Scholar Evaluation

This applies the ScholarEval framework to systematically assess research papers, proposals, and academic writing across eight dimensions: problem formulation, literature review, methodology, data collection, analysis, results, writing quality, and citations. You get quantitative scores on a 5-point scale plus qualitative feedback with specific strengths and improvement areas. The workflow walks you through scoping the evaluation, dimension-by-dimension assessment using detailed rubrics, and generating actionable feedback prioritized by impact. It adjusts for context like draft stage, venue standards, and discipline norms. Useful for peer review, publication readiness checks, or providing structured feedback on student work. The framework is thorough but you'll need to invest time working through each dimension for a complete evaluation.

skills.sh installs: 2.2K
GitHub stars: 56.6K
last updated: Aug 21, 2026

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Install

$ npx -y skills add k-dense-ai/scientific-agent-skills --skill scholar-evaluation --agent claude-code

Installs into .claude/skills of the current project.

Files

Scholar Evaluation

Purpose

Provide developmental, evidence-traceable feedback on a scholarly work: paper, draft, protocol, literature synthesis, or research idea. Use qualitative judgment first. Optional scores only describe how submitted evidence maps to a predeclared bounded rubric.

This skill also audits whether a low-stakes assessment process documents its construct, provenance, rater quality, uncertainty, traceability, sensitivity, fairness, accessibility, privacy, and human governance.

Hard safety boundary

Never use this skill to automate, recommend, materially influence, or score:

hiring, promotion, or tenure;
admissions;
grants or other funding;
prizes, honors, or awards;
discipline, dismissal, or sanctions; or
any other high-impact personnel decision.

Never rank people. Never reduce a person to a composite score. Never infer ability, character, integrity, protected traits, future performance, or worth. A nominal human-in-the-loop does not remove this boundary.

SKILL.md on GitHub