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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>
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.codex/skills/team-issue/roles/integrator/role.md
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84
.codex/skills/team-issue/roles/integrator/role.md
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---
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role: integrator
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prefix: MARSHAL
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inner_loop: false
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message_types: [queue_ready, conflict_found, error]
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---
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# Issue Integrator
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Queue orchestration, conflict detection, and execution order optimization. Uses CLI tools for intelligent queue formation with DAG-based parallel groups.
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## Phase 2: Collect Bound Solutions
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| Input | Source | Required |
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|-------|--------|----------|
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| Issue IDs | Task description (GH-\d+ or ISS-\d{8}-\d{6}) | Yes |
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| Bound solutions | `ccw issue solutions <id> --json` | Yes |
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| wisdom meta | <session>/wisdom/.msg/meta.json | No |
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1. Extract issue IDs from task description via regex
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2. Verify all issues have bound solutions:
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```
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Bash("ccw issue solutions <issueId> --json")
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```
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3. Check for unbound issues:
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| Condition | Action |
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|-----------|--------|
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| All issues bound | Proceed to Phase 3 |
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| Any issue unbound | Report error to coordinator, STOP |
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## Phase 3: Queue Formation via CLI
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**CLI invocation**:
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```
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Bash("ccw cli -p \"
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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
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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
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MODE: analysis
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CONTEXT: @.workflow/issues/solutions/**/*.json | Memory: Issues to queue: <issueIds>
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EXPECTED: Queue JSON with: ordered issue list, conflict analysis, parallel_groups (issues that can run concurrently), depends_on relationships
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Write to: .workflow/issues/queue/execution-queue.json
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CONSTRAINTS: Resolve file conflicts | Optimize for parallelism | Maintain dependency order
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\" --tool gemini --mode analysis")
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```
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**Parse queue result**:
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```
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Read(".workflow/issues/queue/execution-queue.json")
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```
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**Queue schema**:
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```json
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{
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"queue": [{ "issue_id": "", "solution_id": "", "order": 0, "depends_on": [], "estimated_files": [] }],
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"conflicts": [{ "issues": [], "files": [], "resolution": "" }],
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"parallel_groups": [{ "group": 0, "issues": [] }]
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}
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```
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## Phase 4: Conflict Resolution & Reporting
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**Queue validation**:
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| Condition | Action |
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|-----------|--------|
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| Queue file exists, no unresolved conflicts | Report `queue_ready` |
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| Queue file exists, has unresolved conflicts | Report `conflict_found` for user decision |
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| Queue file not found | Report `error`, STOP |
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**Queue metrics for report**: queue size, parallel group count, resolved conflict count, execution order list.
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Update `<session>/wisdom/.msg/meta.json` under `integrator` namespace:
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- Read existing -> merge `{ "integrator": { queue_size, parallel_groups, conflict_count } }` -> write back
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