Direktori skill

Temukan skill yang dapat digunakan kembali untuk AI agents.

Cari skill GitHub nyata berdasarkan tugas lalu periksa stars, trust, audit, kategori, dan jalur pemasangan sebelum digunakan.

Setiap rekomendasi tetap terhubung dengan repositori, audit, dan jalur pemasangannya.

Hasil pencarian: back-in-stock

Direktori bahasa Inggris

Guidance for distinctive, intentional UI design, typography, visual direction, and non-template-like product interfaces.

171K
Stars
82/100
Kepercayaan
Kategori: design-creativeAudit

All Algorithms implemented in Python

222K
Stars
82/100
Kepercayaan
Kategori: educationAudit

A tool that converts codebases, SQL schemas, and other files into queryable knowledge graphs for AI coding assistants.

92K
Stars
80/100
Kepercayaan
Kategori: developmentAudit

Open-source LLM-friendly web crawler and scraper

73K
Stars
82/100
Kepercayaan
Kategori: web-automationAudit

Review a branch or diff against repository standards and the originating spec in two independent analysis passes.

169K
Stars
86/100
Kepercayaan
Kategori: coding-agentsAudit

Break a plan, spec, or conversation into independently actionable tracer-bullet tickets with explicit blocking relationships.

177K
Stars
81/100
Kepercayaan
Kategori: coding-agentsAudit

Reverse-lookup glossary that turns a vague description of a web animation or motion effect into its exact term ("the bouncy thing when a popover opens" → Pop in; "the iOS rubber-band scroll" → Rubber-banding). Use when the user asks "what's it called when…", or describes a motion effect without knowing its name and wants the right word to prompt an AI or designer with. For naming an effect, not designing or building one.

17K
Stars
87/100
Kepercayaan
Kategori: design-creativeAudit

Tensors and Dynamic neural networks in Python with strong GPU acceleration

101K
Stars
76/100
Kepercayaan
Kategori: ml-automationAudit

🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.

162K
Stars
87/100
Kepercayaan
Kategori: ml-automationAudit

Implement work from an approved spec or ticket set, run focused and full tests, invoke code review, and commit the result to the current branch.

176K
Stars
81/100
Kepercayaan
Kategori: coding-agentsAudit

Implement a ChatGPT-like LLM in PyTorch from scratch, step by step

97K
Stars
77/100
Kepercayaan
Kategori: ml-automationAudit

React and Next.js performance guidance for writing, reviewing, and refactoring production UI code.

30K
Stars
88/100
Kepercayaan
Kategori: coding-agentsAudit