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.
Use this skill for engineering workflows where AI agents perform most implementation work and humans enforce quality and risk controls.
Operating Principles
Define completion criteria before execution.
Decompose work into agent-sized units.
Route model tiers by task complexity.
Measure with evals and regression checks.
Eval-First Loop
Define capability eval and regression eval.
Run baseline and capture failure signatures.
Execute implementation.
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)
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