What problem does it solve?
This Skill solves the problem of unstructured, unmeasured AI agent engineering workflows that lead to wasted compute, missed edge cases, and inconsistent, unreliable outputs for software engineering tasks.
Core Features & Use Cases
- Eval-First Execution Loop: Define capability and regression evals upfront to measure implementation impact and catch regressions before they reach production.
- Task Decomposition: Break complex engineering work into 15-minute independently verifiable units with clear done conditions to reduce risk and improve traceability.
- Cost-Aware Model Routing: Assign tasks to the optimal AI model tier (Haiku for simple boilerplate, Sonnet for implementation, Opus for architecture) to balance performance and cost.
- Structured Code Review: Prioritize review of invariants, edge cases, and security assumptions for AI-generated code, avoiding wasted time on style nits handled by automated tools.
Use Case: A solo engineer building an AI-powered feature can use this Skill to structure their workflow, run baseline evals before coding, route each subtask to the appropriate model, and ensure all critical code quality checks are completed efficiently.
Quick Start
Use the agentic-engineering skill to structure your next AI implementation task by first defining completion criteria and baseline evals, then decomposing work into 15-minute verifiable units before routing each subtask to the appropriate model tier based on complexity.
