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agentic-engineering

Enforce eval-first processes for AI agent-led software engineering workflows.

Updated May 9, 2026
Search Tags:#code review#task decomposition#agentic engineering#cost optimization#model routing#eval-first
One-click install
npx skills add https://github.com/RambleRainbow/jd --skill agentic-engineering-ramblerainbow
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: agentic-engineering
Source: https://github.com/RambleRainbow/jd/tree/main/.claude/skills/agentic-engineering
Command: npx skills add https://github.com/RambleRainbow/jd --skill agentic-engineering-ramblerainbow

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill eliminates the chaos of uncoordinated AI agent engineering work that leads to inconsistent code quality, missed edge cases, and uncontrolled model costs, giving teams a repeatable, reliable workflow for agent-led development.

Core Features & Use Cases

  • Eval-First Execution: Define capability and regression evals upfront to measure implementation impact and catch regressions immediately.
  • Task Decomposition: Break work into 15-minute independent units with clear done conditions to reduce risk and improve verifiability.
  • Cost-Aware Model Routing: Match model tiers (Haiku, Sonnet, Opus) to task complexity to balance performance and cost.
  • Use Case: When building a multi-file code refactor, use this Skill to decompose the work into verifiable units, route implementation tasks to Sonnet and architecture reviews to Opus, and run regression evals after each unit to catch breaking changes.

Quick Start

Use the agentic-engineering skill to plan and execute your next AI agent implementation project with built-in eval checks and cost-optimized 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 unpredictable costs in AI agent-led software engineering?▼

Cost-aware model routing balances AI agent engineering expenses by matching model tiers like Haiku, Sonnet, and Opus to task complexity, ensuring performance is maintained while minimizing uncontrolled model costs.

What is an eval-first process for AI code generation?▼

An eval-first process for AI code generation defines capability and regression evaluations upfront to measure implementation impact, catch regressions immediately, and deliver production-ready code with minimized risk.

How do I decompose multi-file refactoring tasks for AI agents?▼

Decompose multi-file refactoring tasks by breaking work into 15-minute independent units with clear done conditions, reducing risk and improving verifiability for AI agent-led software engineering workflows.

Can I use tiered model routing for root-cause analysis and code reviews?▼

Yes, tiered model routing supports root-cause analysis and code reviews by assigning complex architecture evaluations to advanced models like Opus, while routing standard implementation tasks to Sonnet.

What's the best way to ensure code quality in automated agent workflows?▼

The best way to ensure code quality in automated agent workflows is enforcing structured processes with upfront regression testing, human review of invariants, and edge case evaluation to eliminate inconsistent quality.

Why does uncoordinated AI agent engineering lead to missed edge cases?▼

Uncoordinated AI agent engineering leads to missed edge cases due to a lack of structured, eval-first execution that defines capability evaluations upfront and enforces human review of security assumptions.

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