Execute Hugging Face Hub operations using the hf CLI. Download models/datasets, upload files, manage repos, and run cloud compute jobs.
Find the best AI model for any task by querying Hugging Face leaderboards and benchmarks. Recommends top models based on task type, hardware constraints, and benchmark scores.
Add and manage evaluation results in Hugging Face model cards. Supports extracting eval tables from README content, importing scores from Artificial Analysis API, and running custom evaluations with vLLM/lighteval.
Explore, query, and extract data from any Hugging Face dataset using the Dataset Viewer REST API and npx tooling. Zero Python dependencies — covers split/config discovery, row pagination, text search, filtering, SQL via parquetlens, and dataset upload via CLI.
Build Gradio web UIs and demos in Python. Use when creating or editing Gradio apps, components, event listeners, layouts, or chatbots.
Train or fine-tune language models using TRL on Hugging Face Jobs infrastructure. Covers SFT, DPO, GRPO and reward modeling training methods, plus GGUF conversion for local deployment. Includes hardware selection, cost estimation, Trackio monitoring, and Hub persistence.
Use to select models to run locally with llama.cpp and GGUF on CPU, Mac Metal, CUDA, or ROCm. Covers finding GGUFs, quant selection, running servers, exact GGUF file lookup, conversion, and OpenAI-compatible local serving.
Build and publish a Gradio demo on Hugging Face Spaces for a user-provided LoRA. Use when someone asks to create, generate, ship, or publish a Space, demo, Gradio app, or playground for a LoRA — including LoRAs for Qwen-Image, Qwen-Image-Edit, LTX-Video, Wan, FLUX, SDXL, or other diffusion base models. Also triggers when someone describes a LoRA they trained or hosts on the Hub and wants to share it. Covers picking the right base pipeline and `diffusers` inference recipe, designing a UI tailored to the LoRA's task and inputs (Union/multi-task control, edit, video, image, etc.), respecting model-card recommendations (trigger words, steps, guidance, LoRA scale, example inputs), and shipping to ZeroGPU hardware as a private Space by default.
Publish and manage research papers on Hugging Face Hub. Supports creating paper pages, linking papers to models/datasets, claiming authorship, and generating professional markdown-based research articles.
Look up and read Hugging Face paper pages in markdown, and use the papers API for structured metadata like authors, linked models, datasets, Spaces, and media URLs when needed.
Build, deploy, and maintain applications on Hugging Face Spaces — Gradio / Docker / Static SDKs, ZeroGPU and dedicated hardware, model loading, debugging, buckets, inference providers, community grants. Use whenever the user asks to create or host an app on Hugging Face, port code onto ZeroGPU, fix a Space that won't build or run, or otherwise work with `hf spaces …`, `@spaces.GPU`, Space README frontmatter, or the `spaces` Python package.
Build reusable scripts for Hugging Face Hub and API workflows. Useful for chaining API calls, enriching Hub metadata, or automating repeated tasks.
Track and visualize ML training experiments with Trackio. Log metrics via Python API and retrieve them via CLI. Supports real-time dashboards synced to HF Spaces.
Train and fine-tune object detection models (RTDETRv2, YOLOS, DETR and others) and image classification models (timm and transformers models — MobileNetV3, MobileViT, ResNet, ViT/DINOv3) using Transformers Trainer API on Hugging Face Jobs infrastructure or locally. Includes COCO dataset format support, Albumentations augmentation, mAP/mAR metrics, trackio tracking, hardware selection, and Hub persistence.
Coding rules for Gradio Spaces using Hugging Face Spaces ZeroGPU hardware. Covers `@spaces.GPU`, duration and quota tuning, pickle-based process isolation, `gr.State` semantics across the worker boundary, the CUDA availability model, concurrency safety, and CUDA wheel-only build constraints.
Train or fine-tune sentence-transformers models across all three architectures: SentenceTransformer (bi-encoder embeddings), CrossEncoder (rerankers), and SparseEncoder (SPLADE). Covers loss selection, hard-negative mining, evaluators, distillation, LoRA, Matryoshka, and Hugging Face Hub publishing.
Run state-of-the-art machine learning models directly in JavaScript/TypeScript for NLP, computer vision, audio processing, and multimodal tasks. Works in Node.js and browsers with WebGPU/WASM using Hugging Face models.
Train and fine-tune transformer language models using TRL (Transformers Reinforcement Learning). Supports SFT, DPO, GRPO, KTO, RLOO and Reward Model training via CLI commands.
Generate or edit images from text and optional photos
Consult a council of LLMs for a refined answer
Extract text, formulas, or tables from images
Generate live web pages from text prompts
Generate full HTML web apps from text prompts
Generate and run Python or web code from a text prompt
Chat with an AI assistant using the LFM2.5 model
Chat with a multimodal AI that can answer text and image queries
Generate web app HTML/React code from a text description
Generate speech from text using voice design, cloning or presets
Chat with AI using text and images
Generate expressive speech from text with optional voice reference
Explore LLM benchmark leaderboards across multiple categories
Explore NLP tasks like sentiment, NER, QA, and summarization
Transform AI text into human-like writing and detect AI-generated content
Chat with an AI assistant to get instant answers
Chat with an AI assistant in real time
Chat with AI using text and images
Summarize health information in simple language
Generate or edit images from text and optional photos
Extract Korean vocabulary with translations from PDFs websites audio
Chat with an AI‑powered assistant
Analyze uploaded session logs and get AI-driven insights
Save and query personal notes with AI
Generate interactive graphs from blood test data
Watch an AI agent escape a maze in real time
Consult a council of LLMs for a refined answer
Generate natural-sounding speech from text
Explore and compare speech recognition model benchmarks
Generate a full HTML webpage from a text prompt
Chat with a multimodal AI using text and media
Generate images from text prompts with customizable settings
Generate videos from images and text prompts
Chat with an AI using text, images, audio, or video
Generate speech audio from text with optional voice cloning
Generate high‑quality images from text in seconds
Generate speech in any voice from text
Detect and label objects in images and videos
Analyze music and answer questions from audio or YouTube links
Transcribe audio to text with timestamps and downloadable files
Transcribe audio files into text instantly
Generate speech from text with optional voice cloning
Answer complex questions with web‑sourced research
Generate detailed captions for your images
Generate detailed prompts from any image
Extract text, formulas, or tables from images
Edit images with text prompts and style adapters
Transcribe audio to text with multilingual streaming support
A2A agent grounded in Joost de Valk's published posts and video transcripts. Answers natural-language questions about SEO, WordPress, the open web, AI, and open source, with a citation for every claim.
MCP server over Joost de Valk's writing on SEO, WordPress, AI, and the open web. ask_joost returns answers grounded in his posts and talks with source URLs; list_recent_content lists published posts filtered by topic and date.
REST API for registering schemas, circuits, and documents; submitting zero-knowledge proofs; and querying verified attributes. Agents use this API to integrate verifiable provenance into their workflows without handling plaintext data.
Verifiable provenance for AI agents — ZK proofs over confidential documents, no plaintext exposure. Register schemas, circuits, and documents; submit and query zero-knowledge proofs; verify attributes without revealing underlying data.
First stop in the Agentic Resource Discovery treasure hunt. Connect and call the tool to begin.
Export an entity's beneficial ownership and control graph as Beneficial Ownership Data Standard (BODS v0.4) statements in JSON, JSON-Lines or a zipped bundle (GET /export?lei=&format=json|jsonl|zip). Each entity statement carries the cross-reference identifiers GLEIF publishes against the LEI (BIC, MIC, ISIN, OpenCorporates, S&P CIQ, QCC) plus national company-register numbers, so the LEI connects to datasets worldwide.
Find a company's Legal Entity Identifier (LEI) from a name or a national company-registration number. Free-text fan-out search across GLEIF and connected registers (GET /search?q=&kind=entity), plus national-ID to LEI reverse resolution via GLEIF registration-authority codes. Use this first to obtain the LEI that drives the due-diligence lookup.
Run customer due diligence on a legal entity from its Legal Entity Identifier (LEI). Resolves the GLEIF record, fans out to 30+ national company registers and open datasets, maps results to the Beneficial Ownership Data Standard (BODS v0.4), and returns an ownership-and-control graph annotated with sanctions, PEP, debarment, offshore-leaks, FATF-jurisdiction and complex-structure risk signals. Primary endpoint: GET /lookup?lei=. Streaming variant for incremental results: GET /lookup-stream. Open data, no API key required.
Model Context Protocol server (streamable HTTP at https://api.opencheck.world/mcp) exposing OpenCheck as agent-native tools: opencheck_search and opencheck_resolve_national_id to obtain an LEI, opencheck_lookup for owners/controllers and sanctions/PEP/debarment risk, opencheck_export_bods for the BODS v0.4 ownership graph, and opencheck_list_sources. The most directly invocable surface for AI agents.
Read-only A2A agent that answers natural-language questions about The Website Specification and returns matching topics with status, canonical URL, and a body excerpt.
The entire specification as an Open Knowledge Format bundle: one Markdown concept per check, a mirrored concept per cited standard, plus index and change-log files. Browsable at /okf/ or downloadable as a single gzipped tar. The media type is interim and unregistered pending a blessed OKF media type.
Read-only MCP server exposing every spec page as queryable tools — search, list_topics, get_topic, get_checklist, get_categories — plus an audit_url prompt.
Agent Skill teaching a compatible agent when and how to query The Website Specification — the per-page Markdown over HTTP, llms.txt, and the MCP server. Served as SKILL.md (text/markdown, not a packaged zip) and discoverable via /.well-known/agent-skills/index.json.
Belarusian securities data: tokens, shares, bonds, companies.
MCP server for vreeman.com — search Simon Vreeman's technical-marketing tools (GA4 UTM builder, Measurement Protocol hit builder, CRO tools, experiment hypothesis builder) and Stoic philosophy library (Seneca's letters, Marcus Aurelius' Meditations, Epictetus' Discourses, Stockdale's essays, classic poems).
Just a hello world agent — a minimal A2A reference implementation demonstrating the Agent-to-Agent protocol with streaming support.
Use this skill when the user explicitly asks to map, document, or onboard into an existing codebase. Trigger for prompts like "map this codebase", "document this architecture", "onboard me to this repo", or "create codebase docs". Do not trigger for routine feature implementation, bug fixes, or narrow code edits unless the user asks for repository-level discovery.
Add educational comments to the file specified, or prompt asking for file to comment if one is not provided.
Write, debug, and optimize Adobe Illustrator automation scripts using ExtendScript (JavaScript/JSX). Use when creating or modifying scripts that manipulate documents, layers, paths, text frames, colors, symbols, artboards, or any Illustrator DOM objects. Covers the complete JavaScript object model, coordinate system, measurement units, export workflows, and scripting best practices.
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Comprehensive AI prompt engineering safety review and improvement prompt. Analyzes prompts for safety, bias, security vulnerabilities, and effectiveness while providing detailed improvement recommendations with extensive frameworks, testing methodologies, and educational content.
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All Azure MCP tools to create a seamless connection between AI agents and Azure services.
The ESRP OSS MCP server exposes tools to discover & validate trusted Microsoft OSS Packages.
A Model Context Protocol (MCP) server for NuGet.
The Power BI Modeling MCP Server brings Power BI semantic modeling capabilities to your AI agents.
A basic MCP server to operate on the Postman API.
Guide for implementing continual learning in AI coding agents — hooks, memory scoping, reflection patterns. Use when setting up learning infrastructure for agents.
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