llm-engineering
Patterns for building LLM applications - prompt engineering, RAG pipelines, cost optimization, multi-model routing, and evaluation. Auto-triggers when working with AI/LLM code.
npx skills add majiayu000/claude-skill-registry --skill llm-engineering-bigdegenenergy-open-cloud-ops --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# LLM Engineering Skill ## Prompt Engineering Patterns ### Structured Output ```python # Force JSON output with schema response = client.messages.create( model="claude-sonnet-4-5-20250929", messages=[{"role": "user", "content": prompt}], system="Respond with valid JSON matching this schema: {\"name\": str, \"score\": float}", ) # Anthropic tool_use for guaranteed structured output response = client.messages.create( model="claude-sonnet-4-5-20250929", tools=[{ "name": "extract_data", "description": "Extract structured data", "input_schema": { "type": "object", "properties": { "name": {"type": "string"}, "score": {"type": "number"}, }, "required": ["name", "score"], }, }], tool_choice={"type": "tool", "name": "extract_data"}, messages=[{"role": "user", "content": prompt}], ) ``` ### Few-Shot Prompting ```python EXAMPLES = """ Input: "The movie was terrible" Output: {"sentiment": "negative", "confidence": 0.95} Input: "I loved every minute of it" Output: {"sentiment": "positive", "confidence": 0.98} Input: "It was okay, nothing special" Output: {"sentiment": "neutral", "confidence": 0.72} """ prompt = f"{EXAMPLES}\n\nInput: \"{user_text}\"\nOutput:" ``` ### Chain of Thought ```python
- Prompt Engineering Patterns
- Structured Output
- Few-Shot Prompting
- Chain of Thought
- Multi-Model Routing
- Cost-Performance Tiers
- Router Pattern
- Fallback Chain
- RAG Pipeline Patterns
- Chunking Strategies
- Retrieval Pipeline
- Embedding Best Practices
- Cost Optimization
- Token Counting
What does the llm-engineering skill do?
Patterns for building LLM applications - prompt engineering, RAG pipelines, cost optimization, multi-model routing, and evaluation. Auto-triggers when working with AI/LLM code.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill llm-engineering-bigdegenenergy-open-cloud-ops --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.