Agent Skills: Team PlanEx

Team PlanEx

Unified team skill: plan-and-execute pipeline for issue-based development. Built on team-worker agent architecture — coordinator orchestrates, workers are team-worker agents loading role-specific instructions from roles/<role>/role.md.

Architecture

Skill(skill="team-planex", 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 workers -> STOP
                                    |
                    +---------------+---------------+
                    v                               v
               [planner]                       [executor]
         (team-worker agent,            (team-worker agent,
          loads roles/planner/role.md)   loads roles/executor/role.md)

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:

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: PEX
• Session path: .workflow/.team/PEX-<date>-<slug>/
• CLI tools: ccw cli --mode analysis (read-only), ccw cli --mode write (modifications)
• Message bus: mcp__ccw-tools__team_msg(session_id=<session-id>, ...)

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>
execution_method: <codex|gemini>

Read role_spec file (<skill_root>/roles/<role>/role.md) to load Phase 2-4 domain instructions.

## Task Context
task_id: <task-id>
title: <task-title>
description: <task-description>
pipeline_phase: <pipeline-phase>

## Upstream Context
<prev_context>`
})

After spawning, use wait_agent({ timeout_ms: 1800000 }) to collect results. If result.timed_out, send STATUS_CHECK via followup_task (wait 3 min), then FINALIZE with interrupt (wait 3 min), then mark timed_out and close agents. Use close_agent({ target }) each worker.

Model Selection Guide

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: "..."
})

User Commands

Session Directory

.workflow/.team/PEX-<slug>-<YYYY-MM-DD>/
├── .msg/
│   ├── messages.jsonl          # Message bus log
│   └── meta.json               # Session state
├── task-analysis.json          # Coordinator analyze output
├── artifacts/
│   └── solutions/              # Planner solution output per issue
│       ├── <issueId-1>.json
│       └── <issueId-N>.json
└── wisdom/                     # Cross-task knowledge
    ├── learnings.md
    ├── decisions.md
    ├── conventions.md
    └── issues.md

Specs Reference

• specs/pipelines.md — Pipeline definitions, task metadata registry, execution method selection

v4 Agent Coordination

Message Semantics

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({ timeout_ms: 1800000 })  // 30 min — apply timeout cascade if timed_out
// 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", message: "..." }) -- queue plan solution to running executor
• close_agent({ target: "PLAN-001" }) -- cleanup by name after completion

Error Handling