brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent,…
Systematically evaluate scholarly work using the ScholarEval framework, providing structured assessment across research quality dimensions including problem formulation, methodology, analysis, and writing with quantitative scoring and actionable feedback.
$ npx -y skills add xintaofei/codeg --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.
Systematically evaluate scholarly work using the ScholarEval framework, providing structured assessment across research quality dimensions including problem formulation, methodology, analysis, and writing with quantitative scoring and actionable feedback.
name: scholar-evaluation
description: Systematically evaluate scholarly work using the ScholarEval framework, providing structured assessment across research quality dimensions including problem formulation, methodology, analysis, and writing with quantitative scoring and actionable feedback.
license: MIT license
required_environment_variables: [{"name": "OPENROUTER_API_KEY", "prompt": "OpenRouter API key for the skill's LLM-powered steps.", "required_for": "optional features"}]
metadata: {"version": "1.1", "skill-author": "K-Dense Inc.", "openclaw": {"primaryEnv": "OPENROUTER_API_KEY", "envVars": [{"name": "OPENROUTER_API_KEY", "required": false, "description": "OpenRouter API key for the skill's LLM-powered steps."}]}}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 where applicable:
English | 简体中文 | 繁體中文 | 日本語 | 한국어 | Español | Deutsch | Français | Português | العربية Codeg (Code Generation) is a multi-agent coding workspace: run every AI coding agent in one place — and let them work together.
Repo: xintaofei/codeg
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent,…
Use when facing 2+ independent tasks that can be worked on without shared state or sequential dependencies
Use when you have a written implementation plan to execute in a separate session with review checkpoints
Use when implementation is complete, all tests pass, and you need to decide how to integrate the work
Use when receiving code review feedback, before implementing suggestions, especially if feedback seems unclear or technically questionable - requires technical…
Use when completing tasks, implementing major features, or before merging to verify work meets requirements