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: latent-dirichlet-allocation

英語版ディレクトリ

Internet-scale Neural Networks

1.5K
Stars
77/100
信頼
カテゴリ: web3-analytics監査
Sep83

World's Fastest .NET CSV Parser. Modern, minimal, fast, zero allocation, reading and writing of separated values (`csv`, `tsv` etc.). Cross-platform, trimmable and AOT/NativeAOT compatible with blazing fast SIMD vectorized parsing.

1.5K
Stars
83/100
信頼
カテゴリ: data-analysis監査

Create 🔥 videos with Stable Diffusion by exploring the latent space and morphing between text prompts

4.7K
Stars
79/100
信頼
カテゴリ: ml-automation監査

When the user needs to generate, iterate, or scale ad creative for paid advertising. Use when they say 'write ad copy,' 'generate headlines,' 'create ad variations,' 'bulk creative,' 'iterate on ads,' 'ad copy validation,' 'RSA headlines,' 'Meta ad copy,' 'LinkedIn ad,' or 'creative testing.' This is pure creative production — distinct from paid-ads (campaign strategy). Use ad-creative when you need the copy, not the campaign plan.

25K
Stars
77/100
信頼
カテゴリ: design-creative監査

When the user wants to plan, design, or implement an A/B test or experiment. Also use when the user mentions "A/B test," "split test," "experiment," "test this change," "variant copy," "multivariate test," "hypothesis," "conversion experiment," "statistical significance," or "test this." For tracking implementation, see analytics-tracking.

25K
Stars
77/100
信頼
カテゴリ: design-creative監査

Official code of Motus: A Unified Latent Action World Model

1.1K
Stars
74/100
信頼
カテゴリ: media-automation監査

[CVPR2020] Adversarial Latent Autoencoders

3.5K
Stars
67/100
信頼
カテゴリ: robotics-iot監査

Kandinsky 2 — multilingual text2image latent diffusion model

2.8K
Stars
70/100
信頼
カテゴリ: media-automation監査

Zero Allocation Writer/Reader Parser for .NET Core

322
Stars
67/100
信頼
カテゴリ: data-analysis監査

Golang data structures — slices (internals, capacity growth, preallocation, slices package), maps (internals, hash buckets, maps package), arrays, container/list/heap/ring, strings.Builder vs bytes.Buffer, generic collections, pointers (unsafe.Pointer, weak.Pointer), and copy semantics. Use when choosing or optimizing Go data structures, implementing generic containers, using container/ packages, unsafe or weak pointers, or questioning slice/map internals.

3.0K
Stars
73/100
信頼
カテゴリ: design-creative監査

Golang benchmarking, profiling, and performance measurement. Use when writing, running, or comparing Go benchmarks, profiling hot paths with pprof, interpreting CPU/memory/trace profiles, analyzing results with benchstat, setting up CI benchmark regression detection, or investigating production performance with Prometheus runtime metrics. Also use when the developer needs deep analysis on a specific performance indicator - this skill provides the measurement methodology, while `samber/cc-skills-golang@golang-performance` provides the optimization patterns.

3.0K
Stars
67/100
信頼
カテゴリ: research監査

AeroJAX: A differentiable, structure-preserving framework for real-time flow simulation, control, and inverse design. Architected for neural operator integration and latent-space acceleration. Built with JAX.

131
Stars
67/100
信頼
カテゴリ: geo-science監査