Create short animated explainers with Manim for technical concepts, graphs, system diagrams, and product walkthroughs. Plan scenes, render a video, and prepare a thumbnail, with optional handoff for further editing or compositing.
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Build reusable Manim explainers for technical concepts, graphs, system diagrams, and product walkthroughs, then hand off to the wider ECC video stack if needed. Use when the user wants a clean animated explainer rather than a generic talking-head script.
安装前可以查看交给 AI 代理的指令内容。
Use Manim for technical explainers where motion, structure, and clarity matter more than photorealism.
manim CLI for scene renderingffmpeg for post-processing if neededvideo-editing for final assembly or polishremotion-video-creation when the final package needs composited UI, captions, or additional motion layersFor social-graph and network-optimization explainers:
Use assets/network_graph_scene.py as a starting point for network-graph explainers.
Example smoke test:
manim -ql assets/network_graph_scene.py NetworkGraphExplainer
Return:
video-editing for final polishremotion-video-creation for motion-heavy post-processing or compositingcontent-engine when the animation is part of a broader launch还没有评价。欢迎分享你的使用感受。
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Design and audit interfaces for Web, iOS, and Android using WCAG 2.2 accessibility guidance. Check screen-reader labels, keyboard navigation, contrast, and control sizes to identify barriers and guide implementation for people using assistive technology.
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Investigate unreliable AI agents across 12 layers, including instructions, memory, tool execution, and display. Use code and logs to trace failures, rank findings by severity, and produce an ordered repair plan that prioritizes code changes.
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Compare coding agents on tasks from your own codebase. Define success checks, run agents in separate Git working directories, and report pass rates, available API costs, completion time, and consistency across repeated trials.
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Design and refine an AI agent’s available actions, tool inputs, and result formats. Define recovery steps for errors, manage the information kept in context, and evaluate changes through completion rates, retries, and cost per successful task.
中文简介正在准备中。当前显示英语简介。
Diagnose failed AI agent runs by capturing errors, checking likely causes, and choosing small, reversible recovery actions. The workflow produces a structured report of the failure, recovery steps, and evidence, helping explain repeated retries or stalled progress.
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Diagnose AI agent runs that fail repeatedly, loop through tools, or lose track of the task. Capture errors and environment details, test a focused recovery action, and produce a report explaining the evidence and outcome.
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