Skip to content
Skillpedia
Research280k · #278

scientific-thinking-scholar-evaluation

Allows the agent to evaluate academic work in a structured way. It's useful for checking the quality of papers and research.

What it does

This tool evaluates academic or scientific work, like papers and proposals, using a clear set of criteria. It helps check research quality, clarity of ideas, and whether claims are well-supported.

When to use it

  • ✓When reviewing a scientific paper or thesis.
  • ✓To check if claims in a work are supported by evidence.
  • ✓When comparing the quality of different research pieces.

Example of what to ask your AI

“Evaluate this paper on artificial intelligence, focusing on the methodology and evidence quality.”

Want more? Open the sections below.

How to use

The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.

Skill link

https://github.com/affaan-m/ECC/blob/main/skills/scientific-thinking-scholar-evaluation/SKILL.md

Ready-to-paste message

Use this skill to help me: https://github.com/affaan-m/ECC/blob/main/skills/scientific-thinking-scholar-evaluation/SKILL.md Read the SKILL.md file at that link and follow its instructions for my task.
How it works

The AI analyzes the academic work across several sections like the problem, methodology, and citations. It assigns a score from 1 to 5 for each criterion and provides a summary with suggestions for improvement.

Installation

Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.

In the terminal (Claude Code, Codex, Cursor), in your project folder:

npx skills add affaan-m/ECC --skill scientific-thinking-scholar-evaluation

Or download just the file:

curl -L https://raw.githubusercontent.com/affaan-m/ECC/main/skills/scientific-thinking-scholar-evaluation/SKILL.md -o SKILL.md
Technical content (original SKILL.md)

Original text by the author, straight from GitHub.

Scholar Evaluation

Use this skill to evaluate academic or scientific work with a repeatable rubric.

When to Use

  • Reviewing a research paper, proposal, thesis chapter, or literature review.
  • Checking whether claims are supported by cited evidence.
  • Evaluating methodology, study design, analysis, or limitations.
  • Comparing two or more papers for quality or relevance.
  • Producing structured feedback for revision.

Evaluation Scope

Start by identifying the artifact:

  • empirical research paper
  • theoretical paper
  • technical report
  • systematic or narrative literature review
  • research proposal
  • thesis or dissertation chapter
  • conference abstract or short paper

Then choose scope:

  • comprehensive: all rubric dimensions
  • targeted: one or two dimensions, such as method or citations
  • comparative: rank multiple works against the same rubric

Rubric

Score each applicable dimension from 1 to 5:

  • 5: excellent; clear, rigorous, and publication-ready
  • 4: good; minor improvements needed
  • 3: adequate; meaningful gaps but usable
  • 2: weak; substantial revision needed
  • 1: poor; major validity or clarity problems

Use N/A for dimensions that do not apply.

1. Problem and Research Question

  • Is the problem clear and specific?
  • Is the contribution meaningful?
  • Are scope and assumptions explicit?
  • Does the question match the claimed contribution?

2. Literature and Context

  • Is relevant prior work covered?
  • Does the work synthesize rather than merely list sources?
  • Are gaps accurately identified?
  • Are recent and foundational sources balanced?

3. Methodology

  • Does the method answer the research question?
  • Are design choices justified?
  • Are variables, datasets, participants, or materials described clearly?
  • Could another researcher reproduce the work?
  • Are ethical and practical constraints acknowledged?

4. Data and Evidence

  • Are data sources credible and appropriate?
  • Is sample size or corpus coverage adequate?
  • Are inclusion, exclusion, and preprocessing decisions documented?
  • Are missing data and bias risks discussed?

5. Analysis

  • Are statistical, qualitative, or computational methods appropriate?
  • Are baselines and controls fair?
  • Are uncertainty, sensitivity, or robustness checks included when needed?
  • Are alternative explanations considered?

6. Results and Interpretation

  • Are results clearly presented?
  • Do claims stay within the evidence?
  • Are figures, tables, and metrics understandable?
  • Are negative or null results handled honestly?

7. Limitations and Threats to Validity

  • Are limitations specific rather than generic?
  • Are internal, external, construct, and conclusion-validity risks addressed?
  • Does the paper distinguish speculation from demonstrated results?

8. Writing and Structure

  • Is the argument easy to follow?
  • Are sections organized around the research question?
  • Are definitions and notation clear?
  • Is the tone precise and scholarly?

9. Citations

  • Do cited papers support the claims attached to them?
  • Are primary sources used where possible?
  • Are reviews labeled as reviews?
  • Are preprints labeled as preprints?
  • Are citation metadata and links correct?

Review Process

  1. Read the abstract, introduction, figures, and conclusion for claimed contribution.
  2. Read methods and results for evidence quality.
  3. Check the strongest claims against cited sources.
  4. Score each applicable dimension.
  5. Separate critical blockers from revision suggestions.
  6. End with concrete next edits.

Output Template

# Scholar Evaluation: <Artifact>

## Overall Assessment

- Overall score: <1-5 or N/A>
- Confidence: <high | medium | low>
- Summary: <3-5 sentences>

## Dimension Scores

| Dimension | Score | Evidence | Revision priority |
| --- | ---: | --- | --- |
| Problem and question |  |  |  |
| Literature and context |  |  |  |
| Methodology |  |  |  |
| Data and evidence |  |  |  |
| Analysis |  |  |  |
| Results and interpretation |  |  |  |
| Limitations |  |  |  |
| Writing and structure |  |  |  |
| Citations |  |  |  |

## Critical Issues

## Recommended Revisions

## Evidence Checks Needed

Pitfalls

  • Do not use the score as a substitute for concrete feedback.
  • Do not penalize a paper for omitting a dimension outside its scope.
  • Do not treat citation count, venue, or author reputation as proof of quality.

…

FAQ

What does the 1-5 score mean?

A score of 5 is excellent, 3 is adequate, and 1 is very weak. The AI uses these scores to indicate the quality of each aspect of the work.

Can I ask it to evaluate only a specific part of the work?

Yes, you can request a 'targeted' evaluation focusing on specific aspects like methodology or citations.

Will the AI tell me exactly what to change?

The AI provides structured feedback and revision suggestions, but the final decision on changes is yours.

Details

Original description by the author

Structured scholarly-work evaluation for papers, proposals, literature reviews, methods sections, evidence quality, citation support, and research-writing feedback. Use when evaluating academic or scientific work — papers, proposals, methods sections, or evidence quality — against a repeatable rubric.

repo: affaan-m/ECC

Popularity

GitHub stars
280k
Ranking position
#278
License
MIT
Updated
10/15/2026

Is this skill useful?

0

Sign in to vote.

Comments

  • …

More skills from this repository

Similar skills