scholar-evaluation
No security findings. Scanned 1 time, most recently 2026-10-21.
Second scanner: NVIDIA SkillSpector
Independent static run, no language model, over the same files as the Cisco scan above. How the two scanners compare across the whole corpus: Two scanners, one corpus.
What this skill does
Provides 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.
- Package
- 19 files, 165 KB
- SKILL.md
- 1436 words, 12+ sections
- Code
- Python (8)
- Allowed tools
ReadWriteBashGlobPython- License
- MIT
- Compatibility
- Requires Python 3.11+ for optional bundled standard-library CLIs. All tooling is local JSON/CSV processing with no network, credentials, external models, or subprocesses.
SKILL.md covers: Scholar Evaluation · Purpose · Hard safety boundary · ScholarEval status · Metric and prestige policy · Data boundary · Workflow · 1. Confirm allowed use and authorization · 2. Define the construct before criteria · 3. Adapt and validate the rubric · 4. Build traceable evidence records · 5. Rate independently
Findings
No findings. SAFE
What is in the package
| File | Kind | Size |
|---|---|---|
SKILL.md | doc | 12 KB |
assets/evaluation_template.json | config | 1.3 KB |
assets/evidence_manifest_template.json | config | 1.8 KB |
assets/process_checklist_template.json | config | 2.1 KB |
assets/ratings_template.csv | data | 1.7 KB |
assets/rubric_template.json | config | 12 KB |
references/evaluation_framework.md | doc | 10 KB |
references/local_tooling.md | doc | 9.1 KB |
references/responsible_assessment.md | doc | 9.6 KB |
references/security_validation.md | doc | 4.2 KB |
references/source_ledger.md | doc | 14 KB |
scripts/_common.py | script | 36 KB |
scripts/calculate_scores.py | script | 1.8 KB |
scripts/check_process.py | script | 8.1 KB |
scripts/check_traceability.py | script | 8.9 KB |
scripts/generate_report_scaffold.py | script | 9.4 KB |
scripts/summarize_agreement.py | script | 11 KB |
scripts/validate_rubric.py | script | 1.7 KB |
scripts/weight_sensitivity.py | script | 10 KB |
External hosts referenced
arxiv.org doi.org sfdora.org www.ukri.org ico.org.uk inorms.net www.crossref.org credit.niso.org export.arxiv.org github.com ncme.org openreview.net rori.figshare.com unesdoc.unesco.org www.coara.org www.leidenmanifesto.org www.testingstandards.net www.unesco.org www.wcrif.org
Hostnames that appear in the skill's files. Referencing a host is not a finding; compare against the findings above and the skill's stated purpose.
Package contents recorded 2026-10-22 at commit 593377bb0487. The scan above ran against the version current at scan time; the two can differ if the skill has since been updated.
What we checked
- Static analyzer. Pattern rules over SKILL.md and bundled files: known-bad commands, exfiltration URLs, secrets, obfuscation.
- Bytecode analyzer.
- Pipeline analyzer.
- Correlation analyzer.
- Behavioral analyzer. Checks what the skill asks the agent to do against what its description claims it does.
- Trigger analyzer. Looks for instructions that fire on conditions the user did not ask for.
What we did not check
- Runtime behaviour. The skill was not executed; findings come from reading its files.
- Anything fetched at install or run time, including remote scripts, package dependencies, and URLs the skill tells the agent to open.
- Content the analyzers cannot read: images, binaries, and some non-English text.
- Known scanner bypass classes. See the methodology and limitations page.
Sources
- Repository: https://github.com/K-Dense-AI/scientific-agent-skills
- Raw scan result: skills-scholar-evaluation-scan.json
- Badge for your README:
- Request a re-scan