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: sound-synthesis

英語版ディレクトリ

SANA: Efficient High-Resolution Image Synthesis with Linear Diffusion Transformer

8.3K
Stars
86/100
信頼
カテゴリ: media-automation監査

MOSS‑TTS Family is an open‑source speech and sound generation model family from MOSI.AI and the OpenMOSS team. It is designed for high‑fidelity, high‑expressiveness, and complex real‑world scenarios, covering stable long‑form speech, multi‑speaker dialogue, voice/character design, environmental sound effects, and real‑time streaming TTS.

3.5K
Stars
85/100
信頼
カテゴリ: media-automation監査

Offline Text To Speech synthesis for python

2.5K
Stars
80/100
信頼
カテゴリ: media-automation監査

AI video skill for Claude Code & Codex — cinematic product videos with Remotion: 106 shot recipe cards, 161 motion previews, a production-ready template

1.8K
Stars
79/100
信頼
カテゴリ: utility監査

Turn project work into reusable knowledge — an AI-agent skill for Claude Code & Codex

196
Stars
73/100
信頼
カテゴリ: utility監査

[CVPR 2025] MMAudio: Taming Multimodal Joint Training for High-Quality Video-to-Audio Synthesis

2.2K
Stars
79/100
信頼
カテゴリ: robotics-iot監査

Audio generation skill — jingles, beds, voiceover, and sound effects. Routes music requests to Suno V5 / Udio / Lyria, speech to MiniMax TTS / FishAudio / ElevenLabs V3, and SFX to ElevenLabs SFX or AudioCraft. Output is one MP3/WAV file saved to the project folder.

90K
Stars
69/100
信頼
カテゴリ: design-creative監査

[CVPR 2025 Oral]Infinity ∞ : Scaling Bitwise AutoRegressive Modeling for High-Resolution Image Synthesis

1.6K
Stars
83/100
信頼
カテゴリ: media-automation監査

Write precise Seedance 2.0 prompts for multimodal video, camera movement, editing, music, and product storytelling.

3.4K
Stars
70/100
信頼
カテゴリ: design-creative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

A Claude Code custom skill for generating structured Chinese prompts for ByteDance's Seedance 2.0 AI video generation platform.

2.2K
Stars
72/100
信頼
カテゴリ: media監査

A comprehensive ComfyUI integration for Microsoft's VibeVoice text-to-speech model, enabling high-quality single and multi-speaker voice synthesis directly within your ComfyUI workflows.

1.5K
Stars
76/100
信頼
カテゴリ: media-automation監査