scholar-evaluation
Skill3 starsChanged 8 months agoMITView on GitHub
Score and assess CS papers, proposals, literature reviews, and benchmark plans using a structured evaluation rubric. Use when a quantitative or semi-structured assessment is needed for research quality, novelty, technical soundness, empirical rigor, reproducibility, clarity, and submission readiness.
19 other projects use a version of this.
Adopted by
Most recently taken up by
- BrightGold70/skillstheir own version, first seen Nov 8, 2026
- pradyumnasagar/open-research-skillstheir own version, first seen Nov 8, 2026
- K-Dense-AI/claude-scientific-writertheir own version, first seen Nov 7, 2026
- LARi-UQAC/ResearchToolstheir own version, first seen Nov 5, 2026
- MarieLynneBlock/arcanum-artifextheir own version, first seen Nov 1, 2026
- renepf/claude-skillstheir own version, first seen Oct 25, 2026
Other versions19 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 19
- K-Dense-AI/claude-scientific-writer★ 2.6ksame name
- wu-yc/LabClaw★ 1.1ksame name
- LeonChaoX/qinyan-academic-skills★ 910same name
- qinyan-ai/qinyan-academic-skills★ 80same name
- Barrios88/barrios-skills★ 63same name
- Fmarzochi/EGC★ 61same name
- LARi-UQAC/ResearchTools★ 14same name
- pradyumnasagar/open-research-skills★ 8same name
- Wondermonger-daydreaming/claude-skills-library★ 7same name
- MarieLynneBlock/arcanum-artifex★ 4same name
9 more not listed
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository43 other files it gives its agents.
- agent-codingSkill
- benchmark-designSkill
- citation-managementSkill
- code-reproductionSkill
- code-to-paperSkill
- daskSkill
- experiment-trackingSkill
- exploratory-data-analysisSkill
- get-available-resourcesSkill
- github-release-prepSkill
- latex-postersSkill
- literature-reviewSkill
- markdown-mermaid-writingSkill
- markitdownSkill
- matplotlibSkill
- modalSkill
- networkxSkill
- paper-to-codeSkill
- peer-reviewSkill
- plotlySkill
- polarsSkill
- pytorch-lightningSkill
- pyzoteroSkill
- repo-cleanupSkill
- research-planningSkill
- reviewer-responseSkill
- scientific-critical-thinkingSkill
- scientific-schematicsSkill
- scientific-slidesSkill
- scientific-visualizationSkill
- scientific-writingSkill
- scikit-learnSkill
- seabornSkill
- shapSkill
- stable-baselines3Skill
- statistical-analysisSkill
- statsmodelsSkill
- sympySkill
- torch-geometricSkill
- transformersSkill
- umap-learnSkill
- vaexSkill
- venue-templatesSkill
The file
We split this skill into the rules it gives an agent
5 rules. 0 are common, 5 are distinctive, and 0 are very specific to this project.
0
Common
Most of the 18 edited versions we read keep these.
5
Distinctive
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 8
Use this skill when the user wants more structure than a free-form review but does not need a full venue-specific peer review.
- Distinctive: line 34
Use the script when a numeric roll-up is useful.
- Distinctive: line 38
Do not hide major flaws behind an average score.
- Distinctive: line 41
Use scoring to support judgment, not replace it.
- Distinctive: line 45
When no format is requested, return:
“Common” is measured: kept by at least 6 in 10 of the 18 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Scholar Evaluation
Use this skill when the user wants more structure than a free-form review but does not need a full venue-specific peer review.
Good Fits
- comparing multiple candidate papers
- grading a draft literature review
- evaluating a research proposal before implementation
- checking whether a benchmark plan is rigorous enough
- estimating whether a draft is close to submission quality
CS-Oriented Dimensions
Score or discuss these explicitly:
- problem importance and positioning
- novelty or distinct value
- technical soundness
- empirical rigor or evaluation adequacy
- reproducibility and artifact clarity
- writing clarity and organization
- risk areas that could block publication or adoption
Local Resources
references/evaluation_framework.mdscripts/calculate_scores.py
Use the script when a numeric roll-up is useful. Otherwise keep the evaluation semi-structured and explain the score rationale.
Working Rules
- Do not hide major flaws behind an average score.
- Separate missing evidence from poor writing.
- State what would most improve the work.
- Use scoring to support judgment, not replace it.
Default Output
When no format is requested, return:
- dimension-by-dimension assessment
- strengths
- highest-impact weaknesses
- overall readiness judgment
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.