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이 저장소의 skills
SkillsMP는 ChonSong/skill-retriever에서 466개의 skill을 수집했습니다. skill을 열어 소스와 세부 정보를 확인하세요.
ChonSong/skill-retriever수집된 skill 466개 중 40개를 표시합니다.
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…
원문 언어: 영어
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…
원문 언어: 영어
Configure and optimize .geminiignore files for AI context window efficiency and token cost reduction (FinOps).
원문 언어: 영어
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.
원문 언어: 영어
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.
원문 언어: 영어
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."
원문 언어: 영어
Lightweight Gmail integration with standalone OAuth authentication. No MCP server required.
원문 언어: 영어
Draft and explain persistent goal-loop prompts for long-running agent work with clear stop conditions.
원문 언어: 영어
Lightweight Google Calendar integration with standalone OAuth authentication. No MCP server required.
원문 언어: 영어
Lightweight Google Docs integration with standalone OAuth authentication. No MCP server required.
원문 언어: 영어
Lightweight Google Drive integration with standalone OAuth authentication. No MCP server required. Full read/write access.
원문 언어: 영어
Lightweight Google Sheets integration with standalone OAuth authentication. No MCP server required. Full read/write access.
원문 언어: 영어
Lightweight Google Slides integration with standalone OAuth authentication. No MCP server required. Full read/write access.
원문 언어: 영어
GraphQL queries, mutations, and code generation patterns. Use when creating GraphQL operations, working with Apollo Client, or generating types.
원문 언어: 영어
A relentless interview to sharpen a plan or design.
원문 언어: 영어
A relentless interview to sharpen a plan or design, which also creates docs (ADR's and glossary) as we go.
원문 언어: 영어
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.
원문 언어: 영어
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.
원문 언어: 영어
Compact the current conversation into a handoff document for another agent to pick up.
원문 언어: 영어
Command-line access to search, scraping, and structured web data.
원문 언어: 영어
Use HasData APIs for web scraping and structured web data extraction.
원문 언어: 영어
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.
원문 언어: 영어
Hugging Face CLI to estimate the required memory to load Safetensors or GGUF model weights for inference from the Hugging Face Hub
원문 언어: 영어
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…
원문 언어: 영어
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…
원문 언어: 영어
Hugging Face Dataset Viewer
원문 언어: 영어
Build Gradio web UIs and demos in Python. Use when creating or editing Gradio apps, components, event listeners, layouts, or chatbots.
원문 언어: 영어
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…
원문 언어: 영어
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.
원문 언어: 영어
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…
원문 언어: 영어
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…
원문 언어: 영어
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…
원문 언어: 영어
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]",…
원문 언어: 영어
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.
원문 언어: 영어
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,…
원문 언어: 영어
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…
원문 언어: 영어
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…
원문 언어: 영어
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 —…
원문 언어: 영어
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,…
원문 언어: 영어
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,…
원문 언어: 영어