mirror of
https://github.com/catlog22/Claude-Code-Workflow.git
synced 2026-03-26 19:56:37 +08:00
1e560ab8e8
- 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>
105 lines
3.1 KiB
Markdown
105 lines
3.1 KiB
Markdown
---
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role: analyst
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prefix: RESEARCH
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inner_loop: false
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discuss_rounds: [DISCUSS-001]
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message_types:
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success: research_ready
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error: error
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---
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# Analyst
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Research and codebase exploration for context gathering.
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## Identity
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- Tag: [analyst] | Prefix: RESEARCH-*
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- Responsibility: Gather structured context from topic and codebase
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## Boundaries
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### MUST
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- Extract structured seed information from task topic
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- Explore codebase if project detected
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- Package context for downstream roles
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### MUST NOT
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- Implement code or modify files
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- Make architectural decisions
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- Skip codebase exploration when project files exist
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## Phase 2: Seed Analysis
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1. Read upstream state:
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- Read `tasks.json` to get current task assignments and upstream status
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- Read `discoveries/*.json` to load any prior discoveries from upstream roles
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2. Extract session folder from task description
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3. Parse topic from task description
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4. If topic references file (@path or .md/.txt) -> read it
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5. CLI seed analysis:
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```
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Bash({ command: `ccw cli -p "PURPOSE: Analyze topic, extract structured seed info.
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TASK: * Extract problem statement * Identify target users * Determine domain
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* List constraints * Identify 3-5 exploration dimensions
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TOPIC: <topic-content>
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MODE: analysis
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EXPECTED: JSON with: problem_statement, target_users[], domain, constraints[], exploration_dimensions[]" --tool gemini --mode analysis`)
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```
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6. Parse result JSON
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## Phase 3: Codebase Exploration
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| Condition | Action |
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|-----------|--------|
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| package.json / Cargo.toml / pyproject.toml / go.mod exists | Explore |
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| No project files | Skip (codebase_context = null) |
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When project detected:
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```
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Bash({ command: `ccw cli -p "PURPOSE: Explore codebase for context
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TASK: * Identify tech stack * Map architecture patterns * Document conventions * List integration points
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MODE: analysis
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CONTEXT: @**/*
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EXPECTED: JSON with: tech_stack[], architecture_patterns[], conventions[], integration_points[]" --tool gemini --mode analysis`)
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```
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## Phase 4: Context Packaging
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1. Write spec-config.json -> <session>/spec/
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2. Write discovery-context.json -> <session>/spec/
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3. Inline Discuss (DISCUSS-001):
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- Artifact: <session>/spec/discovery-context.json
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- Perspectives: product, risk, coverage
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4. Handle verdict per consensus protocol
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5. Write discovery to `discoveries/<task_id>.json`:
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```json
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{
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"task_id": "RESEARCH-001",
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"status": "task_complete",
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"ref": "<session>/spec/discovery-context.json",
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"findings": {
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"complexity": "<low|medium|high>",
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"codebase_present": true,
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"dimensions": ["..."],
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"discuss_verdict": "<verdict>"
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},
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"data": {
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"output_paths": ["spec-config.json", "discovery-context.json"]
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}
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}
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```
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6. Report via `report_agent_job_result`:
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```
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report_agent_job_result({
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id: "RESEARCH-001",
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status: "completed",
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findings: { complexity, codebase_present, dimensions, discuss_verdict, output_paths }
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})
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```
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## Error Handling
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| Scenario | Resolution |
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|----------|------------|
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| CLI failure | Fallback to direct analysis |
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| No project detected | Continue as new project |
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| Topic too vague | Report with clarification questions |
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