anastasiyaw/codex-claude-code-config/deep-review
Claude Code and agent skill by anastasiyaw/codex-claude-code-config
- community
Checking that the SKILL.md file is still in the repository.
Parallel competency-based code review. Launches independent Agent reviewers per competency (security, performance, architecture, database, concurrency, error-handling, frontend, testing), each with a focused checklist and isolated context. Synthesizes findings into unified report with FIX/DEFER/ACCEPT triage. Use when: "deep review", "thorough review", "parallel review", "review by competency", "full code review", or for large diffs (200+ lines) where /review may be too shallow. Complements /review (pre-landing) — this is for deep dives. Do NOT use just to orient in an unfamiliar codebase or get a structural symbol overview; use repo-map for that (this audits a concrete diff for defects, it is not a navigation map). Review is read-only unless the user explicitly asks to review and fix.
Install
npx vskill@latest install anastasiyaw/codex-claude-code-config/deep-reviewSKILL.md
Deep Review — Parallel Competency-Based Code Review
Inspired by Memento workflow engine's parallel review pattern. Philosophy: one focused expert per domain > one generalist checking everything.
Step 0: Determine scope
Store the BASE branch name, resolved local base ref, and list of changed files. Report-only review does not run git fetch, pull, or another command that updates Git state. If the accepted task explicitly requires a fresh remote base and permits that preparation mutation, run git fetch origin "$BASE" --quiet without suppressing failure before resolving BASE_REF; otherwise state that the review used the locally available ref.
If there is no diff, stop: "Nothing to review — no changes against $BASE."
Step 1: Scoping — select relevant competencies
Based on the changed files, select ONLY the competencies that are relevant. Do NOT run all 8 for a 3-file CSS change.
Default to a review-only task. Enter review-and-fix only when the user clearly authorizes both review and implementation (for example, "review and fix"). In either mode, the review phase remains read-only; review-and-fix continues with the approved remediation in this same task after findings are triaged.
Competency selection rules
| Competency | Trigger files/patterns | |
|---|---|---|
| security | auth, middleware, routes handling user input, env files, CORS, JWT, crypto, passwords, tokens, API keys | |
| performance | database queries, loops over collections, API endpoints, bundle config, image/asset handling, caching | |
| architecture | new files/modules, cross-module imports, service boundaries, DI patterns, >5 files changed | |
| database | migrations, schema changes, raw SQL, ORM queries, transactions, indexes | |
| concurrency | queues, workers, locks, async/await patterns, shared state, cron jobs, webhooks | |
| error-handling | try/catch blocks, error responses, validation, external API calls, file I/O | |
| frontend | Vue/React components, CSS/Tailwind, composables/hooks, stores, routing, i18n | |
| testing | test files changed OR >100 lines of logic changed without test changes |
Select every materially relevant competency and no others. Diff size is a signal to inspect scope, not a reason to add unrelated reviewers. A single competency is valid when the changed risk has one material dimension.
Output the selected competencies with a one-line justification each:
Step 2: Get the diff content
Read the full diff. You need it to construct focused prompts for each competency agent.
Also identify which files are relevant to each selected competency — each agent should receive ONLY the files relevant to its domain, not the entire diff.
Step 3: Launch parallel competency reviews
For each selected competency, launch an Agent tool call in parallel. Each agent:
- Gets ONLY the files relevant to its competency
- Has a focused checklist (from the competency definitions below)
- Returns findings in structured format
- Works in isolated context (no bias from other competency reviews)
CRITICAL: Launch ALL agents in a SINGLE message (parallel tool calls). Do NOT launch them sequentially.
If a reviewer must run tests, give it an isolated writable worktree and a unique writable scratch directory. Capture git status --porcelain=v1, git diff --exit-code, and git diff --cached --exit-code before and after the review. Do not place a test-running reviewer in an OS-level read-only sandbox: pytest and similar tools legitimately create temporary files. If only a strict read-only sandbox is available, provide immutable test receipts for inspection and say that the reviewer did not execute the tests.
Agent prompt template
For each competency, use this prompt structure (fill in {COMPETENCY}, {CHECKLIST}, {FILES}):
Competency checklists
Use the checklist content from the files in competencies/ directory. Read each relevant file before constructing the agent prompt.
If competency files don't exist yet, use the inline checklists below:
security:
- SQL/NoSQL injection (parameterized queries?)
- XSS (user input escaped in output?)
- Auth bypass (middleware on all protected routes?)
- CSRF (tokens validated?)
- Secrets in code (API keys, passwords, tokens hardcoded?)
- Trust boundary violations (LLM/external output used unsanitized?)
- Path traversal (user-controlled file paths?)
- Rate limiting on sensitive endpoints?
- CORS configuration (too permissive?)
- JWT validation (expiry, algorithm, issuer checked?)
performance:
- N+1 queries (loop with DB call inside?)
- Missing indexes on filtered/joined columns?
- Unbounded queries (no LIMIT on user-facing endpoints?)
- Memory leaks (event listeners not cleaned up? growing collections?)
- Bundle impact (new dependencies? tree-shaking?)
- Caching opportunities missed?
- Expensive operations in hot paths?
- Pagination on list endpoints?
- Image/asset optimization?
architecture:
- Separation of concerns (business logic in controllers?)
- Circular dependencies between modules?
- God objects/functions (>200 lines, >5 responsibilities?)
- Abstraction level consistency (mixing HTTP and business logic?)
- Interface contracts (types/schemas for cross-module communication?)
- DRY violations (copy-pasted logic that should be shared?)
- Layer violations (UI calling DB directly?)
- Configuration vs hardcoding?
database:
- Migration safety (reversible? data-preserving? locks?)
- Query performance (JOINs, subqueries, full table scans?)
- Transaction boundaries (operations that should be atomic?)
- Data integrity (constraints, foreign keys, NOT NULL where needed?)
- Index coverage for new queries?
- Connection management (pool exhaustion risk?)
- Schema evolution (backward compatible?)
- Default values for new columns?
concurrency:
- Race conditions (read-modify-write without lock?)
- Deadlock potential (multiple locks in inconsistent order?)
- Idempotency (can the operation be safely retried?)
- Queue handling (ack/nack, retry policy, DLQ?)
- Shared state access (multiple workers writing same resource?)
- Distributed locking (Redis/DB advisory locks where needed?)
- Timeout handling on external calls?
- Graceful shutdown (in-flight requests drained?)
error-handling:
- Swallowed errors (empty catch blocks?)
- Error propagation (errors from dependencies surfaced correctly?)
- User-facing error messages (informative but not leaking internals?)
- Partial failure handling (what if step 3 of 5 fails?)
- Retry logic (exponential backoff? max retries? circuit breaker?)
- Cleanup on failure (resources released? partial writes rolled back?)
- Validation at boundaries (API input, file uploads, webhook payloads?)
- Logging on error paths (enough context to debug? not too noisy?)
frontend:
- Reactivity correctness (Vue: ref vs reactive, computed vs watch?)
- Component responsibility (>300 lines = split candidate?)
- Accessibility (ARIA labels, keyboard nav, contrast?)
- Loading/error states handled?
- Memory leaks (subscriptions, intervals, event listeners cleaned up?)
- i18n (hardcoded strings?)
- Responsive design (mobile breakpoints?)
- XSS in template rendering (v-html with user data?)
testing:
- Logic changed without test update?
- Happy path only (no error/edge case tests?)
- Mocking too much (testing mocks instead of behavior?)
- Test isolation (tests depend on execution order?)
- Assertions quality (testing implementation details vs behavior?)
- Missing integration tests for cross-module changes?
- Flaky patterns (timing, network, random data?)
- Coverage gaps for critical paths (auth, payments, data integrity?)
Step 4: Collect and synthesize
After ALL agents complete, collect their findings and synthesize:
4a: Deduplicate
Multiple competency reviewers may find the same issue. Group findings by file:line and merge duplicates. When merged, note which competencies flagged it (higher confidence when multiple domains agree).
4b: Triage each finding
For each unique finding, assign a triage:
This is the start of the file. Read the full SKILL.md on GitHub.
Security
- Security scanPassed, 100 of 100Pattern checks on the skill's files.
- Model reviewNot runA model review is scheduled for the next rescan.
Quality evaluation
- Task evaluationNot runNo evaluation yet. Run one in Skill Studio.
Source
- SKILL.md in the repositoryCheckingskills/development/deep-review/SKILL.md
Popularity
- Repository stars
- 150
- Repository forks
- 24
- 7-day trend score
- 25
Stars and forks are for the whole GitHub repository, not this skill alone. Not refreshed yet.
Works with
Any agent vskill installs to: 56 targets in all. These 39 have a page on this site.
- Claude Code
- Cursor
- GitHub Copilot
- Windsurf
- Codex
- Gemini CLI
- Cline
- Amp
- Roo Code
- Goose
- Continue
- Kilo Code
- Junie
- Trae
- OpenHands
- Qwen Code
- AI Agent
- Tabnine Chat
- Sourcegraph Cody
- Amazon Q
- Firebase Genkit
- Sweep
- Devin
- Phind
- Mentat
- Claude Desktop
- Aider
- GPT Pilot
- Plandex
- bolt.new
- v0
- Replit Agent
- Anse
- TypingMind
- BetterChatGPT
- LibreChat
- Warp AI
- Pieces
- Fig AI
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