agentic-engineering

Installation
Summary

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
SKILL.md

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.

Task Decomposition

Related skills
Installs
3.6K
GitHub Stars
179.7K
First Seen
Mar 5, 2026