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: experimental

英語版ディレクトリ

Agent skills for VueUse to help AI agents use Vue Composition utilities efficiently with minimal token usage.

377
Stars
73/100
信頼
カテゴリ: coding-agents監査

A curated collection of field-tested, reusable skills for Hermes Agent, covering operational workflows like inspect, diagnose, recover, migrate, and verify.

123
Stars
77/100
信頼
カテゴリ: utility監査

Data structure for annotated matrices in single-cell analysis. Use when working with .h5ad files or integrating with the scverse ecosystem. This is the data format skill—for analysis workflows use scanpy; for probabilistic models use scvi-tools; for population-scale queries use cellxgene-census.

34K
Stars
77/100
信頼
カテゴリ: data-analysis監査

How to use the Adaptyv Bio Foundry API and Python SDK for protein experiment design, submission, and results retrieval. Use this skill whenever the user mentions Adaptyv, Foundry API, protein binding assays, protein screening experiments, BLI/SPR assays, thermostability assays, or wants to submit protein sequences for experimental characterization. Also trigger when code imports `adaptyv`, `adaptyv_sdk`, or `FoundryClient`, or references `foundry-api-public.adaptyvbio.com`.

34K
Stars
78/100
信頼
カテゴリ: design-creative監査

This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.

34K
Stars
80/100
信頼
カテゴリ: research監査

Observe the user's screen via screenpipe, detect repeated research workflows, match them against existing scientific-agent-skills, and draft new skills (or composition recipes that chain existing ones) for the patterns not yet covered. Use when the user asks to analyze their recent work and propose skills based on what they actually do. Requires the screenpipe daemon (https://github.com/screenpipe/screenpipe) running locally on port 3030 — the skill has no other data source and will refuse to run if screenpipe is unreachable. All detection runs locally; only redacted cluster summaries reach the LLM.

34K
Stars
70/100
信頼
カテゴリ: research監査

🚀🎬 ShortGPT - Experimental AI framework for youtube shorts / tiktok channel automation

7.4K
Stars
75/100
信頼
カテゴリ: automation監査

Contrib package for Stable-Baselines3 - Experimental reinforcement learning (RL) code

723
Stars
73/100
信頼
カテゴリ: robotics-iot監査

🔖 FavBox is a local-first experimental browser extension that enhances and simplifies bookmark management without cloud storage or third-party services.

698
Stars
71/100
信頼
カテゴリ: productivity-automation監査

Experimental solutions to selected exercises from the book [Advances in Financial Machine Learning by Marcos Lopez De Prado]

1.9K
Stars
72/100
信頼
カテゴリ: finance監査

Manifold is an experimental platform for enabling long horizon workflow automation using teams of AI assistants.

497
Stars
70/100
信頼
カテゴリ: agent-frameworks監査

An experimental embedded SQL engine in C++20. Query Parquet, CSV, JSON, Arrow, Avro, SQLite, and Excel files directly with SQL, in-process. Early-stage.

448
Stars
67/100
信頼
カテゴリ: data-analysis監査