OpenSkillIndex← back to search
claude-codedevelopment

Babysitter few-shot-example-gen

Few-shot example generation and optimization for improved LLM performance

install
source · Clone the upstream repo
git clone https://github.com/a5c-ai/babysitter
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/a5c-ai/babysitter "$T" && mkdir -p ~/.claude/skills && cp -r "$T/library/specializations/ai-agents-conversational/skills/few-shot-example-gen" ~/.claude/skills/a5c-ai-babysitter-few-shot-example-gen && rm -rf "$T"
manifest: library/specializations/ai-agents-conversational/skills/few-shot-example-gen/SKILL.md
tags
#example-generation#prompt-engineering#llm-optimization#few-shot-learning#example-selection
source content

Few-Shot Example Generation Skill

Capabilities

  • Generate diverse few-shot examples
  • Implement example selection strategies
  • Optimize example ordering for performance
  • Create dynamic example retrieval
  • Design example formats for specific tasks
  • Implement example quality validation

Target Processes

  • prompt-engineering-workflow
  • intent-classification-system

Implementation Details

Example Selection Strategies

  1. Semantic Similarity: Select similar examples
  2. MMR Selection: Diverse example selection
  3. N-Gram Overlap: Lexical similarity
  4. Random Sampling: Baseline selection
  5. Length-Based: Control example sizes

Configuration Options

  • Number of examples
  • Selection algorithm
  • Example format (input/output structure)
  • Max token limits
  • Example store backend

Best Practices

  • Cover edge cases in examples
  • Balance example diversity
  • Optimize example ordering
  • Test with varied inputs
  • Monitor token usage

Dependencies

  • langchain
  • sentence-transformers (for semantic selection)