prompt-template-design
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Prompt Template Design Skill
Capabilities
- Create structured prompt templates with variables
- Implement prompt formatting with different styles
- Design system/user/assistant message templates
- Handle dynamic context injection
- Implement prompt versioning and management
- Create reusable prompt components
Target Processes
- prompt-engineering-workflow
- system-prompt-guardrails
Implementation Details
Template Patterns
- LangChain PromptTemplate: Variable-based templates
- ChatPromptTemplate: Message-based templates
- FewShotPromptTemplate: With example selection
- PipelinePromptTemplate: Composed templates
Configuration Options
- Variable names and defaults
- Input validation rules
- Output format specification
- Partial variable handling
- Template inheritance
Best Practices
- Clear variable naming conventions
- Structured output instructions
- Version control for templates
- Testing with edge cases
- Documentation of template purpose
Dependencies
- langchain-core
Source
git clone https://github.com/a5c-ai/babysitter/blob/main/plugins/babysitter/skills/babysit/process/specializations/ai-agents-conversational/skills/prompt-template-design/SKILL.mdView on GitHub Overview
Design reusable prompt templates that use variables, formatting styles, and version control. It covers LangChain and message-based patterns, with configuration for defaults, validation, and template inheritance to boost consistency and reusability.
How This Skill Works
Templates are defined using patterns like LangChain PromptTemplate, ChatPromptTemplate, FewShotPromptTemplate, and PipelinePromptTemplate. They support variables, defaults, output formats, partial variable handling, and inheritance, with versioning to manage changes over time.
When to Use It
- Building dynamic, data-driven conversations where user/context data changes per interaction
- Standardizing system prompts and guardrails across multiple agents
- Creating few-shot exemplars with consistent formatting and selection logic
- Managing template versions and updates across releases or teams
- Assembling reusable prompt components into complex agent pipelines
Quick Start
- Step 1: Define variables, defaults, and the desired output format for the target prompt
- Step 2: Choose a pattern (LangChain PromptTemplate, ChatPromptTemplate, FewShot, or Pipeline) and implement it
- Step 3: Add versioning, partial variable handling, and test with edge cases
Best Practices
- Use clear, descriptive variable names and document their purpose
- Define a consistent output format and validation rules
- Enable version control for all templates and track changes
- Test templates with edge cases and across languages/tones
- Document the template's purpose and how components compose
Example Use Cases
- LangChain PromptTemplate with variables like userName, topic, and tone for a personalized answer
- ChatPromptTemplate for multi-turn agent conversations with system/user/assistant messages
- FewShotPromptTemplate that selects and formats demonstrations for consistent responses
- PipelinePromptTemplate that sequences instruction, reasoning, and final answer steps
- Inheritance-based templates that share guards and formatting across system, user, and assistant roles
Frequently Asked Questions
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