ce-pipeline-builder

Name: ce-pipeline-builder
Availability: InStock
Author: moffran

Orchestrate CE-first pipelines by wrapping models with WrapCalibratedExplainer.

78|15|Updated May 1, 2023

Search Tags:#calibration #explanation #policy #calibrated-explanations #wrap-calibrated-explainer #pipeline-builder #guarded-explanation

Ready to run

npx skills add https://github.com/Moffran/calibrated_explanations --skill ce-pipeline-builder

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: ce-pipeline-builder
Source: https://github.com/Moffran/calibrated_explanations/tree/main/.claude/skills/ce-pipeline-builder
Command: npx skills add https://github.com/Moffran/calibrated_explanations --skill ce-pipeline-builder

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Build CE-first end-to-end pipelines by wrapping models with WrapCalibratedExplainer and sequencing fit, calibrate, and explain or predict steps.

Core Features & Use Cases

Enforces the mandatory CE-first checklist (library presence, wrapper usage, fitting, calibration, and guarded explanation paths) to ensure safe and calibrated outputs.
Provides a minimal working skeleton for binary, multiclass, and regression pipelines, plus validated utilities from ce_agent_utils for end-to-end automation.
Includes a reference policy in references/ce-first-policy.md to guide correct behavior in agent workflows.

Quick Start

Wrap your model with WrapCalibratedExplainer, fit on proper data, calibrate with separate data, then explain factual instances.

Frequently Asked Questions about ce-pipeline-builder

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I build a calibrated explanation pipeline for interpretable AI?▼

Build a calibrated explanation pipeline by wrapping your model with WrapCalibratedExplainer to enforce strict fit, calibrate, and explain or predict sequencing. This orchestrates end-to-end CE-first workflows for generating safe, interpretable outputs.

What is the CE-first checklist for generating guarded explanations?▼

The CE-first checklist validates library presence, wrapper usage, model fitting, calibration, and guarded explanation paths. It enforces correct behavior in agent workflows to ensure safe, calibrated outputs across standard and guarded inference paths.

Can I use WrapCalibratedExplainer for multiclass and regression pipelines?▼

Yes, WrapCalibratedExplainer supports binary, multiclass, and regression pipelines. The pipeline builder provides minimal working skeletons and validated utilities from ce_agent_utils to automate these end-to-end workflows.

How do I generate narrative explanations alongside calibrated predictions?▼

Generate narrative explanations by following the mandatory CE-first sequencing of fitting and calibrating your wrapped model. The pipeline integrates with ce_agent_utils to automate narrative generation across standard and guarded inference paths.

What are the limitations of using a CE-first pipeline builder?▼

The CE-first pipeline builder requires strict adherence to the fit, calibrate, and explain sequencing. It mandates passing separate data for fitting and calibration, and relies on the WrapCalibratedExplainer wrapper, limiting flexibility for non-standard workflows.