shap
Explain Models with SHAP
Machine learning teams need clear reasons for model predictions and feature influence. This skill provides SHAP workflows, explainer selection, plots, and interpretation guidance for common model types.
Install with my Agent
Copy this request to your Agent. It includes the canonical Skill page and manifest.
Review the Skillstore skill "shap" from https://skillstore.io/skills/davila7-shap.md and its manifest at https://skillstore.io/api/skills/davila7-shap/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.
Test it
Using "shap". A data scientist asks why a fraud model flagged one transaction.
Expected outcome:
The response recommends a local waterfall plot, explains the baseline prediction, and ranks the features that increased or reduced risk.
Using "shap". A team sees an unexpected feature at the top of global importance.
Expected outcome:
The response outlines checks for leakage, cohort differences, feature correlations, and scatter plots for relationship validation.
Using "shap". An engineer wants SHAP explanations in an API.
Expected outcome:
The response proposes saved models and explainers, batched explanation calls, cached results, top-feature summaries, and latency monitoring.
Security Audit
SafeNo evidence of prompt injection, data exfiltration, or unauthorized command execution was found. The static command findings are Markdown backticks, fenced SHAP examples, and one public package installation example. The URL findings are documentation links to SHAP resources, not network request code.
Risk Factors
⚙️ External commands (63)
🌐 Network access (2)
Share & cite this report
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https://skillstore.io/skills/davila7-shap/audits/10?utm_source=security_passport&utm_medium=share&utm_campaign=versioned_reportMarkdown badge
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<a href="https://skillstore.io/skills/davila7-shap?utm_source=security_passport_badge"><img src="https://skillstore.io/badges/skills/davila7-shap/security.svg" alt="Skillstore security assessment" loading="lazy"></a>Embed card
<iframe src="https://skillstore.io/embed/skills/davila7-shap.html" title="Skillstore Security Assessment" sandbox="allow-popups allow-popups-to-escape-sandbox" loading="lazy" referrerpolicy="no-referrer" width="420" height="180"></iframe>Academic citations (APA · BibTeX · CFF)
APA citation
davila7. (2026). shap security audit report (audit version 10) [Author version unspecified]. Skillstore. https://skillstore.io/skills/davila7-shap/audits/10BibTeX citation
@techreport{davila7-davila7-shap-2026,
author = {davila7},
title = {shap security audit report (audit version 10)},
institution = {Skillstore},
year = {2026},
number = {10},
url = {https://skillstore.io/skills/davila7-shap/audits/10},
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: "shap security audit report (audit version 10)"
version: "unspecified"
type: report
authors:
- name: "davila7"
date-released: "2026-07-06"
url: "https://skillstore.io/skills/davila7-shap/audits/10"
identifiers:
- type: other
value: "skillstore:davila7-shap:audit:10"
description: "Skillstore immutable audit report identifier"
Compare variants
2 installable variantsEach author remains a separate installable skill. The recommended variant is ranked by Skillstore evidence.
Why this variant is first
davila7-shap
2026-08-21
k-dense-ai-shap
2026-08-21
Skillstore Score
Why this score Evidence Confidence: HighWhat You Can Build
Explain a Production Prediction
Create local SHAP explanations that show which features moved one prediction away from the baseline.
Audit Feature Importance
Use global plots and cohort comparisons to find dominant features, leakage signals, or unexpected model behavior.
Review Fairness Signals
Compare SHAP values across groups and inspect protected attributes or proxy features during model review.
Try These Prompts
I have a [model type] model and a tabular dataset. Help me choose the right SHAP explainer and explain why.
Guide me through computing SHAP values for my trained model and choosing beeswarm, bar, waterfall, and scatter plots.
Help me use SHAP to debug misclassified samples, inspect suspicious feature importance, and validate feature relationships.
Design a production SHAP workflow with cached explainers, batched computation, top-feature output, monitoring, and versioning.
Best Practices
- Choose specialized explainers before model-agnostic explainers when the model type is known.
- Start with global plots, then inspect individual predictions and key feature relationships.
- Validate SHAP patterns with domain knowledge and check output units before drawing conclusions.
Avoid
- Using KernelExplainer for tree models when TreeExplainer is available.
- Treating SHAP values as causal evidence without additional analysis.
- Explaining huge datasets without sampling, batching, or limiting visual complexity.
Frequently Asked Questions
What models does this skill support?
Can it create SHAP plots?
Does it compute explanations automatically?
Can it help with fairness analysis?
How should users handle slow SHAP computations?
Does SHAP prove causation?
Developer Details
Author
davila7License
MIT
Skillstore revision
r1
Version notice
The author did not declare a version.
Ref
b959ebfd1043a07c0c4378ab94ca4342cb0259f5
Maintenance freshness
7/20/2026
Usage
6 downloads · 499 views
File structure