From prompt-architecture
Guides crafting effective few-shot prompting examples to steer AI behavior, with principles for diversity, clarity, quality, ordering, counts, and anti-patterns.
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Slash command
/prompt-architecture:few-shot-patternsThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Few-shot prompting provides examples of input-output pairs that demonstrate the desired behavior. The AI learns the pattern from the examples and applies it to new inputs. The quality of examples directly determines the quality of outputs.
Few-shot prompting provides examples of input-output pairs that demonstrate the desired behavior. The AI learns the pattern from the examples and applies it to new inputs. The quality of examples directly determines the quality of outputs.
Examples communicate what instructions alone cannot:
Diversity: Examples should cover different scenarios, not repeat the same type
npx claudepluginhub owl-listener/ai-design-skills --plugin prompt-architectureTeaches prompt engineering patterns including few-shot learning, chain-of-thought prompting, prompt optimization, and template systems. Useful for improving LLM output reliability, debugging agent behavior, or learning prompting strategies.
Provides prompt engineering patterns including few-shot learning, chain-of-thought prompting, optimization techniques, and templates. Improves LLM performance, reliability, and agent debugging.
Provides workflows to write, debug, and optimize LLM prompts using few-shot examples, chain-of-thought structuring, system prompts, and templates. Activates for prompt improvement requests.