mirror of
https://github.com/catlog22/Claude-Code-Workflow.git
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26bda9c634
- Implemented the 'monitor' command for coordinator role to handle monitoring events, task completion, and pipeline management. - Created role specifications for the coordinator, detailing responsibilities, command execution protocols, and session management. - Added role specifications for the analyst, discussant, explorer, and synthesizer in the ultra-analyze skill, defining their context loading, analysis, and synthesis processes.
71 lines
3.6 KiB
Markdown
71 lines
3.6 KiB
Markdown
---
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prefix: SCAN
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inner_loop: false
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message_types:
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success: scan_complete
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error: error
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---
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# Code Scanner
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Toolchain + LLM semantic scan producing structured findings. Static analysis tools in parallel, then LLM for issues tools miss. Read-only -- never modifies source code. 4-dimension system: security (SEC), correctness (COR), performance (PRF), maintainability (MNT).
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## Phase 2: Context & Toolchain Detection
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| Input | Source | Required |
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|-------|--------|----------|
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| Task description | From task subject/description | Yes |
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| Session path | Extracted from task description | Yes |
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| .msg/meta.json | <session>/.msg/meta.json | No |
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1. Extract session path, target, dimensions, quick flag from task 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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| Tool | Detection | Dimension |
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|------|-----------|-----------|
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| tsc | `tsconfig.json` exists | COR |
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| eslint | `.eslintrc*` or `eslint` in package.json | COR/MNT |
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| semgrep | `.semgrep.yml` exists | SEC |
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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. Load wisdom files from `<session>/wisdom/` if they exist
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## Phase 3: Scan Execution
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**Quick mode**: Single CLI call with analysis mode, max 20 findings, skip toolchain.
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**Standard mode** (sequential):
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### 3A: Toolchain Scan
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Run detected tools in parallel via Bash backgrounding. Each tool writes to `<session>/scan/tmp/<tool>.{json|txt}`. After `wait`, parse each output into normalized findings:
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- tsc: `file(line,col): error TSxxxx: msg` -> dimension=correctness, source=tool:tsc
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- eslint: JSON array -> severity 2=correctness/high, else=maintainability/medium
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- semgrep: `{results[]}` -> dimension=security, severity from extra.severity
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- ruff: `[{code,message,filename}]` -> S*=security, F*/B*=correctness, else=maintainability
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- mypy: `file:line: error: msg [code]` -> dimension=correctness
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- npm audit: `{vulnerabilities:{}}` -> dimension=security, category=dependency
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Write `<session>/scan/toolchain-findings.json`.
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### 3B: Semantic Scan (LLM via CLI)
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Build prompt with target file patterns, toolchain dedup summary, and per-dimension focus areas:
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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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Execute via `ccw cli --tool gemini --mode analysis --rule analysis-review-code-quality` (fallback: qwen -> codex). Parse JSON array response, validate required fields (dimension, title, location.file), enforce per-dimension limit (max 5 each), filter minimum severity (medium+). Write `<session>/scan/semantic-findings.json`.
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## Phase 4: Aggregate & Output
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1. Merge toolchain + semantic findings, deduplicate (same file + line + dimension = duplicate)
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2. Assign dimension-prefixed IDs: SEC-001, COR-001, PRF-001, MNT-001
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3. Write `<session>/scan/scan-results.json` with schema: `{scan_date, target, dimensions, quick_mode, total_findings, by_severity, by_dimension, findings[]}`
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4. Each finding: `{id, dimension, category, severity, title, description, location:{file,line}, source, suggested_fix, effort, confidence}`
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5. Update `<session>/.msg/meta.json` with scan summary (findings_count, by_severity, by_dimension)
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6. Contribute discoveries to `<session>/wisdom/` files
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