name: eduai-case-analyzer description: Analyze education industry cases and turn them into business insights, short-video scripts, posts, and reusable growth lessons. Use when users mention 教育案例拆解、教培案例、案例分析、商业逻辑拆解、短视频案例文稿、机构案例、AI转型案例、OPC案例. metadata: slug: eduai-case-analyzer displayName: 教育案例拆解器 version: 1.1.0 author: 教培校长AI洞察 category: education tags: - 教培 - 案例拆解 - 短视频 - 商业分析 - OPC

教育案例拆解器

Turn education cases into clear business logic, content angles, and short-video scripts.

References

• references/case-framework.md — case analysis structure.
• references/business-lens.md — growth, product, delivery, channel, AI, and OPC lenses.
• references/video-output.md — short-video script patterns.
• references/output-template.md — recommended output structure.
• references/boundaries.md — privacy and assumption rules.
• references/source-and-causality.md — source quality, timeline, causal claims, counterfactuals, and transfer limits.

Workflow

1. Extract the case background, actors, pain point, action, mechanism, result, and lesson.
2. Choose the most useful lens: growth, product, delivery, AI transformation, private domain, or OPC.
3. Separate confirmed facts, source claims, inference, and unknowns. Reconstruct the timeline before explaining causality.
4. Test alternative explanations and identify the cost, enabling conditions, and transfer limits.
5. Convert the case into one sharp insight and one reusable method.
6. If requested, generate a short-video script, post, or presentation outline without upgrading uncertain claims into facts.

Output rules

• Use Chinese by default.

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• Prefer plain language that education bosses can understand.
• Mark assumptions when information is incomplete.
• Avoid exposing sensitive student, parent, teacher, or institution data.
• Preserve source names or links when supplied and distinguish primary from secondary sources.
• Do not claim that one action caused a result when timing, baseline, or competing factors are unknown.
• Do not fabricate revenue, enrollment, conversion, quotes, or before-after results.

质量中上，框架完整、逻辑严谨。优点是分析流程清晰、质量控制到位、输出规则明确，能有效避免夸大结论和误导信息，对教培从业者友好。不足是缺乏实际案例演示，仅有框架说明，新手难以快速上手实践；另外边界规则和深度指导略显单薄。适合有一定基础的教培从业者使用，初学者可能需要额外参考其他材料。