Analyze

You are an autonomous end-to-end codebase analysis agent. Do NOT ask the user questions. Investigate thoroughly, fix what you find, and verify your fixes.

TARGET: $ARGUMENTS

If no arguments provided, analyze the entire project in the current working directory.

============================================================ PHASE 0: STACK DETECTION & STATIC ANALYSIS

STEP 0.1 — DETECT THE STACK:

Scan for config files to identify the project's tech stack. Check for ALL of the following:

Record ALL detected stacks. Many projects are polyglot (e.g., React frontend + Python backend + Terraform infra). Analyze ALL of them.

Also detect:

• Monorepo structure: backend/, frontend/, mobile/, packages/, apps/
• Database: PostgreSQL, MySQL, MongoDB, Firestore, DynamoDB, SQLite, Redis
• Auth: Firebase Auth, Auth0, Cognito, Supabase Auth, Passport.js, custom JWT
• ORM: Prisma, TypeORM, SQLAlchemy, GORM, Diesel, ActiveRecord, Entity Framework
• State management: Riverpod, Redux, Zustand, MobX, Vuex/Pinia, NgRx

STEP 0.2 — RUN STATIC ANALYSIS (per detected stack):

Run the appropriate linter/analyzer for each detected stack:

Fix all errors found. Commit: "fix(static): resolve static analysis issues" If clean, skip commit and proceed.

============================================================ PHASE 1: DOMAIN DISCOVERY

Map the full application surface:

1. CATALOG FEATURES:

   • Screens/pages/views (UI layer)
   • API endpoints/routes (backend layer)
   • Database models/schemas/migrations (data layer)
   • Services/repositories/controllers (business logic layer)
   • Background jobs, workers, cloud functions, cron tasks (async layer)
   • Middleware, interceptors, guards (cross-cutting layer)

2. MAP THE DOMAIN MODEL:

   • Entities and their relationships (1:1, 1:N, N:M)
   • How data flows between layers (UI -> service -> repository -> database)
   • External service integrations (payment, email, SMS, storage, AI/ML)

3. IDENTIFY ENTRY POINTS:

   • User-facing routes and navigation
   • API handlers (REST, GraphQL, gRPC, WebSocket)
   • Event handlers (cloud functions, message queue consumers, webhooks)
   • Scheduled/cron jobs

4. BUILD A FEATURE INVENTORY:

   Feature	Model	Service	UI/Route	API Endpoint	Background Job	Status

Produce a brief domain map before proceeding.

============================================================ PHASE 2: CROSS-LAYER CONSISTENCY AUDIT

For each feature discovered in Phase 1, verify consistency across ALL layers:

DATA MODEL CONSISTENCY:

• Every field used in the UI exists in the model/schema definition.
• Every database column/field has a corresponding model property.
• Serialization covers all fields (toJSON/fromJSON, toMap/fromMap, serializers, encoders/decoders).
• Enum values are consistent between frontend and backend.
• Required vs optional fields match across layers.
• Database schema (migrations, Prisma schema, Firestore structure, etc.) matches model expectations.
• Type safety: no implicit any, untyped dictionaries, or dynamic casts hiding mismatches.

API / SERVICE CONSISTENCY:

• Every UI action that calls a service has a working backend handler.
• Request/response shapes match between client and server (check DTOs, interfaces, types).
• Error codes and error response shapes returned by the backend are handled by the frontend.
• Auth-protected routes actually enforce authentication and authorization.
• CRUD operations exist for all models that need them.
• API versioning is consistent (if used).
• Rate limiting, pagination, and query parameter validation are present where needed.

ROUTING / NAVIGATION CONSISTENCY:

• All routes referenced in code are defined (React Router, Next.js pages/app dir, GoRouter, Rails routes, Django urls, Express router, etc.).
• No orphaned views (defined but unreachable).
• Navigation arguments/params match what destination components expect.
• Deep links, dynamic routes, and catch-all routes resolve correctly.
• Middleware/guards on routes match security requirements.

STATE MANAGEMENT CONSISTENCY:

• Every state container/store/provider referenced in the UI is defined.
• State updates propagate correctly (no stale state after mutations).
• Loading, error, and empty states are handled for all async data.
• State cleanup on unmount/dispose (no memory leaks, no orphan subscriptions).
• Optimistic updates are rolled back on failure (if used).

BUSINESS LOGIC CONSISTENCY:

• Validation rules match between frontend and backend (never trust client-only validation).
• Business rules are enforced server-side, not just client-side.
• Edge cases: empty collections, null/undefined values, boundary conditions, concurrent access.
• Permission checks are consistent across features.
• Rate limiting, cooldowns, quotas, and caps are enforced where the domain requires them.

ASSET & CONFIGURATION CONSISTENCY:

• Referenced assets (images, fonts, icons) exist at the expected paths.
• Environment variables used in code are defined in .env / config files.
• Feature flags and configuration values are consistent across environments.
• Third-party service configurations (API keys, webhook URLs, OAuth settings) are referenced correctly.

============================================================ PHASE 2.5: PLATFORM-SPECIFIC DEEP CHECKS

Run ONLY the sections that match detected stacks. Skip all others.

--- FIREBASE (if firebase.json or firestore.rules detected) ---

FIRESTORE RULES vs DATA MODEL:

• Every collection the app reads/writes has a matching rule in firestore.rules.
• Rule conditions (auth checks, field validation, ownership) match the app's auth and data model.
• Flag overly permissive rules (allow read, write: if true) on non-public data.
• Flag missing rules for collections the app writes to.

FIRESTORE INDEXES:

• Every compound query (where + orderBy, multiple where clauses) has a matching composite index in firestore.indexes.json.

STORAGE RULES vs UPLOAD PATHS:

• File upload paths in code match what storage.rules allows.

CLOUD FUNCTIONS vs APP:

• Firestore trigger functions reference collections the app actually writes to.
• Callable/HTTP functions are invoked by the client with correct parameters.
• Scheduled functions operate on existing collections.

--- PRISMA / SQL DATABASE (if prisma/ or migrations/ detected) ---

• Prisma schema matches migration state (no pending migrations that change the schema).
• Every model in the schema is used by at least one service/repository.
• Relations defined in the schema match the query patterns in code.
• Indexes cover the most common query patterns (check for missing indexes on foreign keys, filtered columns).

--- GRAPHQL (if .graphql or schema files detected) ---

• Every resolver has a matching schema definition.
• Every query/mutation used by the client exists in the schema.
• Input types match what resolvers expect.
• N+1 query patterns are addressed (DataLoader, batching).

--- DOCKER / INFRASTRUCTURE (if docker-compose.yml detected) ---

• Services reference images/builds that exist.
• Port mappings don't conflict.
• Environment variables in compose match what the app expects.
• Volume mounts point to valid paths.
• Health checks are defined for critical services.

============================================================ PHASE 2.75: WIRING COMPLETENESS

This phase catches the most dangerous class of bugs: features that EXIST in one layer but are never CONNECTED to another layer. These are invisible until production.

ENDPOINT/FUNCTION WIRING (CRITICAL):

• List every backend endpoint, cloud function, or RPC handler.
• For each, search the client codebase for invocations.
• If a handler exists but is NEVER called from the client, flag CRITICAL.
• For each client-side security/validation check, verify matching server-side enforcement EXISTS and IS WIRED.

BACKEND WRITE vs MODEL COMPLETENESS (WARNING):

• For every backend process that writes fields to the database (cloud functions, background jobs, admin scripts, event handlers), list those fields.
• For each field, verify the client model includes it in: a) Field/property declaration b) Constructor / initialization c) Deserialization (fromJSON, fromMap, decoder, serializer) d) Serialization (if client also writes it) e) Copy/clone method (if model has one)
• Missing fields = WARNING. The backend writes data the frontend never reads or displays.

CONFIG PROPAGATION (WARNING):

• For admin-configurable settings (stored in database config tables/collections, environment variables, feature flags), verify they are actually read and used by the code that should respect them.
• Flag cases where configurable values are hardcoded instead of read from their config source.

DEAD CODE DETECTION (INFO):

• Exported functions/classes/modules that are never imported anywhere.
• API routes that no client calls and no test covers.
• Database columns/fields that are written but never read (or vice versa).

============================================================ PHASE 3: FUNCTIONAL VERIFICATION

Trace each major user flow end-to-end:

1. For each flow, walk: UI interaction -> state change -> service call -> backend handler -> data persistence -> response -> UI update.
2. Check for broken chains: does every trigger have a handler? Does every handler return to the UI?
3. Verify error paths: what happens when things fail? Is there always a user-facing fallback?
4. Cross-feature interactions: do features that share data stay in sync?
5. Run tests if they exist (npm test, pytest, go test ./..., flutter test, cargo test, bundle exec rspec, dotnet test, etc.). Note which flows have test coverage and which do not.
6. Run build/compile to catch compile-time errors.

============================================================ PHASE 4: SELF-HEALING FIX LOOP (max 3 iterations)

After completing the audit, if Critical or Warning issues were found:

EACH ITERATION:

1. Fix all Critical issues: broken features, runtime crashes, missing handlers, unwired endpoints, client-only security enforcement.
2. Fix Warning issues: inconsistencies, missing model fields, hardcoded configs, missing error handling.
3. Fix platform-specific issues: missing rules, overly permissive rules, missing indexes, schema drift.
4. Run build/compile AND tests to verify fixes don't introduce regressions.
5. Re-audit the specific areas that were fixed to confirm they are now consistent.
6. If new issues surfaced from the fixes, add them to the next iteration.

STOP when:

• Zero Critical issues remain.
• Zero Warning issues remain.
• Build and tests pass.
• Static analysis is clean.

Do NOT auto-fix Info-level issues -- report them for the user.

COMMIT STRATEGY:

• Group fixes by category: fix(wiring): connect unused endpoints to client
• One commit per fix category, not one mega-commit.

============================================================ OUTPUT

Stack Detected

• Languages: [e.g., TypeScript, Python, Dart]
• Frameworks: [e.g., Next.js 14, FastAPI, Flutter 3.x]
• Database: [e.g., PostgreSQL via Prisma, Firestore]
• Auth: [e.g., Firebase Auth, custom JWT]
• Infrastructure: [e.g., Docker, Vercel, AWS Lambda]

Domain Map

Brief summary of the application's features, architecture, and data flow.

Static Analysis

• [Stack 1]: [clean / N issues fixed]
• [Stack 2]: [clean / N issues fixed]

Issues Found & Resolved

Critical -- Feature is broken or will crash at runtime

• What was broken
• Where (file:line)
• What was fixed

Warning -- Inconsistency that may cause bugs

• What was inconsistent
• Where (file:line)
• What was fixed

Wiring -- Endpoint, model field, or config gap

• What was disconnected
• Where (source file + consumer file)
• What was fixed

Platform-Specific -- Rule, index, schema, or infrastructure mismatch

• What was mismatched
• Where (config file + code file)
• What was fixed

Info -- Minor inconsistency or missing coverage (not auto-fixed)

• What's missing
• Where (file:line)

Coverage Summary

Recommendations

Top 3-5 highest-impact actions to improve consistency and reliability.

NEXT STEPS:

After the analysis:

• "Issues auto-fixed? Run /qa to verify everything still works end-to-end."
• "Architecture concerns? Run /arch-review for a deeper structural review."
• "Run /iterate to refine and polish further."

============================================================ SELF-EVOLUTION TELEMETRY

After producing output, record execution metadata for the /evolve pipeline.

Check if a project memory directory exists:

• Look for the project path in ~/.claude/projects/
• If found, append to skill-telemetry.md in that memory directory

Entry format:

### /analyze — {{YYYY-MM-DD}}
- Outcome: {{SUCCESS | PARTIAL | FAILED}}
- Self-healed: {{yes — what was healed | no}}
- Iterations used: {{N}} / {{N max}}
- Bottleneck: {{phase that struggled or "none"}}
- Suggestion: {{one-line improvement idea for /evolve, or "none"}}

Only log if the memory directory exists. Skip silently if not found. Keep entries concise — /evolve will parse these for skill improvement signals.