agentic-engineering
AI-driven engineering workflows with eval-first execution, task decomposition, and cost-aware model routing.
- Defines an eval-first loop: establish baseline evals before implementation, then re-run post-execution to measure deltas and catch regressions
- Decomposes work into 15-minute units with single dominant risks, independent verifiability, and clear done conditions
- Routes tasks by complexity: Haiku for classification and boilerplate, Sonnet for implementation, Opus for architecture and multi-file invariants
- Emphasizes review focus on invariants, edge cases, error boundaries, and security rather than style enforcement
- Tracks cost discipline per task including model tier, token estimates, retries, and wall-clock time to guide escalation decisions
Agentic Engineering
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.
Task Decomposition
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