pipeline
Classical end-to-end empirical analysis workflow in the traditional Python econometric stack — pandas + numpy + scipy + statsmodels + linearmodels + pyfixest +…
Apply the ScholarEval framework to systematically evaluate scholarly work across quality dimensions (novelty, rigor, significance, and clarity). Use to critique academic papers, research proposals, literature reviews, or grant applications and produce a structured,
$ npx -y skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill scholar-evaluation --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/scholar-evaluationContext preview
The summary Claude sees to decide when to auto-load this skill.
Apply the ScholarEval framework to systematically evaluate scholarly work across quality dimensions (novelty, rigor, significance, and clarity). Use to critique academic papers, research proposals, literature reviews, or grant applications and produce a structured,
name: scholar-evaluation description: "Apply the ScholarEval framework to systematically evaluate scholarly work across quality dimensions (novelty, rigor, significance, and clarity). Use to critique academic papers, research proposals, literature reviews, or grant applications and produce a structured, criterion-based assessment." allowed-tools: [Read, Write, Edit, Bash]
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来源仓库: https://github.com/K-Dense-AI/claude-scientific-writer 项目名称: claude-scientific-writer 开源协议: MIT License 收录日期: 2026-04-02
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Apply the ScholarEval framework to systematically evaluate scholarly and research work. This skill provides structured evaluation methodology based on peer-reviewed research assessment criteria, enabling comprehensive analysis of academic papers, research proposals, literature reviews, and scholarly writing across multiple quality dimensions.
Use this skill when:
**When creating documents with this skill, always consider adding scientific diagrams and schematics to enhance visual communication.**
If your document does not already contain schematics or diagrams:
**For new documents:** Scientific schematics should be generated by default to visually represent key concepts, workflows, architectures, or relationships described in the text.
**How to generate schematics:**
python scripts/generate_schematic.py "your diagram description" -o figures/output.png
The AI will automatically:
**When to add schematics:**
For detailed guidance on creating schematics, refer to the scientific-schematics skill documentation.
---
Begin by identifying the type of scholarly work being evaluated and the evaluation scope:
**Work Types:**
**Evaluation Scope:**
Ask the user to clarify if the scope is ambiguous.
Systematically evaluate the work across the ScholarEval dimensions. For each applicable dimension, assess quality, identify strengths and weaknesses, and provide scores where appropriate.
Refer to `references/evaluation_framework.md` for detailed criteria and rubrics for each dimension.
**Core Evaluation Dimensions:**
1. **Problem Formulation & Research Questions**
2. **Literature Review**
3. **Methodology & Research Design**
4. **Data Collection & Sources**
5. **Analysis & Interpretation**
6. **Results & Findings**
7. **Scholarly Writing & Presentation**
8. **Citations & References**
For each evaluated dimension, provide:
**Qualitative Assessment:**
**Quantitative Scoring (Optional):** Use a 5-point scale
📌 文档结构(2026-07-22 起): 本文件是中文默认入口 —— banner + badges + 信任面 + 9 阶段流水线速览 + 76 行合集总表。 每个合集的完整描述、按用途分组、精确数字、验证方法在 docs/CONTENT_ZH.md(扩展正文,总表行内的 → 直接跳转到对应锚点)。 English version: README-en.md · 中文扩展正文:docs/CONTENT_ZH.md · README-zh-CN.md 已弃用(重定向占位) 🌐 语言: English |
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