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agentic-engineering

Enforce eval-first execution and task decomposition for AI agent engineering workflows.

Updated Jun 25, 2026
Search Tags:#task decomposition#agentic engineering#cost optimization#regression testing#model routing#ai-assisted development#eval-first
One-click install
npx skills add https://github.com/sumeetonline90/fitup_all --skill agentic-engineering-sumeetonline90
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: agentic-engineering
Source: https://github.com/sumeetonline90/fitup_all/tree/main/.cursor/skills/agentic-engineering
Command: npx skills add https://github.com/sumeetonline90/fitup_all --skill agentic-engineering-sumeetonline90

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Unstructured AI agent engineering workflows lead to inconsistent output, uncontrolled costs, and missed quality guardrails, wasting engineering time and introducing avoidable risk.

Core Features & Use Cases

  • Eval-First Execution: Define capability and regression evals upfront to measure implementation impact and catch regressions before they reach production.
  • Task Decomposition: Break work into 15-minute independent units with clear done conditions to reduce risk and improve verifiability of AI-generated work.
  • Tiered Model Routing: Match model tiers (Haiku for simple tasks, Sonnet for implementation, Opus for complex analysis) to task complexity to optimize cost and performance.
  • Use Case: When building a new feature with AI agents, use this skill to structure work into verifiable units, route simple boilerplate edits to cheaper models, and validate changes with automated evals to avoid costly rework.

Quick Start

Use the agentic-engineering skill to structure your next AI implementation task into 15-minute verifiable units with pre-defined success criteria and appropriate model routing.

Frequently Asked Questions about agentic-engineering

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I control costs when using AI agents for software development?▼

Control AI agent development costs by applying tiered model routing, matching task complexity to appropriate model tiers like Haiku for simple edits and Opus for complex analysis to optimize spending.

What is eval-first AI-assisted development and how does it work?▼

Eval-first AI-assisted development defines capability and regression evaluations upfront before implementation, measuring impact and catching regressions early to ensure reliable software delivery.

How do I decompose software engineering tasks for AI agents to reduce risk?▼

Decompose software engineering tasks into 15-minute independent units with clear done conditions, improving verifiability and reducing risk for AI-generated implementation work.

What's the best way to prevent regressions when refactoring with AI agents?▼

Prevent regressions during AI-assisted refactoring by enforcing structured eval-first execution, validating changes with automated regression evaluations before code reaches production.

Can I use different model tiers for different parts of an AI coding workflow?▼

Yes, you can route different workflow stages to different model tiers, using cheaper models for simple boilerplate edits and advanced models for complex implementation and analysis.

Why does unstructured AI agent engineering lead to uncontrolled costs and errors?▼

Unstructured AI agent workflows cause inconsistent output and uncontrolled costs because they lack quality guardrails, task decomposition, and predefined regression evals to catch errors.

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