Renamed to /model-assessment in v6 (removed in v7).
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
/model-evaluation was renamed to /model-assessment in v6.0; this redirect is removed in v7.
Run /model-assessment now, passing the same arguments the user gave /model-evaluation.
Start at /model-assessment Part B, Phases 8–9 (task-correct held-out metrics and the metric-reporting gate).
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Use when a medical AI paper should be found and cited by AI search engines and RAG tools. Optimizes the title, abstract, summary box (Key Points, Research in Context), keywords, preprint, GitHub README/CITATION.cff and Hugging Face card, returning a visible pass/fail checklist.
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Use when a target journal has no profile yet. Reads the journal's author guidelines and writes a detailed /write-paper profile plus a compact /find-journal profile, public or user-local private, in the canonical format with quality gates.
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Use when data needs statistical analysis. Runs reproducible Python/R code for Table 1, diagnostic accuracy, agreement, regression, survival, propensity score, survey-weighted and repeated-measures models, with publication tables. Sample size is /calc-sample-size; pooling studies is /meta-analysis.
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Renamed to /model-selection in v6 (removed in v7).
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Use when analyzing a researcher's publication record from PubMed. Fetches an author's papers, classifies study types and author position, charts the patterns and writes a strategy report, with an optional trajectory-archetype classification. Works from PubMed metadata only.
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Use when one validated cohort analysis must be repeated across many exposure/outcome pairs. Generates one R/Python script per combination from a single methodology template, changing only the variables, and aggregates the results into a summary matrix.
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