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.
npx claudepluginhub owl-listener/ai-design-skills --plugin prompt-architectureThis skill uses the workspace's default tool permissions.
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.
Provides expert guide on prompt engineering patterns like few-shot learning, chain-of-thought prompting, optimization, and template systems for improving LLM prompts and debugging agent behavior.
Designs and optimizes prompts for LLM apps, mastering structure, system/user messages, few-shot examples, chain-of-thought, output parsing, chaining, evaluation, and token optimization. Use for prompt engineering tasks.
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.
Share bugs, ideas, or general feedback.
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