Mmcp.market

scholar-evaluation skill

by K-Dense-AI·K-Dense-AI/scientific-agent-skills·47k stars·MIT

Provide qualitative-first, evidence-traceable developmental review of scholarly works and audit low-stakes research-assessment rubrics with optional local quality controls. Never use for ranking people or consequential decisions.

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Is the scholar-evaluation skill safe?

Clean: nothing in its files matched our rules. We read 19 files in the folder on 2026-09-28.

No findings.

Install the scholar-evaluation skill

A skill is a folder. Copy it into your agent's skills folder and the agent loads it when the task matches its description. 

git clone --depth 1 https://github.com/K-Dense-AI/scientific-agent-skills.git /tmp/scientific-agent-skills
mkdir -p ~/.claude/skills
cp -r /tmp/scientific-agent-skills/skills/scholar-evaluation ~/.claude/skills/scholar-evaluation
available in every project

In the Claude apps, zip the folder and upload it from the Skills settings. The folder on GitHub

The instructions your agent would load

SKILL.md as published, without the frontmatter. Read it on GitHub

Scholar Evaluation

Purpose

Provide developmental, evidence-traceable feedback on a scholarly work: paper, draft, protocol, literature synthesis, or research idea. Use qualitative judgment first. Optional scores only describe how submitted evidence maps to a predeclared bounded rubric.

This skill also audits whether a low-stakes assessment process documents its construct, provenance, rater quality, uncertainty, traceability, sensitivity, fairness, accessibility, privacy, and human governance.

Hard safety boundary

Never use this skill to automate, recommend, materially influence, or score:

  • hiring, promotion, or tenure;
  • admissions;
  • grants or other funding;
  • prizes, honors, or awards;
  • discipline, dismissal, or sanctions; or
  • any other high-impact personnel decision.

Never rank people. Never reduce a person to a composite score. Never infer ability, character, integrity, protected traits, future performance, or worth. A nominal human-in-the-loop does not remove this boundary.

If asked for a prohibited use, stop. Offer developmental comments on a scholarly work or a process-only audit that does not process applications, compare people, recommend an outcome, or advise a decision.

Do not issue publication-readiness, accept/reject, or “top-tier” judgments.

Read references/responsible_assessment.md before any organizational use.

ScholarEval status

The referenced ScholarEval project is an experimental literature-grounded research-idea evaluation framework, not validated psychometrics.

The verified primary record is Moussa et al., ScholarEval: Research Idea Evaluation Grounded in Literature, arXiv:2510.16234v2, revised 2026-02-28. It reports a retrieval-augmented soundness/contribution framework, a 117-idea four-discipline dataset, coverage experiments, and a user study.

Do not generalize those results to person assessment, consequential decisions, all disciplines, or this skill's rubric. No peer-reviewed publication status was verified during the dated review. See references/source_ledger.md.

Metric and prestige policy

Do not score or infer quality from:

  • Journal Impact Factor or other journal measures;
  • h-index, publication counts, or citation counts;
  • altmetrics or attention;
  • journal, conference, venue, institution, employer, or geographic prestige;
  • author affiliation, reputation, network, or career path.

The rubric validator rejects common proxy-measure criteria.

If a qualified reviewer mentions an indicator descriptively outside the scoring tools, record its exact purpose, source, coverage, field and time effects, uncertainty, missingness, biases, gaming risk, and why it does not directly measure quality. Never hide indicators inside an opaque composite.

Data boundary

Bundled scripts accept only strict local JSON/CSV containing pseudonymous IDs, bounded ratings, statuses, uncertainty, and local references.

Do not put raw private applications, CVs, letters, reviewer identities, contact details, protected attributes, or source-document text in inputs, outputs, logs, examples, or prompts. Keep source content in the authorized records system and use opaque local references.

Allowed classifications are:

  • synthetic
  • publicscholarlywork
  • deidentifiedlowstakes

No script searches the web, loads environment files, reads credentials, calls a model, executes supplied text, deserializes executable objects, or launches a process.

Use Bash only to invoke the documented local python3 commands.

Workflow

1. Confirm allowed use and authorization

Record:

  • developmental purpose;
  • unit of assessment: scholarly_work;
  • work type, stage, discipline, language, and audience;
  • authorized source location and data classification;
  • accountable committee owner;
  • conflicts and recusals;
  • accessibility and accommodation process;
  • appeal or correction route; and
  • data purpose, access, retention, and deletion.

Stop on a prohibited decision context or unnecessary private data.

2. Define the construct before criteria

State:

  • what quality or support is being examined;
  • excluded constructs;
  • intended interpretation;
  • contexts where the interpretation does not travel;
  • evidence requirements; and
  • known limitations.

Start with values and disciplinary context, not available metrics.

3. Adapt and validate the rubric

Begin with assets/rubric_template.json, then obtain qualified disciplinary, assessment-methods, stakeholder, accessibility, privacy, and fairness review.

The template deliberately records content validity as not_established. Do not change that status without documented evidence for the exact intended use.

Validate structure:

PYTHONDONTWRITEBYTECODE=1 python3 scripts/validate_rubric.py \
  --rubric assets/rubric_template.json

Read references/evaluation_framework.md for construct, anchor, validity, and rater guidance.

4. Build traceable evidence records

Reviewers may read an authorized work outside the scripts. Record only stable local locators and claim references in assets/evidencemanifesttemplate.json.

For every criterion, distinguish:

  • observed evidence from interpretation;
  • supporting from contrary evidence;
  • available from unavailable evidence;
  • missing from not_applicable; and
  • uncertainty from absence.

Failure to find prior work does not prove novelty.

5. Rate independently

Use assets/evaluation_template.json. Each criterion must be:

rationale reference;

  • rated with an anchor score, bounded uncertainty, evidence IDs, and a local
  • missing with null score/uncertainty and a rationale reference; or
  • not_applicable with null score/uncertainty and a rationale reference.

Do not encode missing or not-applicable as zero. Raters should train, calibrate, disclose conflicts, rate independently, and document disagreement.

6. Run local quality checks

Bounded scoring, without labels or recommendation:

PYTHONDONTWRITEBYTECODE=1 python3 scripts/calculate_scores.py \
  --rubric assets/rubric_template.json \
  --evaluation assets/evaluation_template.json

Evidence traceability:

PYTHONDONTWRITEBYTECODE=1 python3 scripts/check_traceability.py \
  --rubric assets/rubric_template.json \
  --evaluation assets/evaluation_template.json \
  --evidence assets/evidence_manifest_template.json

Inter-rater agreement:

PYTHONDONTWRITEBYTECODE=1 python3 scripts/summarize_agreement.py \
  --rubric assets/rubric_template.json \
  --ratings assets/ratings_template.csv

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