Skill ディレクトリ

AI Agent のための再利用可能な Skill を見つける。

タスクで実際の GitHub Skill を検索し、利用前に Stars、Trust、監査、カテゴリ、インストール経路を確認できます。

すべての推奨は、リポジトリ、監査、インストール経路に明確につながっています。

検索結果: embedding-propagation

英語版ディレクトリ

Convert documents to structured data effortlessly. Unstructured is open-source ETL solution for transforming complex documents into clean, structured formats for language models. Visit our website to learn more about our enterprise grade Platform product for production grade workflows, partitioning, enrichments, chunking and embedding.

15K
Stars
86/100
信頼
カテゴリ: document-processing監査

AI powered open source recommender system engine supports classical/LLM rankers and multimodal content via embedding

9.7K
Stars
86/100
信頼
カテゴリ: ml-automation監査
Yn77

A highly extensible Markdown editor. Version control, AI Copilot, mind map, documents encryption, code snippet running, integrated terminal, chart embedding, HTML applets, Reveal.js, plug-in, and macro replacement.

6.6K
Stars
77/100
信頼
カテゴリ: document-processing監査

Find related notes and excerpts while writing. Your link building copilot displays relevant content in graph + list view. A local embedding model powers semantic search. Zero setup. No API key.

5.2K
Stars
72/100
信頼
カテゴリ: rag-knowledge監査

Semantic search over videos using Gemini Embedding 2 or Qwen3-VL.

4.3K
Stars
80/100
信頼
カテゴリ: rag-knowledge監査

MTEB: Massive Text Embedding Benchmark

3.3K
Stars
80/100
信頼
カテゴリ: rag-knowledge監査

Fast, Accurate, Lightweight Python library to make State of the Art Embedding

3.1K
Stars
77/100
信頼
カテゴリ: data監査

Fine-tune LLMs on your Mac with Apple Silicon. SFT, DPO, GRPO, Vision, TTS, STT, Embedding, and OCR fine-tuning — natively on MLX. Unsloth-compatible API.

1.3K
Stars
80/100
信頼
カテゴリ: media-automation監査

Unify Efficient Fine-tuning of RAG Retrieval, including Embedding, ColBERT, ReRanker.

1.1K
Stars
83/100
信頼
カテゴリ: rag-knowledge監査

Agent Skill for building evidence-backed Markdown knowledge bases with zero-cost setup, image-aware capture, automatic wiki maintenance, and an interactive knowledge graph—without requiring a RAG stack or Obsidian.

144
Stars
76/100
信頼
カテゴリ: utility監査

100+ Chinese Word Vectors 上百种预训练中文词向量

12K
Stars
72/100
信頼
カテゴリ: rag-knowledge監査

Autonomously improve a real artifact (code, training recipe, agent harness, data pipeline, prompt) against an objective and an evaluator, using Hypothesis Tree Refinement (HTR) from the Arbor paper. Use this whenever someone wants to iteratively optimize something over many experiments without overfitting — e.g. "get my model's eval score up", "improve this agent/harness", "tune this pipeline", "beat the baseline on this benchmark", "run a search over approaches and keep the best", "do an MLE-bench / Kaggle-style optimization", or any long-horizon "make this artifact better and don't just memorize the dev set" task. Trigger it even when the user doesn't say "Arbor" or "hypothesis tree" but describes repeated experiment-and-evaluate loops, branching exploration of competing ideas, or worries about a dev/test gap. Runs Claude itself as the coordinator with subagent executors in isolated git worktrees; for the standalone `arbor` CLI tool see references/arbor-upstream.md.

34K
Stars
77/100
信頼
カテゴリ: research監査