mirror of
https://github.com/catlog22/Claude-Code-Workflow.git
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62d8aa3623
- Implement tests for AssociationHighlight, DashboardToolbar, QueuePanel, SessionGroupTree, and TerminalDashboardPage to ensure proper functionality and state management. - Create tests for cliSessionStore, issueQueueIntegrationStore, queueExecutionStore, queueSchedulerStore, sessionManagerStore, and terminalGridStore to validate state resets and workspace scoping. - Mock necessary dependencies and state management hooks to isolate tests and ensure accurate behavior.
103 lines
4.7 KiB
Markdown
103 lines
4.7 KiB
Markdown
## TASK ASSIGNMENT
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### MANDATORY FIRST STEPS
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1. Read shared discoveries: {session_folder}/discoveries.ndjson (if exists, skip if not)
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2. Read project context: .workflow/project-tech.json (if exists)
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---
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## Your Task
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**Task ID**: {id}
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**Title**: {title}
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**Description**: {description}
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**Dimension**: {dimension}
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**Target**: {target}
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### Previous Tasks' Findings (Context)
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{prev_context}
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---
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## Execution Protocol
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1. **Read discoveries**: Load {session_folder}/discoveries.ndjson for shared exploration findings
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2. **Use context**: Apply previous tasks' findings from prev_context above
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3. **Execute**: Perform your assigned role (scanner or reviewer) following the role-specific instructions below
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4. **Share discoveries**: Append exploration findings to shared board:
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```bash
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echo '{"ts":"<ISO8601>","worker":"{id}","type":"<type>","data":{...}}' >> {session_folder}/discoveries.ndjson
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```
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5. **Report result**: Return JSON via report_agent_job_result
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### Role-Specific Instructions
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**If you are a Scanner (SCAN-* task)**:
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1. Extract session path and target from description
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2. Resolve target files (glob pattern or directory → `**/*.{ts,tsx,js,jsx,py,go,java,rs}`)
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3. If no source files found → report empty, complete task cleanly
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4. Detect toolchain availability:
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- tsc: `tsconfig.json` exists → COR dimension
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- eslint: `.eslintrc*` or `eslint` in package.json → COR/MNT
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- semgrep: `.semgrep.yml` exists → SEC dimension
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- ruff: `pyproject.toml` + ruff available → SEC/COR/MNT
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- mypy: mypy available + `pyproject.toml` → COR
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- npmAudit: `package-lock.json` exists → SEC
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5. Run detected tools in parallel via Bash backgrounding
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6. Parse tool outputs into normalized findings with dimension, severity, file:line
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7. Execute semantic scan via CLI: `ccw cli --tool gemini --mode analysis --rule analysis-review-code-quality`
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8. Focus areas per dimension:
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- SEC: Business logic vulnerabilities, privilege escalation, sensitive data flow, auth bypass
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- COR: Logic errors, unhandled exception paths, state management bugs, race conditions
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- PRF: Algorithm complexity, N+1 queries, unnecessary sync, memory leaks, missing caching
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- MNT: Architectural coupling, abstraction leaks, convention violations, dead code
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9. Merge toolchain + semantic findings, deduplicate (same file + line + dimension)
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10. Assign dimension-prefixed IDs: SEC-001, COR-001, PRF-001, MNT-001
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11. Write scan results to session directory
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**If you are a Reviewer (REV-* task)**:
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1. Extract session path and input path from description
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2. Load scan results from previous task (via prev_context or session directory)
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3. If scan results empty → report clean, complete immediately
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4. Triage findings into deep_analysis (critical/high/medium, max 15) and pass_through (remaining)
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5. Split deep_analysis into domain groups:
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- Group A: Security + Correctness → Root cause tracing, fix dependencies, blast radius
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- Group B: Performance + Maintainability → Optimization approaches, refactor tradeoffs
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6. Execute parallel CLI agents for enrichment: `ccw cli --tool gemini --mode analysis --rule analysis-diagnose-bug-root-cause`
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7. Request 6 enrichment fields per finding:
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- root_cause: {description, related_findings[], is_symptom}
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- impact: {scope: low/medium/high, affected_files[], blast_radius}
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- optimization: {approach, alternative, tradeoff}
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- fix_strategy: minimal / refactor / skip
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- fix_complexity: low / medium / high
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- fix_dependencies: finding IDs that must be fixed first
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8. Merge enriched + pass_through findings
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9. Cross-correlate:
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- Critical files: file appears in >=2 dimensions
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- Root cause groups: cluster findings sharing related_findings
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- Optimization suggestions: from root cause groups + standalone enriched findings
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10. Compute metrics: by_dimension, by_severity, dimension_severity_matrix, fixable_count
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11. Write review report to session directory
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### Discovery Types to Share
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- `finding`: {dimension, file, line, severity, title} — Code issue discovered
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- `root_cause`: {finding_id, description, related_findings[]} — Root cause analysis
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- `pattern`: {pattern, files[], occurrences} — Code pattern identified
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---
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## Output (report_agent_job_result)
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Return JSON:
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{
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"id": "{id}",
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"status": "completed" | "failed",
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"findings": "Key discoveries and implementation notes (max 500 chars)",
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"error": ""
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}
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**Scanner findings format**: "Found X security issues (Y critical, Z high), A correctness bugs, B performance issues, C maintainability concerns. Toolchain: [tool results]. LLM scan: [semantic issues]."
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**Reviewer findings format**: "Analyzed X findings. Critical files: [files]. Root cause groups: [count]. Fixable: Y/X. Recommended fix scope: [scope]."
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