feat: migrate all codex team skills from spawn_agents_on_csv to spawn_agent + wait_agent architecture

- Delete 21 old team skill directories using CSV-wave pipeline pattern (~100+ files)
- Delete old team-lifecycle (v3) and team-planex-v2
- Create generic team-worker.toml and team-supervisor.toml (replacing tlv4-specific TOMLs)
- Convert 19 team skills from Claude Code format (Agent/SendMessage/TaskCreate)
  to Codex format (spawn_agent/wait_agent/tasks.json/request_user_input)
- Update team-lifecycle-v4 to use generic agent types (team_worker/team_supervisor)
- Convert all coordinator role files: dispatch.md, monitor.md, role.md
- Convert all worker role files: remove run_in_background, fix Bash syntax
- Convert all specs/pipelines.md references
- Final state: 20 team skills, 217 .md files, zero Claude Code API residuals

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
catlog22
2026-03-24 16:54:48 +08:00
parent 54283e5dbb
commit 1e560ab8e8
334 changed files with 28996 additions and 35516 deletions

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---
role: integrator
prefix: MARSHAL
inner_loop: false
message_types: [queue_ready, conflict_found, error]
---
# Issue Integrator
Queue orchestration, conflict detection, and execution order optimization. Uses CLI tools for intelligent queue formation with DAG-based parallel groups.
## Phase 2: Collect Bound Solutions
| Input | Source | Required |
|-------|--------|----------|
| Issue IDs | Task description (GH-\d+ or ISS-\d{8}-\d{6}) | Yes |
| Bound solutions | `ccw issue solutions <id> --json` | Yes |
| wisdom meta | <session>/wisdom/.msg/meta.json | No |
1. Extract issue IDs from task description via regex
2. Verify all issues have bound solutions:
```
Bash("ccw issue solutions <issueId> --json")
```
3. Check for unbound issues:
| Condition | Action |
|-----------|--------|
| All issues bound | Proceed to Phase 3 |
| Any issue unbound | Report error to coordinator, STOP |
## Phase 3: Queue Formation via CLI
**CLI invocation**:
```
Bash("ccw cli -p \"
PURPOSE: Form execution queue for <count> issues with conflict detection and optimal ordering; success = DAG-based queue with parallel groups written to execution-queue.json
TASK: * Load all bound solutions from .workflow/issues/solutions/ * Analyze file conflicts between solutions * Build dependency graph * Determine optimal execution order (DAG-based) * Identify parallel execution groups * Write queue JSON
MODE: analysis
CONTEXT: @.workflow/issues/solutions/**/*.json | Memory: Issues to queue: <issueIds>
EXPECTED: Queue JSON with: ordered issue list, conflict analysis, parallel_groups (issues that can run concurrently), depends_on relationships
Write to: .workflow/issues/queue/execution-queue.json
CONSTRAINTS: Resolve file conflicts | Optimize for parallelism | Maintain dependency order
\" --tool gemini --mode analysis")
```
**Parse queue result**:
```
Read(".workflow/issues/queue/execution-queue.json")
```
**Queue schema**:
```json
{
"queue": [{ "issue_id": "", "solution_id": "", "order": 0, "depends_on": [], "estimated_files": [] }],
"conflicts": [{ "issues": [], "files": [], "resolution": "" }],
"parallel_groups": [{ "group": 0, "issues": [] }]
}
```
## Phase 4: Conflict Resolution & Reporting
**Queue validation**:
| Condition | Action |
|-----------|--------|
| Queue file exists, no unresolved conflicts | Report `queue_ready` |
| Queue file exists, has unresolved conflicts | Report `conflict_found` for user decision |
| Queue file not found | Report `error`, STOP |
**Queue metrics for report**: queue size, parallel group count, resolved conflict count, execution order list.
Update `<session>/wisdom/.msg/meta.json` under `integrator` namespace:
- Read existing -> merge `{ "integrator": { queue_size, parallel_groups, conflict_count } }` -> write back