--- name: scan description: Scans the codebase against another skill's criteria using a parallel agent team. Use when the user says /scan to audit code quality, find violations, or assess conformance to best practices. --- # Codebase Scan Audit the codebase against another skill's criteria using a parallel agent team. ## Workflow ### 1. Parse args & load skill Extract the skill name from the args passed to this skill. - If no skill name provided, list available skills in `.claude/skills/` and ask the user which to scan against. - If skill doesn't exist, list available skills and tell the user. - Read `.claude/skills//SKILL.md` plus any files in `references/` and `rules/` subdirectories. - Distill the skill's content into a numbered **criteria checklist**: a flat list of concrete, testable rules labeled C1, C2, C3, etc. Each criterion should be a single sentence describing what to check for. - If the skill has no evaluable code criteria (e.g., workflow-only skills like `why` that don't define code patterns or rules), tell the user it's not scannable and stop. ### 2. Discover relevant files Use the skill's criteria to infer file scope: - React/frontend criteria → `app/**/*.tsx`, `app/**/*.ts` - Backend criteria → `services/**/*.ts` - General/mixed → both of the above - CSS/styling → `app/**/*.css`, `app/**/*.tsx` Always exclude: `node_modules/`, `dist/`, `*.test.*`, `*.spec.*`, `**/migrations/**`, `**/*.d.ts`, generated files. Count candidate files. If zero, tell the user and stop. ### 3. Plan team composition Split files into chunks by directory subtree so no file is assigned to two agents: | Candidate files | Teammates | |----------------|-----------| | < 100 | 2 (or skip team for < 20) | | 100–500 | 3 | | 500+ | 4–5, split by top-level directory | For very small scans (< 20 files), skip the team — scan inline and jump to step 6. ### 4. Spawn scan team 1. `spawnTeam` with name `scan-` 2. Create one `TaskCreate` per chunk with: - Subject: `Scan against criteria` - Description: Include the **full criteria checklist**, the file scope (glob patterns), and the teammate instructions from `references/teammate-instructions.md` 3. Spawn teammates as `general-purpose` subagent_type (they need Read, Glob, Grep + team communication) 4. Name teammates `scanner-1`, `scanner-2`, etc. 5. Assign tasks via `TaskUpdate` with `owner` ### 5. Collect & synthesize Wait for all teammates to complete their tasks. Each teammate reports findings in structured format: ``` FINDING: C | SEVERITY | file/path.ts:LINE | Description ``` Collect all findings from teammate messages. ### 6. Generate report Create `.claude-scan/.md` using the format in `references/report-format.md`. Key sections: - Executive Summary (2-3 sentences) - Criteria Evaluated (table with violation counts) - Findings by Severity (Critical → Warning → Info tables) - Patterns Observed - Statistics - Recommended Fix Order (batched by non-overlapping file groups) ### 7. Cleanup & present - If a team was spawned: send `shutdown_request` to all teammates, then `cleanup` - Display inline summary: total findings by severity, top violated criteria, scan scope - Tell the user the full report is at `.claude-scan/.md` - Ask: "Want me to spawn a fix team to address these findings?" ### 8. Fix team (if user says yes) 1. Read the report's "Recommended Fix Order" section 2. Group fixes into non-overlapping file batches 3. `spawnTeam` with name `fix-` 4. Create tasks per batch — each task description includes: - The specific violations to fix (from the report) - The relevant criteria definitions - Instructions from `references/fix-team-instructions.md` 5. Spawn `general-purpose` teammates named `fixer-1`, `fixer-2`, etc. (need Edit/Write) 6. Assign tasks and wait for completion 7. Shutdown and cleanup fix team 8. Ask user if they want to re-scan to verify fixes

scan

Scan your own AI components