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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>
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catlog22
@@ -1,7 +1,7 @@
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---
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name: team-lifecycle-v4
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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".
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allowed-tools: spawn_agent(*), wait_agent(*), send_input(*), close_agent(*), report_agent_job_result(*), Read(*), Write(*), Edit(*), Bash(*), Glob(*), Grep(*), request_user_input(*)
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allowed-tools: 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(*)
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---
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# Team Lifecycle v4
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@@ -29,7 +29,7 @@ Skill(skill="team-lifecycle-v4", args="task description")
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spawn_agent ... spawn_agent
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(team_worker) (team_supervisor)
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per-task resident agent
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lifecycle send_input-driven
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lifecycle assign_task-driven
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+-- wait_agent --------+
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@@ -63,7 +63,8 @@ Before calling ANY tool, apply this check:
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| Tool Call | Verdict | Reason |
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|-----------|---------|--------|
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| `spawn_agent`, `wait_agent`, `close_agent`, `send_input` | ALLOWED | Orchestration |
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| `spawn_agent`, `wait_agent`, `close_agent`, `send_message`, `assign_task` | ALLOWED | Orchestration |
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| `list_agents` | ALLOWED | Agent health check |
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| `request_user_input` | ALLOWED | User interaction |
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| `mcp__ccw-tools__team_msg` | ALLOWED | Message bus |
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| `Read/Write` on `.workflow/.team/` files | ALLOWED | Session state |
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@@ -94,6 +95,8 @@ Coordinator spawns workers using this template:
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```
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spawn_agent({
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agent_type: "team_worker",
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task_name: "<task-id>",
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fork_context: false,
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items: [
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{ type: "text", text: `## Role Assignment
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role: <role>
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@@ -120,13 +123,15 @@ pipeline_phase: <pipeline-phase>` },
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## Supervisor Spawn Template
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Supervisor is a **resident agent** (independent from team_worker). Spawned once during session init, woken via send_input for each CHECKPOINT task.
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Supervisor is a **resident agent** (independent from team_worker). Spawned once during session init, woken via assign_task for each CHECKPOINT task.
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### Spawn (Phase 2 -- once per session)
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```
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supervisorId = spawn_agent({
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agent_type: "team_supervisor",
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task_name: "supervisor",
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fork_context: false,
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items: [
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{ type: "text", text: `## Role Assignment
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role: supervisor
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@@ -137,7 +142,7 @@ requirement: <task-description>
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Read role_spec file (<skill_root>/roles/supervisor/role.md) to load checkpoint definitions.
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Init: load baseline context, report ready, go idle.
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Wake cycle: orchestrator sends checkpoint requests via send_input.` }
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Wake cycle: orchestrator sends checkpoint requests via assign_task.` }
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]
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})
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```
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@@ -145,8 +150,8 @@ Wake cycle: orchestrator sends checkpoint requests via send_input.` }
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### Wake (per CHECKPOINT task)
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```
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send_input({
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id: supervisorId,
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assign_task({
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target: "supervisor",
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items: [
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{ type: "text", text: `## Checkpoint Request
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task_id: <CHECKPOINT-NNN>
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@@ -154,13 +159,38 @@ scope: [<upstream-task-ids>]
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pipeline_progress: <done>/<total> tasks completed` }
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]
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})
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wait_agent({ ids: [supervisorId], timeout_ms: 300000 })
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wait_agent({ targets: ["supervisor"], timeout_ms: 300000 })
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```
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### Shutdown (pipeline complete)
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```
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close_agent({ id: supervisorId })
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close_agent({ target: "supervisor" })
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```
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### Model Selection Guide
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| Role | model | reasoning_effort | Rationale |
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|------|-------|-------------------|-----------|
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| Analyst (RESEARCH-*) | (default) | medium | Read-heavy exploration, less reasoning needed |
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| Writer (DRAFT-*) | (default) | high | Spec writing requires precision and completeness |
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| Planner (PLAN-*) | (default) | high | Architecture decisions need full reasoning |
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| Executor (IMPL-*) | (default) | high | Code generation needs precision |
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| Tester (TEST-*) | (default) | high | Test generation requires deep code understanding |
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| Reviewer (REVIEW-*, QUALITY-*, IMPROVE-*) | (default) | high | Deep analysis for quality assessment |
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| Supervisor (CHECKPOINT-*) | (default) | medium | Gate checking, report aggregation |
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Override model/reasoning_effort in spawn_agent when cost optimization is needed:
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```
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spawn_agent({
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agent_type: "team_worker",
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task_name: "<task-id>",
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fork_context: false,
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model: "<model-override>",
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reasoning_effort: "<effort-level>",
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items: [...]
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})
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```
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## Wave Execution Engine
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@@ -172,9 +202,9 @@ For each wave in the pipeline:
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3. **Build upstream context** -- For each task, gather findings from `context_from` tasks via tasks.json and `discoveries/{id}.json`
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4. **Separate task types** -- Split into regular tasks and CHECKPOINT tasks
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5. **Spawn regular tasks** -- For each regular task, call `spawn_agent({ agent_type: "team_worker", items: [...] })`, collect agent IDs
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6. **Wait** -- `wait_agent({ ids: [...], timeout_ms: 900000 })`
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7. **Collect results** -- Read `discoveries/{task_id}.json` for each agent, update tasks.json status/findings/error, then `close_agent({ id })` each worker
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8. **Execute checkpoints** -- For each CHECKPOINT task, `send_input` to supervisor, `wait_agent`, read checkpoint report from `artifacts/`, parse verdict
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6. **Wait** -- `wait_agent({ targets: [...], timeout_ms: 900000 })`
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7. **Collect results** -- Read `discoveries/{task_id}.json` for each agent, update tasks.json status/findings/error, then `close_agent({ target })` each worker
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8. **Execute checkpoints** -- For each CHECKPOINT task, `assign_task` to supervisor, `wait_agent`, read checkpoint report from `artifacts/`, parse verdict
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9. **Handle block** -- If verdict is `block`, prompt user via `request_user_input` with options: Override / Revise upstream / Abort
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10. **Persist** -- Write updated state to `<session>/tasks.json`
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@@ -189,6 +219,39 @@ For each wave in the pipeline:
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| `recheck` | Re-run quality check |
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| `improve [dimension]` | Auto-improve weakest dimension |
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## v4 Agent Coordination
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### Message Semantics
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| Intent | API | Example |
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|--------|-----|---------|
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| Queue supplementary info (don't interrupt) | `send_message` | Send planning results to running implementers |
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| Wake resident supervisor for checkpoint | `assign_task` | Trigger CHECKPOINT-* evaluation on supervisor |
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| Supervisor reports back to coordinator | `send_message` | Supervisor sends checkpoint verdict as supplementary info |
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| Check running agents | `list_agents` | Verify agent + supervisor health during resume |
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**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.
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### Agent Health Check
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Use `list_agents({})` in handleResume and handleComplete:
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```
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// Reconcile session state with actual running agents
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const running = list_agents({})
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// Compare with tasks.json active_agents
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// Reset orphaned tasks (in_progress but agent gone) to pending
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// ALSO check supervisor: if supervisor missing but CHECKPOINT tasks pending -> respawn
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```
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### Named Agent Targeting
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Workers are spawned with `task_name: "<task-id>"` enabling direct addressing:
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- `send_message({ target: "IMPL-001", items: [...] })` -- queue planning context to running implementer
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- `assign_task({ target: "supervisor", items: [...] })` -- wake supervisor for checkpoint
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- `close_agent({ target: "IMPL-001" })` -- cleanup regular worker by name
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- `close_agent({ target: "supervisor" })` -- shutdown supervisor at pipeline end
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## Completion Action
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When pipeline completes, coordinator presents:
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