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构建可复用的Manim解释器，用于技术概念、图表、系统图和产品演示，并在需要时移交给更广泛的ECC视频栈。当用户希望获得清晰的动画解释而非通用的人物讲解脚本时使用。
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Based on SOC occupation classification
Instinct-based learning system that observes sessions via hooks, creates atomic instincts with confidence scoring, and evolves them into skills/commands/agents. v2.1 adds project-scoped instincts to prevent cross-project contamination.
Suggests manual context compaction at logical intervals to preserve context through task phases rather than arbitrary auto-compaction.
Suggests manual context compaction at logical intervals to preserve context through task phases rather than arbitrary auto-compaction.
Best practices for Remotion - Video creation in React. 29 domain-specific rules covering 3D, animations, audio, captions, charts, transitions, and more.
Verification loop for Django projects: migrations, linting, tests with coverage, security scans, and deployment readiness checks before release or PR.
Project-specific skill template covering architecture, patterns, testing, and deployment guidance.
| name | manim-video |
| description | 构建可复用的Manim解释器，用于技术概念、图表、系统图和产品演示，并在需要时移交给更广泛的ECC视频栈。当用户希望获得清晰的动画解释而非通用的人物讲解脚本时使用。 |
| origin | ECC |
在运动、结构和清晰度比逼真度更重要的技术讲解中，使用 Manim。
manim 命令行用于场景渲染ffmpeg 用于后期处理（如需）video-editing 用于最终合成或润色remotion-video-creation 当最终成品需要合成 UI、字幕或额外运动层时对于社交图谱和网络优化讲解：
使用 assets/network_graph_scene.py 作为网络图讲解的起点。
烟雾测试示例：
manim -ql assets/network_graph_scene.py NetworkGraphExplainer
返回：
video-editing 用于最终润色remotion-video-creation 用于运动密集型后期处理或合成content-engine 当动画是更广泛发布的一部分时