each CHECKPOINT task.

Spawn (Phase 2 -- once per session)

supervisorId = spawn_agent({
  agent_type: "team_supervisor",
  task_name: "supervisor",
  fork_context: false,
  items: [
    { type: "text", text: `## 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 assign_task.` }
  ]
})

Wake (per CHECKPOINT task)

assign_task({
  target: "supervisor",
  items: [
    { type: "text", text: `## Checkpoint Request
task_id: <CHECKPOINT-NNN>
scope: [<upstream-task-ids>]
pipeline_progress: <done>/<total> tasks completed` }
  ]
})
wait_agent({ targets: ["supervisor"], timeout_ms: 300000 })

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_context: false,
  model: "<model-override>",
  reasoning_effort: "<effort-level>",
  items: [...]
})

Wave Execution Engine

For each wave in the pipeline:

  1. Load state -- Read <session>/tasks.json, filter tasks for current wave
  2. Skip failed deps -- Mark tasks whose dependencies failed/skipped as skipped
  3. Build upstream context -- For each task, gather findings from context_from tasks via tasks.json and discoveries/{id}.json
  4. Separate task types -- Split into regular tasks and CHECKPOINT tasks
  5. Spawn regular tasks -- For each regular task, call spawn_agent({ agent_type: "team_worker", items: [...] }), collect agent IDs
  6. Wait -- wait_agent({ targets: [...], timeout_ms: 900000 })
  7. Collect results -- Read discoveries/{task_id}.json for each agent, update tasks.json status/findings/error, then close_agent({ target }) each worker
  8. Execute checkpoints -- For each CHECKPOINT task, assign_task to supervisor, wait_agent, read checkpoint report from artifacts/, parse verdict
  9. Handle block -- If verdict is block, prompt user via request_user_input with options: Override / Revise upstream / Abort
  10. 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 assign_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 assign_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", items: [...] }) -- queue planning context to running implementer
  • assign_task({ target: "supervisor", items: [...] }) -- 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:

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-<slug>-<date>/
├── 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"