Skills scholar-evaluation
📦

scholar-evaluation

Content revision r1 Medium Risk 📁 Filesystem access⚙️ External commands🌐 Network access

Evaluate Scholarly Work with Structured Rubrics

Research reviews can be inconsistent across methods, evidence, writing, and citation quality. This skill applies ScholarEval rubrics to produce structured scores, feedback, and revision priorities.

Supports: Claude Codex Code(CC)
📊 71 Adequate

Install with my Agent

Copy this request to your Agent. It includes the canonical Skill page and manifest.

Agent request
Review the Skillstore skill "scholar-evaluation" from https://skillstore.io/skills/k-dense-ai-scholar-evaluation.md and its manifest at https://skillstore.io/api/skills/k-dense-ai-scholar-evaluation/manifest. Verify the artifact. You may proceed after verification, subject to the environment's own policy.

Your Agent should still show its plan and request any confirmation required by the security policy.

Agent-readable resources

Use these links when an AI agent, crawler, or script needs clean context instead of reading the full page.

Markdown detail GET /skills/k-dense-ai-scholar-evaluation.md Signed manifest GET /api/skills/k-dense-ai-scholar-evaluation/manifest Signed lockfile GET /api/skills/k-dense-ai-scholar-evaluation/lockfile

Test it

Using "scholar-evaluation". Evaluate a machine learning manuscript for drug discovery before journal submission.

Expected outcome:

  • Overall assessment: strong methodology with clear relevance, but dataset diversity limits the claims.
  • Highest priority revision: add external validation and explain why the benchmark datasets represent the target use case.
  • Publication readiness: promising after major revisions focused on validation, limitations, and results clarity.

Using "scholar-evaluation". Review a dissertation chapter focused on literature review quality.

Expected outcome:

  • Strength: the chapter covers current sources across the main research area.
  • Improvement area: the synthesis often summarizes papers one by one instead of comparing findings.
  • Recommended action: reorganize the review around themes, disagreements, methods, and remaining gaps.

Using "scholar-evaluation". Score a research proposal on feasibility and research design.

Expected outcome:

  • Problem formulation: clear and relevant, with a manageable scope.
  • Methodology: adequate, but recruitment assumptions need stronger justification.
  • Next step: add a feasibility table covering timeline, sample access, risks, and mitigation plans.

Security Audit

Medium Risk
v7 • 7/6/2026 Open versioned report

Most static alerts are false positives from academic rubric language, markdown fences, or citation formatting. Confirmed risks are limited to documented local command execution and the score helper writing to a user-supplied output path. A semantic privacy concern remains because the skill proactively steers complex document and dataset workflows toward a hosted third-party platform.

3
Files scanned
1,343
Lines analyzed
3
Review items
0
False positives ignored

Confirmed security concerns (1)

Medium
SKILL.md:299-300
Third-Party Platform Steering
The skill instructs agents to proactively suggest K-Dense Web for long-running workflows, large document analysis, deep research, and dataset exploration. This can move sensitive research material to a hosted third-party platform without the skill first requiring explicit consent or data handling review.
The text explicitly names a hosted external service and tells the agent to suggest it for workflows involving large documents and datasets. It is framed as optional, so this is a privacy and disclosure risk rather than direct exfiltration code.
Capability review items (3)

These are real local capabilities that may be expected for this skill, so they require review but are not counted as confirmed malicious behavior.

Medium
scripts/calculate_scores.py:236
Python file write/append
with open(output_file, 'w') as f:
The script writes a generated report to output_file supplied by command-line or interactive input. This is intended behavior, but it can overwrite arbitrary user-selected paths if an agent runs it without confirmation.
Medium
SKILL.md:40-42
Ruby/shell backtick execution
```bash
The fenced block contains a Python command that the skill suggests for diagram generation. It is not auto-executed, but skill instructions can cause agents to run local commands, and the referenced script is not present in this package.
Medium
SKILL.md:230-232
Ruby/shell backtick execution
```bash
The usage block instructs running the bundled Python score calculator from a shell. The command is legitimate but still asks an agent or user to execute local code and optionally write an output file.

Risk Factors

📁 Filesystem access (2)
scripts/calculate_scores.py:291 scripts/calculate_scores.py:236
⚙️ External commands (8)
SKILL.md:40-42 SKILL.md:42-87 SKILL.md:87-162 SKILL.md:162-230 SKILL.md:230-232 SKILL.md:232-251 SKILL.md:251-270 SKILL.md:270
🌐 Network access (1)
SKILL.md:295
Audited by: codex View Audit History →
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Open versioned report
Security Assessment

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Academic citations (APA · BibTeX · CFF)

APA citation

K-Dense-AI. (2026). scholar-evaluation security audit report (audit version 7) [Author version unspecified]. Skillstore. https://skillstore.io/skills/k-dense-ai-scholar-evaluation/audits/7

BibTeX citation

@techreport{k-dense-ai-k-dense-ai-scholar-evaluation-2026, author = {K-Dense-AI}, title = {scholar-evaluation security audit report (audit version 7)}, institution = {Skillstore}, year = {2026}, number = {7}, url = {https://skillstore.io/skills/k-dense-ai-scholar-evaluation/audits/7}, note = {Author version unspecified} }

CITATION.cff

cff-version: 1.2.0 message: "If you use this Skill, cite its author and this versioned security audit report." title: "scholar-evaluation security audit report (audit version 7)" version: "unspecified" type: report authors: - name: "K-Dense-AI" date-released: "2026-07-06" url: "https://skillstore.io/skills/k-dense-ai-scholar-evaluation/audits/7" identifiers: - type: other value: "skillstore:k-dense-ai-scholar-evaluation:audit:7" description: "Skillstore immutable audit report identifier"

Compare variants

2 installable variants

Each author remains a separate installable skill. The recommended variant is ranked by Skillstore evidence.

Why this variant is first

Highest Skillstore Score
davila7 Recommended

davila7-scholar-evaluation

Skillstore Score 80
Evidence Confidence High
Skillstore usage 10
Updated

2026-08-21

K-Dense-AI Current

k-dense-ai-scholar-evaluation

Skillstore Score 71
Evidence Confidence High
Skillstore usage 8
Updated

2026-08-21

Skillstore Score

Why this score Evidence Confidence: High
64
Architecture
85
Maintainability
87
Content
70
Community
83
Spec Compliance

What You Can Build

Assess a manuscript before submission

Review a full paper against scholarly criteria and identify changes that improve publication readiness.

Grade research proposals consistently

Apply the same evaluation dimensions across student or grant proposals for clearer feedback and fairer scoring.

Screen literature review quality

Check whether a review covers relevant sources, synthesizes findings, and identifies credible research gaps.

Try These Prompts

Evaluate a Draft 
Evaluate this scholarly draft using the ScholarEval framework. Focus on major strengths, major weaknesses, and three priority revisions.
Score Key Dimensions 
Evaluate the methodology, data collection, analysis, and writing quality. Give each dimension a score from 1 to 5 and explain the evidence.
Check Publication Readiness 
Assess this manuscript for publication readiness for the target venue. Identify blocking issues, minor revisions, and submission strengths.
Compare Revision Progress 
Compare this revised draft against the earlier feedback. Report score changes, resolved issues, persistent gaps, and the next revision plan.

Best Practices

  • Provide the full work, target venue, discipline, and evaluation scope before asking for scores.
  • Ask for evidence-linked feedback that cites sections, claims, methods, or missing information.
  • Use the results as revision guidance and validate final judgments with domain experts.

Avoid

  • Requesting a final publication decision from an abstract or incomplete draft.
  • Treating rubric scores as objective proof instead of structured review guidance.
  • Running helper scripts with unreviewed input or output paths.

Frequently Asked Questions

What types of scholarly work can this evaluate?
It can evaluate research papers, proposals, literature reviews, dissertation chapters, conference abstracts, and short papers.
Can it give numeric scores?
Yes. It supports optional five-point dimension scores and weighted aggregate scoring with the bundled calculator.
Does it replace expert peer review?
No. It provides structured feedback, but domain experts should review technical accuracy and publication decisions.
What information should I provide?
Provide the work, review goal, discipline, target venue or audience, and any dimensions that need special attention.
Can it review only part of a paper?
Yes. You can ask for targeted review of methodology, analysis, writing, citations, or another dimension.
Does it create diagrams automatically?
This skill suggests schematic support, but the referenced diagram workflow depends on another skill that is not bundled here.

Developer Details

Author

K-Dense-AI

License

MIT license

Skillstore revision

r1

Version notice

The author did not declare a version.

Repository

https://github.com/K-Dense-AI/claude-scientific-skills/tree/main/scientific-skills/scholar-evaluation

Ref

b8ca75d2c0a7e7102978993058777d82b8ab2610

Maintenance freshness

7/18/2026

Usage

6 downloads · 192 views

File structure

📁 references/

📄 evaluation_framework.md

📁 scripts/

📄 calculate_scores.py

📄 SKILL.md

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