Agent Skills
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ai-engineer
GitHub专注于端到端AI系统开发，涵盖LLM集成、RAG架构、向量搜索及智能体构建。提供从API对接到生产部署的最佳实践与工作流，适用于构建AI功能、聊天机器人及优化延迟成本，但不涉及底层模型训练或基础设施运维。
Trigger Scenarios
构建LLM驱动的应用或功能
实现RAG系统和向量数据库检索
集成OpenAI等AI API
设计嵌入和检索管道
开发对话式AI或智能体
Install
npx skills add NeverSight/learn-skills.dev --skill ai-engineer -g -y
SKILL.md
Frontmatter
{
"name": "ai-engineer",
"description": "Expert in building comprehensive AI systems, integrating LLMs, RAG architectures, and autonomous agents into production applications. Use when building AI-powered features, implementing LLM integrations, designing RAG pipelines, or deploying AI systems."
}
AI Engineer
Purpose
Provides expertise in end-to-end AI system development, from LLM integration to production deployment. Covers RAG architectures, embedding strategies, vector databases, prompt engineering, and AI application patterns.
When to Use
- Building LLM-powered applications or features
- Implementing RAG (Retrieval-Augmented Generation) systems
- Integrating AI APIs (OpenAI, Anthropic, etc.)
- Designing embedding and vector search pipelines
- Building chatbots or conversational AI
- Implementing AI agents with tool use
- Optimizing AI system latency and cost
Quick Start
Invoke this skill when:
- Building LLM-powered applications or features
- Implementing RAG systems with vector databases
- Integrating AI APIs into applications
- Designing embedding and retrieval pipelines
- Building conversational AI or agents
Do NOT invoke when:
- Training custom ML models from scratch (use ml-engineer)
- Deploying ML models to production infrastructure (use mlops-engineer)
- Managing multi-agent coordination (use agent-organizer)
- Optimizing LLM serving infrastructure (use llm-architect)
Decision Framework
AI Feature Type:
├── Simple Q&A → Direct LLM API call
├── Knowledge-based answers → RAG pipeline
├── Multi-step reasoning → Chain-of-thought or agents
├── External actions needed → Tool-use agents
├── Real-time data → Streaming + function calling
└── Complex workflows → Multi-agent orchestration
Core Workflows
1. RAG Pipeline Implementation
- Chunk documents with appropriate strategy
- Generate embeddings using suitable model
- Store in vector database with metadata
- Implement semantic search with reranking
- Construct prompts with retrieved context
- Add evaluation and monitoring
2. LLM Integration
- Select appropriate model for use case
- Design prompt templates with versioning
- Implement structured output parsing
- Add retry logic and fallbacks
- Monitor token usage and costs
- Cache responses where appropriate
3. AI Agent Development
- Define agent capabilities and tools
- Implement tool interfaces with validation
- Design agent loop with termination conditions
- Add guardrails and safety checks
- Implement logging and tracing
- Test edge cases and failure modes
Best Practices
- Version prompts alongside application code
- Use structured outputs (JSON mode) for reliability
- Implement semantic caching for common queries
- Add human-in-the-loop for critical decisions
- Monitor hallucination rates and retrieval quality
- Design for graceful degradation when AI fails
Anti-Patterns
| Anti-Pattern | Problem | Correct Approach |
|---|---|---|
| Prompt in code | Hard to iterate and test | Use prompt templates with versioning |
| No evaluation | Unknown quality in production | Implement eval pipelines |
| Synchronous LLM calls | Slow user experience | Use streaming responses |
| Unbounded context | Token limits and cost | Implement context windowing |
| No fallbacks | System fails on API errors | Add retry logic and alternatives |
Version History
- e0220ca Current 2026-07-05 21:10
