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SkillsMP は ChonSong/skill-retriever から 466 件の skill を収集しています。skill を開くとソースと詳細を確認できます。
ChonSong/skill-retriever収集済み skill 466 件中 40 件を表示しています。
Use this skill when building real-time, bidirectional streaming applications with the Gemini Live API. Covers WebSocket-based audio/video/text streaming, voice activity detection (VAD), native audio features, function calling, session management, ephemeral…
原文の言語: 英語
Use this skill for generative video editing, text-to-video, image-referenced video generation, and first-frame-to-video transition animations using the official google-genai SDK. Includes workflows for pre-processing/optimizing high-resolution or long source…
原文の言語: 英語
Configure and optimize .geminiignore files for AI context window efficiency and token cost reduction (FinOps).
原文の言語: 英語
Upload local images to GitHub and get canonical user-attachments embed URLs; use when asked to attach a screenshot to a PR, issue, or comment, or to embed before/after images in a README.
原文の言語: 英語
Structures git workflow practices. Use when making any code change. Use when committing, branching, resolving conflicts, or when you need to organize work across multiple parallel streams.
原文の言語: 英語
When the user wants to optimize their GitHub profile, README, or project discoverability. Trigger phrases include "GitHub README," "README optimization," "GitHub profile," "GitHub stars," "GitHub discoverability," "awesome lists," or "GitHub marketing."
原文の言語: 英語
Lightweight Gmail integration with standalone OAuth authentication. No MCP server required.
原文の言語: 英語
Draft and explain persistent goal-loop prompts for long-running agent work with clear stop conditions.
原文の言語: 英語
Lightweight Google Calendar integration with standalone OAuth authentication. No MCP server required.
原文の言語: 英語
Lightweight Google Docs integration with standalone OAuth authentication. No MCP server required.
原文の言語: 英語
Lightweight Google Drive integration with standalone OAuth authentication. No MCP server required. Full read/write access.
原文の言語: 英語
Lightweight Google Sheets integration with standalone OAuth authentication. No MCP server required. Full read/write access.
原文の言語: 英語
Lightweight Google Slides integration with standalone OAuth authentication. No MCP server required. Full read/write access.
原文の言語: 英語
GraphQL queries, mutations, and code generation patterns. Use when creating GraphQL operations, working with Apollo Client, or generating types.
原文の言語: 英語
A relentless interview to sharpen a plan or design.
原文の言語: 英語
A relentless interview to sharpen a plan or design, which also creates docs (ADR's and glossary) as we go.
原文の言語: 英語
Interview the user relentlessly about a plan or design. Use when the user wants to stress-test a plan before building, or uses any 'grill' trigger phrases.
原文の言語: 英語
Delegate well-specified implementation tasks to xAI's Grok Build CLI running headlessly while the orchestrating agent plans, writes task specs, reviews every diff, and owns the result.
原文の言語: 英語
Compact the current conversation into a handoff document for another agent to pick up.
原文の言語: 英語
Command-line access to search, scraping, and structured web data.
原文の言語: 英語
Use HasData APIs for web scraping and structured web data extraction.
原文の言語: 英語
Use Hugging Face Hub via MCP server tools. Search models, datasets, Spaces, papers. Get repo details, fetch documentation, run compute jobs, and use Gradio Spaces as AI tools. Available when connected to the HF MCP server.
原文の言語: 英語
Hugging Face CLI to estimate the required memory to load Safetensors or GGUF model weights for inference from the Hugging Face Hub
原文の言語: 英語
Hugging Face Hub CLI (`hf`) for downloading, uploading, and managing models, datasets, spaces, buckets, repos, papers, jobs, and more on the Hugging Face Hub. Use when: handling authentication; managing local cache; managing Hugging Face Buckets; running or…
原文の言語: 英語
Run evaluations for Hugging Face Hub models using inspect-ai and lighteval on local hardware. Use for backend selection, local GPU evals, and choosing between vLLM / Transformers / accelerate. Not for HF Jobs orchestration, model-card PRs, .eval_results…
原文の言語: 英語
Hugging Face Dataset Viewer
原文の言語: 英語
Build Gradio web UIs and demos in Python. Use when creating or editing Gradio apps, components, event listeners, layouts, or chatbots.
原文の言語: 英語
Train or fine-tune language and vision models using TRL (Transformer Reinforcement Learning) or Unsloth with Hugging Face Jobs infrastructure. Covers SFT, DPO, GRPO and reward modeling training methods, plus GGUF conversion for local deployment. Includes…
原文の言語: 英語
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.
原文の言語: 英語
Look up and read Hugging Face paper pages in markdown, and use the papers API for structured metadata such as authors, linked models/datasets/spaces, Github repo and project page. Use when the user shares a Hugging Face paper page URL, an arXiv URL or ID, or…
原文の言語: 英語
Track and visualize ML training experiments with Trackio. Use when logging metrics during training (Python API), firing alerts for training diagnostics, or retrieving/analyzing logged metrics (CLI). Supports real-time dashboard visualization, alerts with…
原文の言語: 英語
Trains and fine-tunes vision models for object detection (D-FINE, RT-DETR v2, DETR, YOLOS), image classification (timm models — MobileNetV3, MobileViT, ResNet, ViT/DINOv3 — plus any Transformers classifier), and SAM/SAM2 segmentation using Hugging Face…
原文の言語: 英語
Use when the user asks about finding the best, top, or recommended model for a task, wants to know what AI model to use, or wants to compare models by benchmark scores. Triggers on: "best model for X", "what model should I use for", "top models for [task]",…
原文の言語: 英語
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.
原文の言語: 英語
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,…
原文の言語: 英語
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…
原文の言語: 英語
Use this skill when the user wants to build tool/scripts or achieve a task where using data from the Hugging Face API would help. This is especially useful when chaining or combining API calls or the task will be repeated/automated. This Skill creates a…
原文の言語: 英語
AI demos and GPU compute with Gradio Spaces and Hugging Face Spaces ZeroGPU. Use when writing or reviewing code that uses `@spaces.GPU`, configuring `python_version` or `requirements.txt` for a ZeroGPU Space, or handling ZeroGPU-specific code constraints —…
原文の言語: 英語
Convert Hugo documentation sites and Hugo-managed content into standard Markdown. Use when Agent needs to inspect a local Hugo repository, read hugo.toml or config files, content/, archetypes/, layouts/_shortcodes/, layouts/_markup/, and related docs content,…
原文の言語: 英語
Operates HyperExecute end-to-end for TestMu AI/LambdaTest cloud test execution: analyze projects, create YAML, validate locally, run CLI jobs, debug failures, and wire CI. Use when the user mentions HyperExecute, hyperexecute.yaml, HyperExecute CLI,…
原文の言語: 英語