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>

.codex/skills/team-planex/SKILL.md

@@ -1,7 +1,7 @@
 ---
 name: team-planex
 description: Unified team skill for plan-and-execute pipeline. Pure router — coordinator always. Beat model is coordinator-only in monitor.md. Triggers on "team planex".
-allowed-tools: spawn_agent(*), wait_agent(*), send_input(*), close_agent(*), report_agent_job_result(*), request_user_input(*), Read(*), Write(*), Edit(*), Bash(*), Glob(*), Grep(*), mcp__ccw-tools__team_msg(*)
+allowed-tools: spawn_agent(*), wait_agent(*), send_message(*), assign_task(*), close_agent(*), list_agents(*), report_agent_job_result(*), request_user_input(*), Read(*), Write(*), Edit(*), Bash(*), Glob(*), Grep(*), mcp__ccw-tools__team_msg(*)
 ---
 
 # Team PlanEx
@@ -53,7 +53,8 @@ Before calling ANY tool, apply this check:
 
 | Tool Call | Verdict | Reason |
 |-----------|---------|--------|
-| `spawn_agent`, `wait_agent`, `close_agent`, `send_input` | ALLOWED | Orchestration |
+| `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 |
@@ -83,6 +84,8 @@ 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>
@@ -107,7 +110,27 @@ pipeline_phase: <pipeline-phase>` },
 })
 ```
 
-After spawning, use `wait_agent({ ids: [...], timeout_ms: 900000 })` to collect results, then `close_agent({ id })` each worker.
+After spawning, use `wait_agent({ targets: [...], timeout_ms: 900000 })` to collect results, then `close_agent({ target })` each worker.
+
+
+### Model Selection Guide
+
+| Role | model | reasoning_effort | Rationale |
+|------|-------|-------------------|-----------|
+| Planner (PLAN-*) | (default) | high | Solution planning requires deep code analysis |
+| Executor (EXEC-*) | (default) | high | Code implementation needs precision |
+
+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: [...]
+})
+```
 
 ## User Commands
 
@@ -140,6 +163,45 @@ After spawning, use `wait_agent({ ids: [...], timeout_ms: 900000 })` to collect
 
 - [specs/pipelines.md](specs/pipelines.md) — Pipeline definitions, task metadata registry, execution method selection
 
+## v4 Agent Coordination
+
+### Message Semantics
+
+| Intent | API | Example |
+|--------|-----|---------|
+| Send plan updates to running executor | `send_message` | Queue planner solution details to EXEC-* workers |
+| Not used in this skill | `assign_task` | No resident agents -- planner and executor are one-shot |
+| Check running agents | `list_agents` | Verify planner/executor health during resume |
+
+### Two-Phase Pipeline Pattern
+
+Plan-and-execute is a **Two-Phase pattern**: planner creates solution plans (PLAN-*), then coordinator spawns executors (EXEC-*) for each planned issue. The planner may dynamically create EXEC-* task entries in tasks.json.
+
+```
+// Phase 1: Planner runs, creates EXEC-* tasks in tasks.json
+spawn_agent({ agent_type: "team_worker", task_name: "PLAN-001", ... })
+wait_agent({ targets: ["PLAN-001"], timeout_ms: 900000 })
+// Phase 2: Executors run per-issue, may run in sequence or parallel
+// Inner loop: planner/executor handle multiple tasks internally
+```
+
+### 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
+```
+
+### Named Agent Targeting
+
+Workers are spawned with `task_name: "<task-id>"` enabling direct addressing:
+- `send_message({ target: "EXEC-001", items: [...] })` -- queue plan solution to running executor
+- `close_agent({ target: "PLAN-001" })` -- cleanup by name after completion
+
 ## Error Handling
 
 | Scenario | Resolution |