OpenClaw

ai-engineer

AI/ML engineering specialist for building intelligent features, RAG systems, LLM integrations, data pipelines, vector search, and AI-powered applications. Use when building anything involving: LLMs, embeddings, vector databases, RAG, fine-tuning, prompt engineering, AI agents, ML pipelines, or deploying models to production. NOT for general web dev (use rapid-prototyper) or simple API calls.

bullkis10 来源安装数☆ 0 仓库 Stars

查看来源 ↗安装与使用 SKILL.md ↗

安装与使用

请根据来源仓库和你的 AI 工具说明安装此技能。先阅读技能文件和依赖要求，再添加到工作流。

SkillHub CLI

skillhub install bullkis1/ai-engineer

打开来源仓库，查看说明和兼容环境后，按来源提供的命令或步骤安装。

将来源链接提供给你的 Agent，让它先读取说明、核对依赖，并向你说明将执行的安装步骤。

查看来源 ↗使用指南

技能说明

AI Engineer

Build practical AI systems that work in production. Data-driven, systematic, performance-focused.

Core Capabilities

LLM Integration: OpenAI, Anthropic, local models (Ollama, llama.cpp), LiteLLM
RAG Systems: Chunking, embeddings, vector search, retrieval, re-ranking
Vector DBs: Chroma (local), Pinecone (managed), Weaviate, FAISS, Qdrant
Agents & Tools: Tool-calling, multi-step agents, OpenClaw sub-agents
Data Pipelines: Ingestion, cleaning, transformation, feature engineering
MLOps: Model versioning (MLflow), monitoring, drift detection, A/B testing
Evaluation: Benchmark construction, bias testing, performance metrics

Decision Framework

Which LLM provider?

Prototyping/speed: OpenAI GPT-4o or Anthropic Claude Sonnet
Local/private: Ollama + Qwen 2.5 32B or Llama 3.3 70B
Multi-provider abstraction: LiteLLM (swap models without code changes)
Embeddings: text-embedding-3-small (OpenAI) or nomic-embed-text (local)

Which vector DB?

Local/dev: Chroma (zero setup)
Production managed: Pinecone
Self-hosted production: Qdrant or Weaviate
Already in Postgres: pgvector extension

RAG or fine-tuning?

RAG first — always try RAG before fine-tuning. 90% of cases RAG is enough.
Fine-tune only when: style/tone change needed, domain vocab is highly specialized, latency must be minimal

RAG Workflow

1. Ingest

# Chunk documents (rule of thumb: 512 tokens, 50 overlap)
from langchain.text_splitter import RecursiveCharacterTextSplitter
splitter = RecursiveCharacterTextSplitter(chunk_size=512, chunk_overlap=50)
chunks = splitter.split_documents(docs)

2. Embed + store

import chromadb
from chromadb.utils.embedding_functions import OpenAIEmbeddingFunction

client = chromadb.PersistentClient(path="./chroma_db")
ef = OpenAIEmbeddingFunction(api_key=os.environ["OPENAI_API_KEY"], model_name="text-embedding-3-small")
collection = c...

来源信息

Source: github
Repository: openclaw/skills
Path: skills/bullkis1/ai-engineer
Version: —
Commit: 71306517c6d62af8cea18230e8401363d8c22f85
分类来源: rules
状态: active
可见性: public

这些信息来自收录时的来源分析，可能滞后；使用前请核对最新来源文件。

来源内容

SKILL.md

# AI Engineer

Build practical AI systems that work in production. Data-driven, systematic, performance-focused.

## Core Capabilities

- **LLM Integration**: OpenAI, Anthropic, local models (Ollama, llama.cpp), LiteLLM
- **RAG Systems**: Chunking, embeddings, vector search, retrieval, re-ranking
- **Vector DBs**: Chroma (local), Pinecone (managed), Weaviate, FAISS, Qdrant
- **Agents & Tools**: Tool-calling, multi-step agents, OpenClaw sub-agents
- **Data Pipelines**: Ingestion, cleaning, transformation, feature engineering
- **MLOps**: Model versioning (MLflow), monitoring, drift detection, A/B testing
- **Evaluation**: Benchmark construction, bias testing, performance metrics

## Decision Framework

### Which LLM provider?
- **Prototyping/speed**: OpenAI GPT-4o or Anthropic Claude Sonnet
- **Local/private**: Ollama + Qwen 2.5 32B or Llama 3.3 70B
- **Multi-provider abstraction**: LiteLLM (swap models without code changes)
- **Embeddings**: text-embedding-3-small (OpenAI) or nomic-embed-text (local)

### Which vector DB?
- **Local/dev**: Chroma (zero setup)
- **Production managed**: Pinecone
- **Self-hosted production**: Qdrant or Weaviate
- **Already in Postgres**: pgvector extension

### RAG or fine-tuning?
- **RAG first** — always try RAG before fine-tuning. 90% of cases RAG is enough.
- Fine-tune only when: style/tone change needed, domain vocab is highly specialized, latency must be minimal

## RAG Workflow

### 1. Ingest
```python
# Chunk documents (rule of thumb: 512 tokens, 50 overlap)
from langchain.text_splitter import RecursiveCharacterTextSplitter
splitter = RecursiveCharacterTextSplitter(chunk_size=512, chunk_overlap=50)
chunks = splitter.split_documents(docs)
```

### 2. Embed + store
```python
import chromadb
from chromadb.utils.embedding_functions import OpenAIEmbeddingFunction

client = chromadb.PersistentClient(path="./chroma_db")
ef = OpenAIEmbeddingFunction(api_key=os.environ["OPENAI_API_KEY"], model_name="text-embedding-3-small")
collection = c...

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