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Building production AI applications with Foundation Models. Covers prompt engineering, RAG, agents, finetuning, evaluation, and deployment. Use when working with LLMs, building AI features, or architecting AI systems.
デフォルトでは、最初にソースを確認する Prompt が選択されています。直接コマンドに切り替えるか、ローカルコピーをダウンロードすることもできます。
インストールを決める前に、SKILL.md と SkillsMP に表示されている付属ファイルをお読みください。
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
直接コマンドでは確認用 Prompt が省略されます。実行前にソースを確認してください。
npx skills add https://github.com/doanchienthangdev/omgkit --skill ai-engineeringコマンドは1行のまま表示されます。コピー前に横へスクロールして全体を確認してください。
ローカルで確認しますか？SkillsMP が現在取得できるファイルをダウンロードできます。
Automatic design system context injection for UI consistency
AI agent practices test-first development with the Red-Green-Refactor cycle for confident, well-designed code. Use when implementing features, fixing bugs, or establishing testing practices.
The agent enforces mandatory test completion before any task or feature can be marked as done, ensuring code quality through strict validation gates and evidence-based completion criteria.
SOC 職業分類に基づく
SKILL.md を表示中
| name | ai-engineering |
| description | Building production AI applications with Foundation Models. Covers prompt engineering, RAG, agents, finetuning, evaluation, and deployment. Use when working with LLMs, building AI features, or architecting AI systems. |
Comprehensive skills for building AI applications with Foundation Models.
┌─────────────────────────────────────────────────────┐
│ APPLICATION LAYER │
│ Prompt Engineering, RAG, Agents, Guardrails │
├─────────────────────────────────────────────────────┤
│ MODEL LAYER │
│ Model Selection, Finetuning, Evaluation │
├─────────────────────────────────────────────────────┤
│ INFRASTRUCTURE LAYER │
│ Inference Optimization, Caching, Orchestration │
└─────────────────────────────────────────────────────┘
| 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/ |
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
| 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 |
Based on "AI Engineering" by Chip Huyen (O'Reilly, 2025).