few-shot-example-gen
Few-shot example generation and optimization for improved LLM performance
npx skills add a5c-ai/babysitter --skill few-shot-example-gen --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.
# 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)
- Capabilities
- Target Processes
- Implementation Details
- Example Selection Strategies
- Configuration Options
- Best Practices
- Dependencies
What does the few-shot-example-gen skill do?
Few-shot example generation and optimization for improved LLM performance
How do I install it?
Run `npx skills add a5c-ai/babysitter --skill few-shot-example-gen --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 a5c-ai/babysitter, a repository with 1,642 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.