benchmark-validation

Name: benchmark-validation
Availability: InStock
Author: concordance-co

Assess benchmark accessibility, runnability, and label sufficiency for interpretability analysis.

4|1|Updated May 20, 2026

Search Tags:#validation #benchmark #labels #data-quality #interpretability

Ready to run

npx skills add https://github.com/concordance-co/xenon --skill benchmark-validation

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill helps teams quickly decide whether a benchmark is worth deeper mechanistic interpretation work by evaluating accessibility, data and label availability, licensing, and practical viability.

Core Features & Use Cases

Benchmark screening: Assess public availability, licensing, runnable harnesses, and label richness to filter weak ideas.
Product relevance and risk assessment: Estimate how well a benchmark maps to plausible mechanistic questions and meaningful product implications.
Decision artifacts: Produce a validation memo and a compact summary to guide next steps.

Quick Start

Run the validation workflow to generate a memo and summary for a benchmark to decide whether to proceed.

Frequently Asked Questions about benchmark-validation

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

FAQPage Schema

How do I validate a benchmark for mechanistic interpretability analysis?▼

To validate a benchmark for mechanistic interpretability, assess public data accessibility, clear licensing, runnable harnesses, and label richness to determine viability for deeper work. This process outputs a human-readable validation memo and structured summary.

What makes a benchmark suitable for mechanistic interpretability work?▼

A benchmark is suitable for mechanistic interpretability work when it offers public data, clear licensing, scalable evaluation, and sufficiently rich labels. The assessment estimates how well the benchmark maps to plausible mechanistic questions and meaningful product implications.

How do I screen public benchmarks to see if they are runnable and accessible?▼

Screen public benchmarks by evaluating their public availability, licensing terms, and runnable harnesses. Apply this assessment to filter weak ideas and determine if the benchmark is accessible and sufficiently labeled for scalable evaluation.

Does benchmark validation require specific licensing and label availability?▼

Yes, benchmark validation requires clear licensing and sufficient label availability. The process specifically evaluates data and label richness alongside public accessibility to decide whether a benchmark is worth deeper mechanistic interpretation work.

What outputs do I get from a benchmark screening process?▼

Benchmark screening produces decision artifacts including a human-readable validation memo and a compact structured summary. These outputs are aligned with methodology guidance to help teams decide whether to proceed with deeper analysis.

When should I not use a benchmark for deeper mechanistic work?▼

You should not use a benchmark for deeper mechanistic work if it lacks public data, clear licensing, scalable evaluation, or sufficient labels. The validation process filters these weak ideas to prevent investing in non-viable benchmarks.