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From llm-application-dev
Provides advanced prompt engineering patterns for production LLM applications: few-shot learning, chain-of-thought, structured outputs, and template systems.
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/llm-application-dev:prompt-engineering-patternsThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability.
Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability.
from langchain_anthropic import ChatAnthropic
from langchain_core.prompts import ChatPromptTemplate
from pydantic import BaseModel, Field
# Define structured output schema
class SQLQuery(BaseModel):
query: str = Field(description="The SQL query")
explanation: str = Field(description="Brief explanation of what the query does")
tables_used: list[str] = Field(description="List of tables referenced")
# Initialize model with structured output
llm = ChatAnthropic(model="claude-sonnet-4-6")
structured_llm = llm.with_structured_output(SQLQuery)
# Create prompt template
prompt = ChatPromptTemplate.from_messages([
("system", """You are an expert SQL developer. Generate efficient, secure SQL queries.
Always use parameterized queries to prevent SQL injection.
Explain your reasoning briefly."""),
("user", "Convert this to SQL: {query}")
])
# Create chain
chain = prompt | structured_llm
# Use
result = await chain.ainvoke({
"query": "Find all users who registered in the last 30 days"
})
print(result.query)
print(result.explanation)
Detailed pattern documentation lives in references/details.md. Read that file when the navigation tier above is insufficient.
Track these KPIs for your prompts:
npx claudepluginhub yo-steven/agents-exploration-20260523 --plugin llm-application-devApplies advanced prompt engineering patterns including few-shot, chain-of-thought, structured outputs, and prompt optimization for production LLM applications.
Applies prompt engineering patterns like few-shot, chain-of-thought, structured JSON outputs, optimization, and templates to boost LLM reliability and performance in production apps.
Provides structured techniques for designing, optimizing, and debugging prompts for production LLM applications. Covers few-shot learning, chain-of-thought, template systems, and system prompt design.