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、商業邏輯拆解、短影片案例文稿、機構案例、AI轉型案例、OPC案例. metadata: slug: eduai-case-analyzer displayName: 教育案例拆解器 version: 1.1.0 author: 教培校長AI洞察 category: education tags: - 教培 - 案例拆解 - 短影片 - 商業分析 - 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.

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• 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.
• 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.

質量中上，框架完整、邏輯嚴謹。優點是分析流程清晰、質量控制到位、輸出規則明確，能有效避免誇大結論和誤導資訊，對教培從業者友好。不足是缺乏實際案例演示，僅有框架說明，新手難以快速上手實踐；另外邊界規則和深度指導略顯單薄。適合有一定基礎的教培從業者使用，初學者可能需要額外參考其他材料。