shap
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
Explain and audit machine-learning predictions with SHAP. Use for selecting SHAP explainers and maskers, computing and validating feature attributions, handling multi-output explanations, and producing local or global SHAP visualizations.
- Package
- 10 files, 137 KB
- SKILL.md
- 1933 words, 12+ sections
- Code
- Python (1)
- Allowed tools
ReadBash- License
- MIT
- Compatibility
- Requires Python 3.12+ and uv for SHAP 0.52.0; model-specific libraries are optional.
SKILL.md covers: SHAP · Operating Rules · Install · Standard Workflow · 1. Define the explanation target · 2. Select an explainer and masker · 3. Compute a modern `Explanation` · sklearn tree classifiers expose one output per class. · 4. Control tree output semantics when needed · 5. Use a model-agnostic callable deliberately · 0.52 selector dispatch may drop output_names for permutation. · 6. Visualize the question, not merely the available plot
Findings
No findings. SAFE
What is in the package
| File | Kind | Size |
|---|---|---|
SKILL.md | doc | 16 KB |
references/data-maskers.md | doc | 12 KB |
references/explainers.md | doc | 18 KB |
references/migration.md | doc | 9.8 KB |
references/modalities.md | doc | 12 KB |
references/plots.md | doc | 12 KB |
references/theory.md | doc | 13 KB |
references/troubleshooting.md | doc | 14 KB |
references/workflows.md | doc | 19 KB |
scripts/tabular_report.py | script | 11 KB |
External hosts referenced
shap.readthedocs.io github.com arxiv.org doi.org export.arxiv.org huggingface.co proceedings.mlr.press proceedings.neurips.cc pypi.org scikit-learn.org www.nature.com xgboost.readthedocs.io
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-shap-scan.json
- Badge for your README:
- Request a re-scan