/scholar-evaluation
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.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
- You can call itInvoke it directly when you want it.
- Slash command
/scholar-evaluation
Context 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,
SKILL.md
scholar-evaluation.SKILL.mdname: 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]
<!-- ╔══════════════════════════════════════════════════════════════╗ ║ 本文件为开源 Skill 原始文档,收录仅供学习与研究参考 ║ ║ CoPaper.AI 收集整理 | https://copaper.ai ║ ╚══════════════════════════════════════════════════════════════╝
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
声明: 本文件版权归原作者所有。此处收录旨在为社会科学实证研究者 提供 AI Agent Skills 的集中参考。如有侵权,请联系删除。 -->
Scholar Evaluation
Overview
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.
When to Use This Skill
Use this skill when:
- Evaluating research papers for quality and rigor
- Assessing literature review comprehensiveness and quality
- Reviewing research methodology design
- Scoring data analysis approaches
- Evaluating scholarly writing and presentation
- Providing structured feedback on academic work
- Benchmarking research quality against established criteria
- Assessing publication readiness for target venues
- Providing quantitative evaluation to complement qualitative peer review
Visual Enhancement with Scientific Schematics
**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:
- Use the **scientific-schematics** skill to generate AI-powered publication-quality diagrams
- Simply describe your desired diagram in natural language
- Nano Banana Pro will automatically generate, review, and refine the schematic
**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:
- Create publication-quality images with proper formatting
- Review and refine through multiple iterations
- Ensure accessibility (colorblind-friendly, high contrast)
- Save outputs in the figures/ directory
**When to add schematics:**
- Evaluation framework diagrams
- Quality assessment criteria decision trees
- Scholarly workflow visualizations
- Assessment methodology flowcharts
- Scoring rubric visualizations
- Evaluation process diagrams
- Any complex concept that benefits from visualization
For detailed guidance on creating schematics, refer to the scientific-schematics skill documentation.
---
Evaluation Workflow
Step 1: Initial Assessment and Scope Definition
Begin by identifying the type of scholarly work being evaluated and the evaluation scope:
**Work Types:**
- Full research paper (empirical, theoretical, or review)
- Research proposal or protocol
- Literature review (systematic, narrative, or scoping)
- Thesis or dissertation chapter
- Conference abstract or short paper
**Evaluation Scope:**
- Comprehensive (all dimensions)
- Targeted (specific aspects like methodology or writing)
- Comparative (benchmarking against other work)
Ask the user to clarify if the scope is ambiguous.
Step 2: Dimension-Based Evaluation
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**
- Clarity and specificity of research questions
- Theoretical or practical significance
- Feasibility and scope appropriateness
- Novelty and contribution potential
2. **Literature Review**
- Comprehensiveness of coverage
- Critical synthesis vs. mere summarization
- Identification of research gaps
- Currency and relevance of sources
- Proper contextualization
3. **Methodology & Research Design**
- Appropriateness for research questions
- Rigor and validity
- Reproducibility and transparency
- Ethical considerations
- Limitations acknowledgment
4. **Data Collection & Sources**
- Quality and appropriateness of data
- Sample size and representativeness
- Data collection procedures
- Source credibility and reliability
5. **Analysis & Interpretation**
- Appropriateness of analytical methods
- Rigor of analysis
- Logical coherence
- Alternative explanations considered
- Results-claims alignment
6. **Results & Findings**
- Clarity of presentation
- Statistical or qualitative rigor
- Visualization quality
- Interpretation accuracy
- Implications discussion
7. **Scholarly Writing & Presentation**
- Clarity and organization
- Academic tone and style
- Grammar and mechanics
- Logical flow
- Accessibility to target audience
8. **Citations & References**
- Citation completeness
- Source quality and appropriateness
- Citation accuracy
- Balance of perspectives
- Adherence to citation standards
Step 3: Scoring and Rating
For each evaluated dimension, provide:
**Qualitative Assessment:**
- Key strengths (2-3 specific points)
- Areas for improvement (2-3 specific points)
- Critical issues (if any)
**Quantitative Scoring (Optional):** Use a 5-point scale
Read more
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]
<!-- ╔══════════════════════════════════════════════════════════════╗ ║ 本文件为开源 Skill 原始文档,收录仅供学习与研究参考 ║ ║ CoPaper.AI 收集整理 | https://copaper.ai ║ ╚══════════════════════════════════════════════════════════════╝
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
声明: 本文件版权归原作者所有。此处收录旨在为社会科学实证研究者 提供 AI Agent Skills 的集中参考。如有侵权,请联系删除。 -->
Scholar Evaluation
Overview
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.
When to Use This Skill
Use this skill when:
- Evaluating research papers for quality and rigor
- Assessing literature review comprehensiveness and quality
- Reviewing research methodology design
- Scoring data analysis approaches
- Evaluating scholarly writing and presentation
- Providing structured feedback on academic work
- Benchmarking research quality against established criteria
- Assessing publication readiness for target venues
- Providing quantitative evaluation to complement qualitative peer review
Visual Enhancement with Scientific Schematics
**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:
- Use the **scientific-schematics** skill to generate AI-powered publication-quality diagrams
- Simply describe your desired diagram in natural language
- Nano Banana Pro will automatically generate, review, and refine the schematic
**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:
- Create publication-quality images with proper formatting
- Review and refine through multiple iterations
- Ensure accessibility (colorblind-friendly, high contrast)
- Save outputs in the figures/ directory
**When to add schematics:**
- Evaluation framework diagrams
- Quality assessment criteria decision trees
- Scholarly workflow visualizations
- Assessment methodology flowcharts
- Scoring rubric visualizations
- Evaluation process diagrams
- Any complex concept that benefits from visualization
For detailed guidance on creating schematics, refer to the scientific-schematics skill documentation.
---
Evaluation Workflow
Step 1: Initial Assessment and Scope Definition
Begin by identifying the type of scholarly work being evaluated and the evaluation scope:
**Work Types:**
- Full research paper (empirical, theoretical, or review)
- Research proposal or protocol
- Literature review (systematic, narrative, or scoping)
- Thesis or dissertation chapter
- Conference abstract or short paper
**Evaluation Scope:**
- Comprehensive (all dimensions)
- Targeted (specific aspects like methodology or writing)
- Comparative (benchmarking against other work)
Ask the user to clarify if the scope is ambiguous.
Step 2: Dimension-Based Evaluation
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**
- Clarity and specificity of research questions
- Theoretical or practical significance
- Feasibility and scope appropriateness
- Novelty and contribution potential
2. **Literature Review**
- Comprehensiveness of coverage
- Critical synthesis vs. mere summarization
- Identification of research gaps
- Currency and relevance of sources
- Proper contextualization
3. **Methodology & Research Design**
- Appropriateness for research questions
- Rigor and validity
- Reproducibility and transparency
- Ethical considerations
- Limitations acknowledgment
4. **Data Collection & Sources**
- Quality and appropriateness of data
- Sample size and representativeness
- Data collection procedures
- Source credibility and reliability
5. **Analysis & Interpretation**
- Appropriateness of analytical methods
- Rigor of analysis
- Logical coherence
- Alternative explanations considered
- Results-claims alignment
6. **Results & Findings**
- Clarity of presentation
- Statistical or qualitative rigor
- Visualization quality
- Interpretation accuracy
- Implications discussion
7. **Scholarly Writing & Presentation**
- Clarity and organization
- Academic tone and style
- Grammar and mechanics
- Logical flow
- Accessibility to target audience
8. **Citations & References**
- Citation completeness
- Source quality and appropriateness
- Citation accuracy
- Balance of perspectives
- Adherence to citation standards
Step 3: Scoring and Rating
For each evaluated dimension, provide:
**Qualitative Assessment:**
- Key strengths (2-3 specific points)
- Areas for improvement (2-3 specific points)
- Critical issues (if any)
**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 |
Other skills on auto-empirical-research-skills.
- /pipeline
Classical end-to-end empirical analysis workflow in the traditional Python econometric stack — pandas + numpy + scipy + statsmodels + linearmodels + pyfixest + rdrobust + econml + causalml + matplotlib/seaborn. **Defaults to economics empirical-paper style** (AER / QJE / AEJ) —
Open skill - /pipeline
Classical end-to-end empirical analysis workflow in the modern tidyverse + econometrics R ecosystem — dplyr + tidyr + haven + fixest + sandwich + lmtest + clubSandwich + AER + ivreg + did + bacondecomp + HonestDiD + eventstudyr + rdrobust + rddensity + Synth + gsynth + synthdid
Open skill - /pipeline
Classical end-to-end empirical analysis workflow in the traditional Stata ecosystem — native Stata + reghdfe + ivreg2 + csdid + did_imputation + eventstudyinteract + sdid + rdrobust + rddensity + synth + synth_runner + psmatch2 + teffects + ebalance + coefplot + esttab + asdoc +
Open skill - /00-Full-empirical-analysis-skill_StatsPAI
Use when the user asks to run a full empirical / causal analysis in Python — by default in the style of an applied economics paper (AER / QJE / JPE / ReStud / AEJ) with DID / RD / IV / SCM / DML / matching, written-out estimating equation + identifying assumption, Table 1 /
Open skill - /00.1-Full-empirical-analysis-skill_Python
Classical end-to-end empirical analysis workflow in the traditional Python econometric stack — pandas + numpy + scipy + statsmodels + linearmodels + pyfixest + rdrobust + econml + causalml + matplotlib/seaborn. **Defaults to economics empirical-paper style** (AER / QJE / AEJ) —
Open skill - /00.2-Full-empirical-analysis-skill_Stata
Classical end-to-end empirical analysis workflow in the traditional Stata ecosystem — native Stata + reghdfe + ivreg2 + csdid + did_imputation + eventstudyinteract + sdid + rdrobust + rddensity + synth + synth_runner + psmatch2 + teffects + ebalance + coefplot + esttab + asdoc +
Open skill

