學術辯論與觀點比較技能
純指令技能。LLM 結構化思考 + read_draft, search_local_references, mcp_cgu_deep_think, mcp_cgu_spark_collision。
觸發：辯論、pro/con、devil's advocate、挑戰假設、compare viewpoints
Framework 1: Academic Debate（雙方辯論）
針對研究議題結構化正反方論點。按證據等級排列：
SR/Meta > RCT > Cohort > Case-Control > Cross-Sectional > Case Series > Expert Opinion
輸出：Position A (supporting) → Position B (opposing) → Methodological Considerations (偏誤) → Synthesis (agreement / disagreement / clinical bottom line)
Framework 2: Devil's Advocate（魔鬼代言人）
系統性挑戰一個研究主張。5 面向：
- Methodological — 研究類型對應偏誤（見下方）
- Statistical — multiple comparisons, effect size, CI width, missing data, power
- Generalizability — population, setting, timeframe, intervention fidelity
- Alternative Explanations — confounding, reverse causation, temporal trends, Hawthorne
- Likely Reviewer Questions — 基於 study type
產出：Strengthening Recommendations（address counter-arguments, add sensitivity analysis, cite SR, frame conservatively）
Framework 3: Viewpoint Comparison（觀點比較）
多個理論/方法系統性比較。預設比較維度：
Evidence Base, Theoretical Foundation, Clinical Applicability, Patient Safety, Cost-Effectiveness, Current Guidelines, Limitations
Study-Type-Specific Biases
| Type |
Key Biases |
| RCT |
Selection, performance, detection, attrition, reporting |
| Cohort |
Selection, confounding, information, loss to follow-up, healthy worker |
| Case-Control |
Recall, selection (controls), confounding, misclassification, temporal ambiguity |
| Cross-Sectional |
No temporality, prevalence-incidence, non-response, information |
| Retrospective |
Information (records), survivorship, confounding, missing data |
Auto-detect：randomized/RCT=RCT, cohort/prospective=Cohort, case-control/odds=Case-Control, cross-sectional/prevalence=Cross-Sectional, retrospective/chart=Retrospective
使用原則
- 基於證據，引用已存文獻
- 平衡呈現雙方
- 偏誤分析對應正確研究類型
- 最終給臨床建議（為 Discussion 服務）
1---2name: academic-debate3description: 學術辯論與觀點比較框架。 LOAD THIS SKILL WHEN: debate、辯論、pro/con、正反方、devil's advocate、挑戰假設、counter-argument、反駁、compare viewpoints、比較觀點、challenge、質疑 CAPABILITIES: 純指令技能，不需要專屬 MCP tool。利用 LLM 推理能力 + 現有 tools (read_draft, search_local_references) 完成分析。4---56# 學術辯論與觀點比較技能78純指令技能。LLM 結構化思考 + `read_draft`, `search_local_references`, `mcp_cgu_deep_think`, `mcp_cgu_spark_collision`。910觸發：辯論、pro/con、devil's advocate、挑戰假設、compare viewpoints1112---1314## Framework 1: Academic Debate（雙方辯論）1516針對研究議題結構化正反方論點。按**證據等級**排列：17SR/Meta > RCT > Cohort > Case-Control > Cross-Sectional > Case Series > Expert Opinion1819輸出：Position A (supporting) → Position B (opposing) → Methodological Considerations (偏誤) → Synthesis (agreement / disagreement / clinical bottom line)2021---2223## Framework 2: Devil's Advocate（魔鬼代言人）2425系統性挑戰一個研究主張。5 面向：26271. **Methodological** — 研究類型對應偏誤（見下方）282. **Statistical** — multiple comparisons, effect size, CI width, missing data, power293. **Generalizability** — population, setting, timeframe, intervention fidelity304. **Alternative Explanations** — confounding, reverse causation, temporal trends, Hawthorne315. **Likely Reviewer Questions** — 基於 study type3233產出：Strengthening Recommendations（address counter-arguments, add sensitivity analysis, cite SR, frame conservatively）3435---3637## Framework 3: Viewpoint Comparison（觀點比較）3839多個理論/方法系統性比較。預設比較維度：40Evidence Base, Theoretical Foundation, Clinical Applicability, Patient Safety, Cost-Effectiveness, Current Guidelines, Limitations4142---4344## Study-Type-Specific Biases4546| Type | Key Biases |47| --------------- | -------------------------------------------------------------------------------- |48| RCT | Selection, performance, detection, attrition, reporting |49| Cohort | Selection, confounding, information, loss to follow-up, healthy worker |50| Case-Control | Recall, selection (controls), confounding, misclassification, temporal ambiguity |51| Cross-Sectional | No temporality, prevalence-incidence, non-response, information |52| Retrospective | Information (records), survivorship, confounding, missing data |5354Auto-detect：randomized/RCT=RCT, cohort/prospective=Cohort, case-control/odds=Case-Control, cross-sectional/prevalence=Cross-Sectional, retrospective/chart=Retrospective5556---5758## 使用原則59601. 基於證據，引用已存文獻612. 平衡呈現雙方623. 偏誤分析對應正確研究類型634. 最終給臨床建議（為 Discussion 服務）
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