prompt-engineering-patterns
This skill should be used when the user asks to "optimize a prompt", "improve prompt performance", "design a prompt template", "write better prompts", "debug prompt issues", "use chain-of-thought", "structured prompting", "few-shot prompting", or wants to apply advanced prompt engineering patterns for production LLM applications.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/wshobson/agents --skill prompt-engineering-patterns- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads prompt-engineering-patterns automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
Security audits
Independent scans of the skill's instructions and bundled files.
SKILL.md
The instructions your agent reads when the skill is triggered.
Prompt Engineering Patterns
Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability.
When to Use This Skill
- Designing complex prompts for production LLM applications
- Optimizing prompt performance and consistency
- Implementing structured reasoning patterns (chain-of-thought, tree-of-thought)
- Building few-shot learning systems with dynamic example selection
- Creating reusable prompt templates with variable interpolation
- Debugging and refining prompts that produce inconsistent outputs
- Implementing system prompts for specialized AI assistants
- Using structured outputs (JSON mode) for reliable parsing
Core Capabilities
1. Few-Shot Learning
- Example selection strategies (semantic similarity, diversity sampling)
- Balancing example count with context window constraints
- Constructing effective demonstrations with input-output pairs
- Dynamic example retrieval from knowledge bases
- Handling edge cases through strategic example selection
2. Chain-of-Thought Prompting
- Step-by-step reasoning elicitation
- Zero-shot CoT with "Let's think step by step"
- Few-shot CoT with reasoning traces
- Self-consistency techniques (sampling multiple reasoning paths)
- Verification and validation steps
3. Structured Outputs
- JSON mode for reliable parsing
- Pydantic schema enforcement
- Type-safe response handling
- Error handling for malformed outputs
4. Prompt Optimization
- Iterative refinement workflows
- A/B testing prompt variations
- Measuring prompt performance metrics (accuracy, consistency, latency)
- Reducing token usage while maintaining quality
- Handling edge cases and failure modes
5. Template Systems
- Variable interpolation and formatting
- Conditional prompt sections
- Multi-turn conversation templates
- Role-based prompt composition
- Modular prompt components
6. System Prompt Design
- Setting model behavior and constraints
- Defining output formats and structure
- Establishing role and expertise
- Safety guidelines and content policies
- Context setting and background information
Quick Start
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-5")
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 patterns and worked examples
Detailed pattern documentation lives in references/details.md. Read that file when the navigation tier above is insufficient.
Best Practices
- Be Specific: Vague prompts produce inconsistent results
- Show, Don't Tell: Examples are more effective than descriptions
- Use Structured Outputs: Enforce schemas with Pydantic for reliability
- Test Extensively: Evaluate on diverse, representative inputs
- Iterate Rapidly: Small changes can have large impacts
- Monitor Performance: Track metrics in production
- Version Control: Treat prompts as code with proper versioning
- Document Intent: Explain why prompts are structured as they are
Common Pitfalls
- Over-engineering: Starting with complex prompts before trying simple ones
- Example pollution: Using examples that don't match the target task
- Context overflow: Exceeding token limits with excessive examples
- Ambiguous instructions: Leaving room for multiple interpretations
- Ignoring edge cases: Not testing on unusual or boundary inputs
- No error handling: Assuming outputs will always be well-formed
- Hardcoded values: Not parameterizing prompts for reuse
Success Metrics
Track these KPIs for your prompts:
- Accuracy: Correctness of outputs
- Consistency: Reproducibility across similar inputs
- Latency: Response time (P50, P95, P99)
- Token Usage: Average tokens per request
- Success Rate: Percentage of valid, parseable outputs
- User Satisfaction: Ratings and feedback
FAQ
What does the prompt-engineering-patterns skill do?
This skill should be used when the user asks to "optimize a prompt", "improve prompt performance", "design a prompt template", "write better prompts", "debug prompt issues", "use chain-of-thought", "structured prompting", "few-shot prompting", or wants to apply advanced prompt engineering patterns for production LLM applications.
How do I install prompt-engineering-patterns?
Run npx skills add https://github.com/wshobson/agents --skill prompt-engineering-patterns in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/prompt-engineering-patterns/ (personal) or .claude/skills/prompt-engineering-patterns/ (project).
Which AI agents can use prompt-engineering-patterns?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is prompt-engineering-patterns safe to install?
Security partners have reviewed it: Gen Agent Trust Hub passed; Socket passed; Snyk passed; Runlayer passed; ZeroLeaks passed. As with any skill, read the SKILL.md and bundled scripts before installing.
Who maintains prompt-engineering-patterns?
It is published by wshobson in wshobson/agents. It has 23,602 recorded installs.
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