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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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@@ -1,7 +1,7 @@
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---
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name: team-review
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description: "Unified team skill for code review. 3-role pipeline: scanner, reviewer, fixer. Triggers on team-review."
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allowed-tools: spawn_agent(*), wait_agent(*), send_input(*), close_agent(*), report_agent_job_result(*), request_user_input(*), Read(*), Write(*), Edit(*), Bash(*), Glob(*), Grep(*), mcp__ace-tool__search_context(*)
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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__ace-tool__search_context(*)
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---
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# Team Review
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@@ -53,7 +53,8 @@ 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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@@ -84,6 +85,8 @@ 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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@@ -107,7 +110,28 @@ pipeline_phase: <pipeline-phase>` },
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})
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```
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After spawning, use `wait_agent({ ids: [...], timeout_ms: 900000 })` to collect results, then `close_agent({ id })` each worker.
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After spawning, use `wait_agent({ targets: [...], timeout_ms: 900000 })` to collect results, then `close_agent({ target })` each worker.
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### Model Selection Guide
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| Role | model | reasoning_effort | Rationale |
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|------|-------|-------------------|-----------|
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| Scanner (SCAN-*) | (default) | medium | Toolchain scanning + pattern matching, less reasoning |
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| Reviewer (REV-*) | (default) | high | Deep root cause analysis requires full reasoning |
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| Fixer (FIX-*) | (default) | high | Code modification needs precision |
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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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## User Commands
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@@ -121,6 +145,37 @@ After spawning, use `wait_agent({ ids: [...], timeout_ms: 900000 })` to collect
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| `--dimensions=sec,cor,prf,mnt` | Custom dimensions |
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| `-y` / `--yes` | Skip confirmations |
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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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| Send scan findings to running reviewer | `send_message` | Queue scan results to REV-* as supplementary context |
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| Not used in this skill | `assign_task` | No resident agents -- sequential 3-stage pipeline |
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| Check running agents | `list_agents` | Verify agent health during resume |
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### Pipeline Pattern
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This is a **sequential 3-stage pipeline** (scan -> review -> fix). No parallel phases. Each stage completes before the next starts. The coordinator may skip stages (0 findings -> skip review+fix; user declines fix -> skip fix).
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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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```
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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: "REV-001", items: [...] })` -- queue scan findings to running reviewer
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- `close_agent({ target: "SCAN-001" })` -- cleanup by name after completion
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## Completion Action
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When pipeline completes, coordinator presents:
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@@ -54,6 +54,16 @@ Then STOP.
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## handleResume
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**Agent Health Check** (v4):
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```
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// Verify actual running agents match session state
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const runningAgents = list_agents({})
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// For each active_agent in tasks.json:
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// - If agent NOT in runningAgents -> agent crashed
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// - Reset that task to pending, remove from active_agents
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// This prevents stale agent references from blocking the pipeline
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```
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1. Read tasks.json, check active_agents
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2. No active agents -> handleSpawnNext
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3. Has active agents -> check each status
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@@ -88,6 +98,7 @@ state.tasks[taskId].status = 'in_progress'
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// 2) Spawn worker
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const agentId = spawn_agent({
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agent_type: "team_worker",
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task_name: taskId, // e.g., "SCAN-001" — enables named targeting
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items: [
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{ type: "text", text: `## Role Assignment
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role: ${role}
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@@ -109,12 +120,18 @@ Execute built-in Phase 1 (task discovery) -> role Phase 2-4 -> built-in Phase 5
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// 3) Track agent
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state.active_agents[taskId] = { agentId, role, started_at: now }
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// 4) Wait for completion
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wait_agent({ ids: [agentId] })
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// 5) Collect results
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state.tasks[taskId].status = 'completed'
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delete state.active_agents[taskId]
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// 4) Wait for completion — use task_name for stable targeting (v4)
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const waitResult = wait_agent({ targets: [taskId], timeout_ms: 900000 })
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if (waitResult.timed_out) {
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state.tasks[taskId].status = 'timed_out'
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close_agent({ target: taskId })
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delete state.active_agents[taskId]
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} else {
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// 5) Collect results
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state.tasks[taskId].status = 'completed'
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close_agent({ target: taskId }) // Use task_name, not agentId
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delete state.active_agents[taskId]
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}
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```
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e. Check for checkpoints after worker completes:
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@@ -136,8 +153,31 @@ delete state.active_agents[taskId]
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5. Update tasks.json, output summary, STOP
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**Cross-Agent Supplementary Context** (v4):
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When spawning workers in a later pipeline phase, send upstream results as supplementary context to already-running workers:
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```
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// Example: Send scan results to running reviewer
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send_message({
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target: "<running-agent-task-name>",
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items: [{ type: "text", text: `## Supplementary Context\n${upstreamFindings}` }]
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})
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// Note: send_message queues info without interrupting the agent's current work
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```
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Use `send_message` (not `assign_task`) for supplementary info that enriches but doesn't redirect the agent's current task.
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## handleComplete
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**Cleanup Verification** (v4):
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```
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// Verify all agents are properly closed
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const remaining = list_agents({})
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// If any team agents still running -> close_agent each
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// Ensures clean session shutdown
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```
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Pipeline done. Generate report and completion action.
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1. All tasks completed or deleted (no pending, no in_progress)
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@@ -6,7 +6,7 @@ Orchestrate team-review: parse target -> detect mode -> dispatch task chain -> m
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**You are a dispatcher, not a doer.** Your ONLY outputs are:
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- Session state files (`.workflow/.team/` directory)
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- `spawn_agent` / `wait_agent` / `close_agent` / `send_input` calls
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- `spawn_agent` / `wait_agent` / `close_agent` / `send_message` / `assign_task` calls
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- Status reports to the user / `request_user_input` prompts
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**FORBIDDEN** (even if the task seems trivial):
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@@ -36,6 +36,8 @@ WRONG: Edit/Write on project source files — worker work
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- Maintain session state (tasks.json)
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- Execute completion action when pipeline finishes
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- **Always proceed through full Phase 1-5 workflow, never skip to direct execution**
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- Use `send_message` for supplementary context (non-interrupting) and `assign_task` for triggering new work
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- Use `list_agents` for session resume health checks and cleanup verification
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### MUST NOT
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- Run analysis tools directly (semgrep, eslint, tsc, etc.)
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@@ -151,6 +153,16 @@ Delegate to @commands/monitor.md#handleSpawnNext:
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- auto_archive -> Archive & Clean
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- auto_keep -> Keep Active
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## v4 Coordination Patterns
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### Message Semantics
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- **send_message**: Queue supplementary info to a running agent. Does NOT interrupt current processing. Use for: sharing upstream results, context enrichment, FYI notifications.
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- **assign_task**: Assign new work and trigger processing. Use for: waking idle agents, redirecting work, requesting new output.
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### Agent Lifecycle Management
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- **list_agents({})**: Returns all running agents. Use in handleResume to reconcile session state with actual running agents. Use in handleComplete to verify clean shutdown.
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- **Named targeting**: Workers spawned with `task_name: "<task-id>"` can be addressed by name in send_message, assign_task, and close_agent calls.
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## Error Handling
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| Error | Resolution |
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