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Found 1,066 Skills
Reddit community moderation via PRAW with LLM-powered report classification: fetch modqueue, classify reports against subreddit rules and author history, and take mod actions (approve, remove, lock). Supports interactive, auto, and dry-run modes.
Build generative UI apps with OpenUI and OpenUI Lang — the token-efficient open standard for LLM-generated interfaces. Use when mentioning OpenUI, @openuidev, generative UI, streaming UI from LLMs, component libraries for AI, or replacing json-render/A2UI. Covers scaffolding, defineComponent, system prompts, the Renderer, and debugging OpenUI Lang output.
Convert files, URLs, and media to markdown using the markit-ai CLI and SDK with pluggable converters and LLM support.
MacOS voice input tool with local/cloud ASR engines, LLM text optimization, and fully local storage built in Swift
PyTorch implementation of TurboQuant for LLM KV cache compression using two-stage vector quantization (random rotation + Lloyd-Max + QJL residual correction).
Fetch and compile arXiv papers on LLMs, autonomous agents, and AI infrastructure into scored, grouped research digests. Stores digests at ~/.aibtc/arxiv-research/digests/. No API key required.
Fine-tune LLMs using the Tinker API. Covers supervised fine-tuning, reinforcement learning, LoRA training, vision-language models, and both high-level Cookbook patterns and low-level API usage.
Transform code, issues, or context into a detailed prompt/context for another LLM to fix or implement. Use when preparing comprehensive context for external LLM assistance, bug fixes, improvements, or feature implementations. Provides detailed context without implementation suggestions, letting the receiving LLM decide how to implement solutions. Focuses on "what" (problem, requirements, current state) not "how" (implementation approach).
AI-first coding guidelines for projects maintained by LLMs. Use when creating new code, refactoring, or reviewing code to optimize for model reasoning, regenerability, and debugging; applies to layout, architecture, functions, naming, logging, platform use, and tests.
Atlas Cloud API integration skill — quickly call 300+ AI image generation, video generation, and LLM models through a unified API. Use this skill when the user needs to integrate AI image generation (e.g., Flux, Seedream, DALL-E), AI video generation (e.g., Kling, Sora, Seedance), or call LLM APIs (OpenAI-compatible format) into their project. Applicable scenarios include: generating images, generating videos, calling large language models, using Atlas Cloud API, configuring ATLASCLOUD_API_KEY, querying available model lists, searching models by keyword, uploading local images/media files, one-step quick generation, image-to-video, text-to-image, text-to-video, AI content creation tool integration. Even if the user doesn't explicitly mention Atlas Cloud, this skill should be considered whenever AI media generation API integration development is involved.
This skill should be used when the user asks to "evaluate agent performance", "build test framework", "measure agent quality", "create evaluation rubrics", or mentions LLM-as-judge, multi-dimensional evaluation, agent testing, or quality gates for agent pipelines. Part of the context engineering skill suite — also activates when the user mentions "context engineering" or "context-engineering" in the context of measuring agent effectiveness.
List available LLM-accessible credentials. Use when you need API keys, passwords, or other secrets that have been made available to you.