What problem does it solve?
Unstructured AI agent engineering workflows often lead to wasted compute, inconsistent output quality, and unmeasured implementation impact due to missing evaluation guardrails, poor task decomposition, and inefficient model routing.
Core Features & Use Cases
- Eval-First Execution Loop: Define capability and regression evals upfront to baseline performance, capture failure signatures, and measure implementation impact objectively.
- 15-Minute Task Decomposition: Break work into independently verifiable units with single dominant risks and clear done conditions to reduce complexity and improve traceability.
- Tiered Model Routing: Assign Haiku, Sonnet, or Opus models to matching task complexities to optimize cost and performance, escalating only when lower tiers fail with clear reasoning gaps.
- Use Case: For example, when building a new terminal emulator feature with AI agents, use this skill to decompose the work into small testable units, run baseline evals before implementation, and route simple boilerplate tasks to Haiku to reduce API costs.
Quick Start
Use the agentic-engineering skill to plan and execute a new feature implementation with eval checks, task decomposition, and cost-optimized model routing.
