陌讯skills聚合平台

ai-engineering

📁 doanchienthangdev/omgkit 📅 8 days ago
2
总安装量
2
周安装量
#73750
全站排名
安装命令
npx skills add https://github.com/doanchienthangdev/omgkit --skill ai-engineering

Agent 安装分布

opencode 2
gemini-cli 2
antigravity 2
claude-code 2
github-copilot 2
codex 2

Skill 文档

AI Engineering Skills

Comprehensive skills for building AI applications with Foundation Models.

AI Engineering Stack

┌─────────────────────────────────────────────────────┐
│  APPLICATION LAYER                                   │
│  Prompt Engineering, RAG, Agents, Guardrails        │
├─────────────────────────────────────────────────────┤
│  MODEL LAYER                                         │
│  Model Selection, Finetuning, Evaluation            │
├─────────────────────────────────────────────────────┤
│  INFRASTRUCTURE LAYER                                │
│  Inference Optimization, Caching, Orchestration     │
└─────────────────────────────────────────────────────┘

12 Core Skills

Skill Description Guide
Foundation Models Model architecture, sampling, structured outputs foundation-models/
Evaluation Methodology Metrics, AI-as-judge, comparative evaluation evaluation-methodology/
AI System Evaluation End-to-end evaluation, benchmarks, model selection ai-system-evaluation/
Prompt Engineering System prompts, few-shot, chain-of-thought, defense prompt-engineering/
RAG Systems Chunking, embedding, retrieval, reranking rag-systems/
AI Agents Tool use, planning strategies, memory systems ai-agents/
Finetuning LoRA, QLoRA, PEFT, model merging finetuning/
Dataset Engineering Data quality, curation, synthesis, annotation dataset-engineering/
Inference Optimization Quantization, batching, caching, speculative decoding inference-optimization/
AI Architecture Gateway, routing, observability, deployment ai-architecture/
Guardrails & Safety Input/output guards, PII protection, injection defense guardrails-safety/
User Feedback Explicit/implicit signals, feedback loops, A/B testing user-feedback/

Development Process

1. Use Case Evaluation → 2. Model Selection → 3. Evaluation Pipeline
                                                      ↓
4. Prompt Engineering → 5. Context (RAG/Agents) → 6. Finetuning (if needed)
                                                      ↓
7. Inference Optimization → 8. Deployment → 9. Monitoring & Feedback

Quick Decision Guide

Need Start With
Improve output quality prompt-engineering
Add external knowledge rag-systems
Multi-step reasoning ai-agents
Reduce latency/cost inference-optimization
Measure quality evaluation-methodology
Protect system guardrails-safety

Reference

Based on “AI Engineering” by Chip Huyen (O’Reilly, 2025).

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