About
Promptify transforms user requests into detailed, optimized prompts for AI models, enhancing clarity and specificity. It analyzes intent, adds missing context, and structures prompts to eliminate ambiguity for better responses. Use it when a user asks to "promptify" or explicitly needs prompt engineering assistance.
Quick Install
Claude Code
Recommendednpx skills add ravnhq/ai-toolkit -a claude-code/plugin add https://github.com/ravnhq/ai-toolkitgit clone https://github.com/ravnhq/ai-toolkit.git ~/.claude/skills/promptifyCopy and paste this command in Claude Code to install this skill
GitHub Repository
Frequently asked questions
What is the promptify skill?
promptify is a Claude Skill by ravnhq. Skills package instructions and resources that Claude loads on demand, so Claude can perform promptify-related tasks without extra prompting.
How do I install promptify?
Use the install commands on this page: add promptify to Claude Code as a plugin, or clone its repository into your skills directory, then restart Claude so it picks up the skill.
What category does promptify belong to?
promptify is in the Other category, tagged ai.
Is promptify free to use?
Yes. promptify is listed on AIMCP and free to install. It runs inside Claude, so no separate service account is required to use the skill itself.
Related Skills
LlamaGuard is Meta's 7-8B parameter model for moderating LLM inputs and outputs across six safety categories like violence and hate speech. It offers 94-95% accuracy and can be deployed using vLLM, Hugging Face, or Amazon SageMaker. Use this skill to easily integrate content filtering and safety guardrails into your AI applications.
This Claude Skill helps developers optimize cloud costs through resource rightsizing, tagging strategies, and spending analysis. It provides a framework for reducing cloud expenses and implementing cost governance across AWS, Azure, and GCP. Use it when you need to analyze infrastructure costs, right-size resources, or meet budget constraints.
This Claude Skill analyzes sports betting markets including spreads, over/unders, and prop bets by examining historical trends and situational statistics to identify value bets. It provides structured markdown output with actionable recommendations for educational purposes. Developers should use this for sports betting analysis tools while noting it's designed for entertainment/education only.
This skill quantizes LLMs to 8-bit or 4-bit precision using bitsandbytes, achieving 50-75% memory reduction with minimal accuracy loss. It's ideal for running larger models on limited GPU memory or accelerating inference, supporting formats like INT8, NF4, and FP4. The skill integrates with HuggingFace Transformers and enables QLoRA training and 8-bit optimizers.
