shap
by K-Dense-AIshap skill for model interpretability and explainable AI. Use it to understand predictions, compute feature attributions, choose SHAP plots, and debug model behavior for Data Analysis across tree, linear, deep learning, and black-box models.
This skill scores 78/100, which means it is a solid listing candidate for directory users: the repository gives enough real SHAP workflow guidance to justify installation, though it is not fully packaged for frictionless adoption. The skill is clearly aimed at explainability tasks and should help agents trigger and execute SHAP-related work with less guesswork than a generic prompt.
- Strong triggerability: the frontmatter and overview explicitly name SHAP, feature importance, prediction explanations, bias/fairness analysis, and multiple plot types.
- Substantial workflow content: the SKILL.md body is large, with many headings and workflow/constraint signals, suggesting more than a placeholder or demo.
- Good agent leverage: it covers multiple model families, so agents can apply the skill across tree, deep learning, linear, and black-box models.
- No install command or supporting files are present, so users may need to infer setup and usage details from the document alone.
- The repository appears to be documentation-only, so practical execution support may depend on the agent’s existing tooling and SHAP library knowledge.
Overview of shap skill
What shap does
The shap skill helps you explain model predictions with SHAP values, so you can see which inputs pushed a prediction up or down. It is best for users who need model interpretability, feature attribution, or an explainable AI workflow for real analysis rather than a generic “feature importance” summary.
When this skill is the right fit
Use the shap skill when you need to answer practical questions like: why did this prediction happen, which features matter most, is the model behaving fairly, or how do I present a reliable explanation to stakeholders. It fits tree models, linear models, deep learning models, and many black-box models.
What users usually care about most
Most people installing shap want fast path-to-output guidance: which explainer to choose, what data the explainer needs, and which plot best matches the question. The skill is valuable because it focuses on the explanation workflow, not just the library API.
How to Use shap skill
Install and locate the core instructions
Install the shap skill with the directory’s normal skill installation flow, then open scientific-skills/shap/SKILL.md first. If the package includes linked context in the future, check README.md, AGENTS.md, metadata.json, and any rules/, resources/, or references/ folders, but this repo currently centers the workflow in SKILL.md.
Turn a vague request into a usable prompt
The shap skill works best when your prompt includes the model type, the prediction task, the dataset slice to explain, and the analysis goal. For example, instead of “use shap on my model,” ask for: a SHAP explanation for a binary classifier, the top features for one prediction, a global summary for the validation set, and a waterfall plot for a selected row.
Provide the inputs SHAP actually needs
Strong shap usage usually depends on a background dataset, a specific prediction row or sample set, and the exact model object or prediction function. If you only provide the model name and no data context, the output will be less useful. Include feature names, preprocessing details, class labels, and any known constraints such as missing values or categorical encoding.
Read the workflow in the right order
Start with the overview and the “when to use” guidance, then move to the explainer-selection step and the plotting examples. For decision quality, pay attention to any instructions about matching explainer type to model family, because using the wrong explainer is the most common reason SHAP outputs become slow, noisy, or misleading.
shap skill FAQ
Is shap better than a normal prompt?
Usually yes, if you need a repeatable explainability workflow. A normal prompt can describe SHAP, but the shap skill gives more structured guidance on choosing the right explainer, preparing inputs, and reading the result correctly.
Is shap beginner-friendly?
It is beginner-friendly for basic inspection, especially feature importance and single-prediction explanations. It is less beginner-friendly if you want to interpret interactions, compare models, or debug preprocessing issues, because those tasks depend on good data setup.
When should I not use shap?
Do not use shap when you only need a simple model score or a vague “why is this happening” answer without access to the model and data. It is also not the best choice if your explanation must be extremely fast at large scale and you cannot afford local explanation overhead.
What should I check before installing shap?
Make sure your environment can run the model you want to explain and that you have representative background data. For shap for Data Analysis, the biggest blocker is usually not the library itself but incomplete input context.
How to Improve shap skill
Give it the right slice of the problem
The best shap results come from narrow, testable asks: one model, one task, one dataset slice, one explanation goal. If you ask for “all SHAP plots,” you usually get weaker output than if you request a beeswarm for global ranking plus a waterfall plot for one high-risk prediction.
Include the details that change the explanation
Mention model family, target type, feature preprocessing, and whether you want local or global interpretation. These details affect explainer choice and how SHAP values should be read. For example, tree-based models and neural networks often need different setup choices, and encoded features may need human-readable feature mapping.
Watch for the most common failure modes
The main failure modes in shap usage are mismatched background data, explaining transformed features without mapping them back, and using the wrong plot for the question. If the first result looks off, revise the prompt with the exact row index, class name, preprocessing pipeline, and the business question you want answered.
Iterate from explanation to decision
After the first output, ask for the next interpretation step: compare two samples, inspect interaction effects, or summarize the top drivers in plain language. That is the fastest way to turn shap from a visualization tool into a practical analysis workflow for model debugging and stakeholder reporting.