agentic-engineering — Автоматизация инженерных задач с помощью AI-агентов

--- name: agentic-engineering description: > Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing. Use when AI agents perform most implementation work and humans enforce quality and risk controls. metadata: origin: ECC --- # Agentic Engineering Use this skill for engineering workflows where AI agents perform most implementation work and humans enforce quality and risk controls. ## Operating Principles 1. Define completion criteria before execution. 2. Decompose work into agent-sized units. 3. Route model tiers by task complexity. 4. Measure with evals and regression checks. ## Eval-First Loop 1. Define capability eval and regression eval. 2. Run baseline and capture failure signatures. 3. Execute implementation. 4. Re-run evals and compare deltas. **Example workflow:** ``` 1. Write test that captures desired behavior (eval) 2. Run test → capture baseline failures 3. Implement feature 4. Re-run test → verify improvements 5. Check for regressions in other tests ``` ## Task Decomposition Apply the 15-minute unit rule: - Each unit should be independently verifiable - Each unit should have a single dominant risk - Each unit should expose a clear done condition **Good decomposition:** ``` Task: Add user authentication ├─ Unit 1: Add password hashing (15 min, security risk) ├─ Unit 2: Create login endpoint (15 min, API contract risk) ├─ Unit 3: Add session management (15 min, state risk) └─ Unit 4: Protect routes with middleware (15 min, auth logic risk) ``` **Bad decomposition:** ``` Task: Add user authentication (2 hours, multiple risks) ``` ## Model Routing Choose model tier based on task complexity: - **Haiku**: Classification, boilerplate transforms, narrow edits - Example: Rename variable, add type annotation, format code - **Sonnet**: Implementation and refactors - Example: Implement feature, refactor module, write tests - **Opus**: Architecture, root-cause analysis, multi-file invariants - Example: Design system, debug complex issue, review architecture **Cost discipline:** Escalate model tier only when lower tier fails with a clear reasoning gap. ## Session Strategy - **Continue session** for closely-coupled units - Example: Implementing related functions in same module - **Start fresh session** after major phase transitions - Example: Moving from implementation to testing - **Compact after milestone completion**, not during active debugging - Example: After feature complete, before starting next feature ## Review Focus for AI-Generated Code Prioritize: - Invariants and edge cases - Error boundaries - Security and auth assumptions - Hidden coupling and rollout risk Do not waste review cycles on style-only disagreements when automated format/lint already enforce style. **Review checklist:** - [ ] Edge cases handled (null, empty, boundary values) - [ ] Error handling comprehensive - [ ] Security assumptions validated - [ ] No hidden coupling between modules - [ ] Rollout risk assessed (breaking changes, migrations) ## Cost Discipline Track per task: - Model tier used - Token estimate - Retries needed - Wall-clock time - Success/failure outcome **Example tracking:** ``` Task: Implement user login Model: Sonnet Tokens: ~5k input, ~2k output Retries: 1 (initial implementation had auth bug) Time: 8 minutes Outcome: Success ``` ## When to Use This Skill - Managing AI-driven development workflows - Planning agent task decomposition - Optimizing model tier selection - Implementing eval-first development - Reviewing AI-generated code - Tracking development costs ## Integration with Other Skills - **tdd-workflow**: Combine with eval-first loop for test-driven development - **verification-loop**: Use for continuous validation during implementation - **search-first**: Apply before implementation to find existing solutions - **coding-standards**: Reference during code review phase