Agent skill · research science · k-dense-ai
scholar-evaluation
Provide qualitative-first, evidence-traceable developmental review of scholarly works and audit low-stakes research-assessment rubrics with optional local quality controls. Never use for ranking people or consequential decisions.
Why this skill is useful
Provides domain-specific frameworks and qualitative assessment methodologies for evaluating scholarly works that the AI wouldn't reliably generate on its own.
What it needs
Requires python installed locally. About 6k tokens when loaded. Last updated 2026-08-03. 32,849 stars on the source repository.
What this skill does
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. If asked for a prohibited use, stop. Offer developmental comments on a scholarly work or a process-only audit that does not process applications, compare people, recommend an outcome, or advise a decision. Do not issue publication-readiness, accept/reject, or “top-tier” judgments. Read references/responsibleassessment.md before any organizational use. ScholarEval status The referenced ScholarEval project is an experimental literature-grounded research-idea evaluation framework, not validated psychometrics. The verified primary record is Moussa et al., ScholarEval: Research Idea Evaluation Grounded in Literature, arXiv:2510.16234v2, revised 2026-02-28. It reports a retrieval-augmented soundness/contribution framework, a 117-idea four-discipline dataset, coverage experiments, and a user study. Do not generalize those results to person assessment, consequential decisions, all disciplines, or this skill's rubric. No peer-reviewed publication status was verified during the dated review. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills k-dense-ai/scholar-evaluation