benchmark-to-latent-labels

Name: benchmark-to-latent-labels
Availability: InStock
Author: concordance-co

Convert benchmark native labels into a latent-label specification for mechanistic interpretability.

4|1|Updated May 20, 2026

Search Tags:#ontology #audit #benchmark #interpretability #confounds #latent-labels #labeling-functions

Ready to run

npx skills add https://github.com/concordance-co/xenon --skill benchmark-to-latent-labels

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: benchmark-to-latent-labels
Source: https://github.com/concordance-co/xenon/tree/main/.agents/skills/benchmark-to-latent-labels
Command: npx skills add https://github.com/concordance-co/xenon --skill benchmark-to-latent-labels

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill helps convert benchmark labels into a machine-actionable latent-label specification for mechanistic interpretability, enabling downstream labeling, confound planning, and ontology freezing.

Core Features & Use Cases

Separate prompt-side structure from response-side behavior.
Classify labels into direct vs derived targets, validation signals, or nuisance variables.
Build a benchmark-specific latent-label ontology and freeze a first-pass schema.

Quick Start

Provide the benchmark's native labels and metadata to generate a latent-label spec, labeling functions, and a confound plan.

Frequently Asked Questions about benchmark-to-latent-labels

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

FAQPage Schema

How do I convert benchmark labels into latent labels for interpretability?▼

Latent labels separate prompt-side structure from response-side behavior by classifying benchmark labels into direct targets, derived targets, validation signals, or nuisance variables. This process creates a machine-actionable specification for mechanistic interpretability and confound planning.

What's the best way to build a labeling ontology from benchmark metadata?▼

Building a labeling ontology from benchmark metadata requires classifying native labels into direct versus derived targets, then freezing a first-pass schema. This creates a structured inventory separating prompt-side structure from response-side behavior for downstream interpretability tasks.

How do I create labeling functions for a mechanistic interpretability benchmark?▼

Creating labeling functions for a mechanistic interpretability benchmark involves converting the native labels into a latent-label specification. You classify variables, build a benchmark-specific ontology, and generate corresponding functions alongside a confound audit gap list.

Does this approach to latent-label generation support confound auditing?▼

Yes, latent-label generation supports confound auditing by classifying benchmark labels into validation signals and nuisance variables. It produces a confound plan and gap list during ingestion to identify potential interpretability confounds.

When do I need to freeze a labeling ontology schema for benchmark ingestion?▼

You need to freeze a labeling ontology schema during benchmark ingestion when preparing for mechanistic interpretability. Freezing the first-pass schema stabilizes the latent-label inventory, ensuring downstream labeling functions and confound audits operate on consistent targets.