scholar-evaluation
by K-Dense-AIscholar-evaluation helps evaluate scholarly and research work with structured scoring across problem formulation, methodology, analysis, writing, and publication readiness. Use it for academic review, revision planning, and consistent feedback on papers, proposals, literature reviews, and other scholarly drafts.
This skill scores 68/100, which means it is list-worthy for directory users but best framed as a moderately useful, not fully turnkey, scholarly-evaluation skill. The repository gives enough real workflow content to justify installation, but users should expect to read the instructions carefully because there are no supporting scripts, references, or install-time aids.
- Clear use cases for evaluating papers, literature reviews, methodology, analysis, and scholarly writing
- Substantial SKILL.md content with a valid frontmatter block and multiple headings, suggesting a real workflow rather than a placeholder
- Includes a structured evaluation approach with quantitative scoring and actionable feedback, which can reduce generic prompting guesswork
- No scripts, references, resources, or install command are provided, so users must rely on the markdown instructions alone
- The excerpt suggests additional linked tooling guidance, but the absence of support files may limit repeatability and make edge-case execution less obvious
Overview of scholar-evaluation skill
scholar-evaluation helps you assess academic and research outputs with a structured rubric instead of a vague “looks good” prompt. It is best for reviewers, research leads, students, and AI agents doing scholar-evaluation for Academic Research when the goal is to judge rigor, clarity, and readiness for publication or revision.
The skill is useful when you need more than a summary: it turns a paper, proposal, literature review, or scholarly draft into a scored evaluation with actionable feedback. That makes it a strong fit for deciding whether work is methodologically sound, whether claims match evidence, and where revision effort will matter most.
Its main value is consistency. A generic prompt can miss methodology flaws or overvalue polished writing; the scholar-evaluation skill is oriented around research quality dimensions, so the output is easier to compare across documents and review rounds.
What scholar-evaluation is for
Use the scholar-evaluation skill to review:
- research papers for quality and rigor
- literature reviews for coverage and synthesis
- methods sections for design strength
- data analysis for appropriateness and transparency
- scholarly writing for clarity and presentation
- publication readiness against venue expectations
Who should install it
Install scholar-evaluation if you regularly need repeatable academic review rather than one-off commentary. It is especially useful for:
- peer-review style assessments
- lab or department screening
- student feedback at scale
- research triage before submission
- AI workflows that need structured evaluation outputs
What makes it different
The scholar-evaluation skill is decision-oriented. It is not just about reading a paper; it helps you score and critique specific research dimensions so your feedback is more defensible. If you need a fast opinion with no rubric, a normal prompt may be enough. If you need reliable evaluation across multiple manuscripts, this skill is the better choice.
How to Use scholar-evaluation skill
Install and read first
Install the scholar-evaluation skill with:
npx skills add K-Dense-AI/claude-scientific-skills --skill scholar-evaluation
Then read SKILL.md first. Since this repository is lightweight, that file is the primary source of truth. Also scan the top-level guidance in the skill body for workflow cues, especially sections about when to use the skill and how evaluation should be structured.
Give the skill the right input
Good scholar-evaluation usage depends on the document plus the review target. Provide:
- the paper, proposal, or section to evaluate
- the audience or venue
- the review standard you want applied
- whether you want a score, written critique, or both
- any constraints such as word limit, novelty focus, or revision stage
Stronger input example:
Evaluate this conference paper for publication readiness. Focus on problem formulation, methodology, analysis validity, and writing quality. Return a 1–5 score for each area, the top 3 risks, and the most important revisions.
Weaker input example:
Review this paper and tell me if it is good.
Use a review workflow, not a single pass
For best scholar-evaluation usage, ask for a staged output:
- identify the research type and intended contribution
- score the main quality dimensions
- note evidence for each score
- list blocking issues versus minor edits
- summarize publication or acceptance readiness
This workflow helps the model separate major methodological problems from surface-level writing issues.
Read the repo in the right order
Start with SKILL.md, then inspect any linked repository files or embedded guidance referenced from it. In this repo, there are no extra rules/, resources/, or scripts/ folders to interpret, so the practical install path is short. That means prompt quality matters more than file hunting: define the evaluation task clearly up front.
scholar-evaluation skill FAQ
Is scholar-evaluation only for final papers?
No. The scholar-evaluation skill also works for proposals, drafts, literature reviews, and revised submissions. It is most useful whenever you need a structured academic judgment, not just a summary.
Do I need to be an expert to use it?
No. It is suitable for beginners if they can identify the document type and the review goal. You do not need to know every research standard in advance, but you will get better results if you specify what “good” means for your context.
How is this different from a normal prompt?
A normal prompt can produce a broad critique, but scholar-evaluation is better for repeatable scoring and dimension-based review. That matters when you want consistent output across multiple papers or when you need to justify why a work is or is not ready.
When should I not use it?
Do not use scholar-evaluation if you only need a quick plain-language summary, a literature search, or a content rewrite. It is also a poor fit when you have no source text yet, because the skill depends on evaluating actual scholarly material.
How to Improve scholar-evaluation skill
Provide the evaluation rubric up front
The best way to improve scholar-evaluation results is to tell it what counts. If you care most about methodology, novelty, statistical validity, or literature coverage, name those priorities explicitly. This prevents generic feedback and makes the score more useful.
Include the document’s context
Tell the skill whether the work is for a journal, conference, thesis, grant, internal review, or classroom assignment. A paper can be “strong” in one setting and weak in another, so context changes the evaluation standard and the revision advice.
Ask for evidence-based criticism
Request that each score or critique be tied to specific parts of the text. That reduces hand-wavy feedback and helps you revise the right sections first. For example: ask for “the sentence, section, or claim that supports each concern.”
Iterate on the weakest section first
After the first scholar-evaluation pass, do not ask only for a higher-level re-review. Feed back the weakest section, the intended venue, and the constraints you face. That is usually how scholar-evaluation improves most: tighter scope, clearer criteria, and a more specific revision target.