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Scholar Evaluation

Gives developmental, evidence-traceable feedback on scholarly works and audits low-stakes assessment rubrics.

Source Download skill
Repository
K-Dense-AI/scientific-agent-skills
Stars
55,780
Licence
MIT
Files
15 files

What it does

This skill teaches an AI agent to review a scholarly work (a paper, draft, protocol, literature review, or research idea) in a qualitative, developmental way, linking comments to specific evidence locations. It can also check whether a low-stakes assessment process documents its construct, rater quality, uncertainty, privacy and human governance, using bundled local scripts that validate rubric and evaluation JSON/CSV files and calculate bounded rubric math. It explicitly forbids use for hiring, funding, admissions, awards, discipline, ranking people, or publication-readiness judgments.

Great for

  • Researchers wanting structured feedback on a draft paper or research protocol
  • Students learning how to evaluate the evidence behind a scholarly claim
  • Groups designing a low-stakes, developmental review rubric who want a governance checklist

Skip it if

  • Hiring, promotion, tenure, admissions, grants or awards decisions: the skill explicitly prohibits these and the tools are not validated for person assessment
  • Judging whether a paper is publishable or top-tier: the skill forbids such judgments and no validated psychometric basis exists
  • Ranking people or using journal impact factors, h-index or venue prestige as quality signals: the skill and rubric validator reject these proxies

Try asking

  • “Give developmental feedback on my draft manuscript, linking each comment to a specific section”
  • “Help me audit whether our low-stakes peer-feedback process documents its construct and rater training”
  • “Build an evidence manifest for the claims in this literature synthesis”
  • “Check my rubric JSON for structural problems before a pilot review”
Step-by-step guide: how to use Scholar EvaluationInstall, first run, examples, tips and FAQs

Install in 3 steps

  1. 1

    Download the skill

    The ZIP contains the 15 original files, the MIT licence and an attribution note.

    scholar-evaluation.zip
  2. 2

    Add it to your agent

    Claude Code (all projects)

    mkdir -p ~/.claude/skills && unzip scholar-evaluation.zip -d ~/.claude/skills/

    Claude Code / Agent SDK (this project only)

    mkdir -p .claude/skills && unzip scholar-evaluation.zip -d .claude/skills/

    Claude apps (web/desktop): Settings → Capabilities → Skills → Upload skill, and choose the ZIP.

  3. 3

    Ask for the task

    No special command needed — the agent reads the skill's description and loads it when your request matches. Try one of the prompts above.

Get better results

  • Keep inputs to synthetic, public, or deidentified data only; do not paste private applications, CVs or letters
  • Freeze the exact work revision and evidence-access date before independent rating so raters assess the same material
  • Treat any scores as descriptive mappings to a predeclared rubric, and have qualified humans review the output

What's inside

  • assets/evaluation_template.json
  • assets/evidence_manifest_template.json
  • assets/process_checklist_template.json
  • assets/ratings_template.csv
  • assets/rubric_template.json
  • references/evaluation_framework.md
  • references/local_tooling.md
  • references/responsible_assessment.md
  • references/security_validation.md
  • references/source_ledger.md
  • scripts/_common.py
  • scripts/calculate_scores.py
  • scripts/check_process.py
  • scripts/check_traceability.py
  • SKILL.md

Redistributed unmodified from K-Dense-AI/scientific-agent-skills under the MIT licence. The assessment was drafted with AI from the skill's files on 2026-10-29. Skills can instruct your agent to run code — read SKILL.md and any scripts before installing.