# scholar-evaluation

> 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.

**Use case**: Score scholarly work across research quality dimensions

**Canonical URL**: https://agentcookbooks.com/skills/scholar-evaluation/

**Topics**: claude-code, skills, science, scientific-writing

**Trigger phrases**: "evaluate this paper", "score this manuscript", "assess research quality", "ScholarEval review", "rate this study"

**Source**: [K-Dense AI](https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/scientific-skills/scholar-evaluation)

**License**: MIT

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## What it does

`scholar-evaluation` is a Claude Code skill from K-Dense AI's [scientific-agent-skills repo](https://github.com/K-Dense-AI/scientific-agent-skills). It turns Claude into a structured evaluator that applies the ScholarEval framework to scholarly work — producing quantitative scores across research quality dimensions (problem formulation, methodology, analysis, writing) alongside actionable feedback for each dimension.

A session produces a scored evaluation report: dimension-level scores, an overall rating, strengths, weaknesses, and specific improvement recommendations — structured for use in grant panels, research training programs, or self-assessment workflows.

## When to use it

Reach for it when:

- You're on a grant review panel and need a consistent scoring framework across multiple applications
- You're running a research training program and want structured, reproducible feedback on student work
- You want a quantitative self-assessment of your own manuscript before submission

When *not* to reach for it:

- Writing the narrative text of a peer review — use `peer-review`
- Evaluating evidence quality for a clinical decision — use `scientific-critical-thinking`

## Install

Copy the `SKILL.md` from K-Dense AI's [scholar-evaluation folder](https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/scientific-skills/scholar-evaluation) into `.claude/skills/scholar-evaluation/` in your project.

Trigger phrases: "evaluate this paper", "score this manuscript", "assess research quality", "ScholarEval review".

## What a session looks like

A typical session has three phases:

1. **Manuscript intake.** Provide the paper text or file. Claude identifies the research type and calibrates which ScholarEval dimensions apply (e.g., clinical research dimensions differ from computational work).
2. **Dimension scoring.** Each ScholarEval dimension is evaluated independently with a score and supporting rationale drawn from the manuscript text.
3. **Report generation.** Scores are aggregated, strengths and weaknesses are summarized, and prioritized improvement recommendations are listed — structured for easy comparison across a set of papers.

## Receipts

**Where it works well:**
- Grant panel workflows where consistency across reviewers matters — the framework produces comparable scores across different reviewers using the same rubric
- Training contexts where students need specific, dimension-level feedback rather than general comments

**Where it backfires:**
- Highly interdisciplinary work that doesn't map cleanly onto standard research quality dimensions
- The scoring can produce confident-seeming numbers for dimensions where the manuscript is ambiguous; scores should be treated as a starting point for discussion, not final verdicts

**Pattern that works:** use the dimension scores to structure discussion in panel review, not to make yes/no decisions mechanically; the framework's value is in consistent vocabulary, not algorithmic selection.

## Source and attribution

Originally authored by [K-Dense Inc.](https://github.com/K-Dense-AI). The canonical SKILL.md lives in the [`scholar-evaluation` folder](https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/scientific-skills/scholar-evaluation) of their public scientific-agent-skills repository.

License: MIT. Install, adapt, and redistribute with attribution preserved.

This page documents the skill from a practitioner's perspective. For the formal spec and any updates, defer to the source repo.