完整的视频创作套件,支持原创创作、视频二创、视频分析三种模式,集成Coze Bot API、Edge-TTS、Suno API,涵盖多智能体协同、素材生成、视频合成全流程
npx skills add https://github.com/anbeime/skill --skill video-creation-suite
opencv-python>=4.8.0
pillow>=10.0.0
moviepy>=1.0.3
numpy>=1.24.0
requests>=2.28.0
edge-tts>=6.1.0
COZE_BOT_ID, COZE_API_KEYpip install edge-ttsSUNO_API_KEY智能体根据用户输入自动选择模式:
scripts/voice_generator.py 生成角色配音--input <旁白数据JSON> --output ./output/audio/voicescripts/sound_generator.py --type sound 生成音效--input <音效配置JSON> --output ./output/audio/sound_effectsscripts/sound_generator.py --type music 生成背景音乐--input <音乐配置JSON> --output ./output/audio/background_musicscripts/video_compositor.py 合成最终视频--images <图片目录> --audio <音频目录> --subtitles <字幕文件> --output <输出路径>scripts/quality_checker.py 检测技术指标10. 质检
11. 数据反馈
scripts/video_frame_extractor.py 提取关键帧--input <原视频路径> --output ./output/frames --interval 2./output/frames/scripts/coze_bot_client.py 分析关键帧./output/analysis.jsonscripts/image_generator.py 生成图片./output/images/scripts/voice_generator.py 合成配音(基于Edge-TTS)--input <旁白脚本JSON> --output ./output/voice./output/voice/scripts/sound_generator.py --type both 生成音效和背景音乐--input <完整配置JSON> --output ./output/audio./output/audio/sound_effects/, 背景音乐到 ./output/audio/background_music/scripts/subtitle_generator.py 生成字幕文件--input <字幕数据JSON> --output ./output/subtitles./output/subtitles/scripts/video_compositor.py 合成视频--images ./output/images --audio ./output/audio --voice ./output/voice --subtitles ./output/subtitles --output ./output/final.mp4./output/final.mp4scripts/file_server.py 启动HTTP服务器--port 8080 --directory ./outputhttp://localhost:8080/final.mp4scripts/video_frame_extractor.py 提取关键帧--input <视频路径> --output <输出目录> --interval <间隔秒数>scripts/coze_bot_client.py 分析每帧内容--message "<分析提示>" --image_path <图片路径>用户: "帮我创作一个《三体》威慑纪元的8分钟视频"
智能体执行:
1. 自动选择模式一: 《三体》IP创作
2. 按照前期筹备 → 视觉制作 → 音频与剪辑 → 质量管控流程
3. 调用相应脚本生成配音、音效、背景音乐
4. 合成最终视频
用户: "这个视频帮我二创一下,换个风格"
智能体执行:
1. 自动选择模式二: 视频二创
2. 提取原视频关键帧
3. 视觉分析,提取风格特征
4. 生成新素材(图片、配音、音效、音乐)
5. 合成新视频
用户: "分析这个视频,提取分镜和提示词"
智能体执行:
1. 自动选择模式三: 视频分析
2. 抽取视频关键帧
3. 分析每帧内容
4. 输出结构化分析结果
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Microscopy data management platform. Access images via Python, retrieve datasets, analyze pixels, manage ROIs/annotations, batch processing, for high-content screening and microscopy workflows.
Python library for working with DICOM (Digital Imaging and Communications in Medicine) files. Use this skill when reading, writing, or modifying medical imaging data in DICOM format, extracting pixel data from medical images (CT, MRI, X-ray, ultrasound), anonymizing DICOM files, working with DICOM metadata and tags, converting DICOM images to other formats, handling compressed DICOM data, or processing medical imaging datasets. Applies to tasks involving medical image analysis, PACS systems, radiology workflows, and healthcare imaging applications.
This skill should be used when working with pre-trained transformer models for natural language processing, computer vision, audio, or multimodal tasks. Use for text generation, classification, question answering, translation, summarization, image classification, object detection, speech recognition, and fine-tuning models on custom datasets.
Take anbeime/video-creation-suite from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.
The instructions reference pip.
Without those the skill loads but fails at the first command.