Prompt Optimization skills

Arize Prompt Optimization

github

Enhance LLM prompts using data-driven insights.

Prompting

danielmiessler

Streamline your prompt engineering with a standard library.

HeyGen Video Generation

calesthio

Create talking-head videos and presentations effortlessly.

AI Prompt Engineering Safety Review

github

Enhance prompt safety and effectiveness with expert analysis.

DSPy

nousresearch

Declarative programming for language models.

AI.MD Conversion System

sickn33

Transform CLAUDE.md into structured AI-friendly formats.

Prompt Engineering

davila7

Enhance your prompt strategies for better AI outcomes.

Evolving AI Agents

orchestra-research

Automatically optimize AI agents across any domain.

Prompt Master

nidhinjs

Optimizes prompts for AI tools with precision.

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Senior Prompt Engineer

alirezarezvani

Optimize prompts and evaluate LLM outputs efficiently.

Prompt Optimizer

affaan-m

Enhance your prompts for better AI interactions.

Prompt Engineering Patterns

davila7

Enhance LLM performance with advanced prompt techniques.

Evolve prompts/regex/SQL/code with Imbue's evolution loop.

Turn any rough prompt, half-formed idea, or task description into a finished, ready-to-send prompt optimized for any LLM model inside a chat interface — NOT the API. Use this skill whenever the user wants to write, rewrite, optimize, improve, sharpen, or polish a prompt for chat. Trigger phrases include "rewrite this prompt", "make this a better prompt", "optimize this prompt", "turn this into a prompt", "help me prompt this", "draft a prompt that...", "I want to ask...", or whenever the user pastes a draft prompt and asks for improvements. Also trigger when the user describes a task they plan to send to an LLM model and clearly wants a reusable, well-structured prompt rather than a direct answer. The output is always a single, copy-pasteable prompt in a code block that the user sends as-is — never a template with placeholders.

Transform vague prompts into precise, well-structured specifications using EARS (Easy Approach to Requirements Syntax) methodology. This skill should be used when users provide loose requirements, ambiguous feature descriptions, or need to enhance prompts for AI-generated code, products, or documents. Triggers include requests to "optimize my prompt", "improve this requirement", "make this more specific", or when raw requirements lack detail and structure.