lime-explainer
LIME-based local explanation skill for individual predictions across tabular, text, and image data.
npx skills add a5c-ai/babysitter --skill lime-explainer --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# lime-explainer ## Overview LIME-based local explanation skill for individual predictions across tabular, text, and image data using Local Interpretable Model-agnostic Explanations. ## Capabilities - Tabular data explanations - Text classification explanations - Image classification explanations - Submodular pick for representative samples - Custom distance metrics - Kernel width tuning - Feature discretization - Local surrogate model analysis ## Target Processes - Model Interpretability and Explainability Analysis - Model Evaluation and Validation Framework ## Tools and Libraries - LIME - scikit-learn - numpy - PIL/Pillow (for images) ## Input Schema ```json { "type": "object", "required": ["modelPath", "dataType", "instancePath"], "properties": { "modelPath": { "type": "string", "description": "Path to the trained model or prediction function" }, "dataType": { "type": "string", "enum": ["tabular", "text", "image"], "description": "Type of data to explain" }, "instancePath": { "type": "string", "description": "Path to instance(s) to explain" }, "tabularConfig": { "type": "object", "properties": { "trainingDataPath": { "type": "string" }, "featureNames": { "type": "array", "items"
- Overview
- Capabilities
- Target Processes
- Tools and Libraries
- Input Schema
- Output Schema
- Usage Example
What does the lime-explainer skill do?
LIME-based local explanation skill for individual predictions across tabular, text, and image data.
How do I install it?
Run `npx skills add a5c-ai/babysitter --skill lime-explainer --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From a5c-ai/babysitter, a repository with 1,642 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.