assay

Automate viability scoring for Discovery Harness candidates using a structured rubric.

1|Updated Dec 13, 2025

Search Tags:#data analytics #scoring rubric #candidate assessment #viability scoring #Discovery Harness

Ready to run

npx skills add https://github.com/stephendor/TDL --skill assay-stephendor

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: assay
Source: https://github.com/stephendor/TDL/tree/main/.agents/skills/assay
Command: npx skills add https://github.com/stephendor/TDL --skill assay-stephendor

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires validate_assay_scorecard, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill provides scalability and efficiency in assessing the viability of candidates within the Discovery Harness, reducing the time and effort spent on manual evaluation.

Core Features & Use Cases

Scalable Viability Scoring: Automatically evaluate the feasibility and novelty of candidates.
Customizable Evaluation Criteria: Apply a rubric that includes topology, data feasibility, and novelty to inform decisions.
Integration with Workflow: Seamlessly integrate into the Discovery Harness process, enhancing productivity and accuracy.

Quick Start

Run the 'assay' skill on the candidate with ID 'arXiv:2606.11911' to evaluate its viability.

Frequently Asked Questions about assay

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

FAQPage Schema

How do I automate viability scoring for candidates in the Discovery Harness?▼

Viability scoring for Discovery Harness candidates is automated by running the assay skill on a specific candidate ID, which evaluates project feasibility, data quality, and novelty using a structured scoring rubric.

What evaluation criteria are used for assessing project feasibility and novelty?▼

Project feasibility and novelty are assessed using a customizable scoring rubric that evaluates candidate topology, data feasibility, and novelty to inform decisions within the Discovery Harness framework.

Do I need Python-based scorecard validation tools to run candidate assessments?▼

Yes, Python-based scorecard validation tools are required to run candidate assessments, as the assay skill explicitly requires the validate_assay_scorecard dependency to function correctly.

What is the best way to scale up candidate evaluation in the Discovery Harness?▼

The best way to scale up candidate evaluation in the Discovery Harness is using automated viability scoring, which reduces the time and effort spent on manual evaluation of multiple candidates.

Can I customize the scoring rubric for data feasibility and topology evaluations?▼

Yes, the scoring rubric for data feasibility and topology evaluations can be customized, as the assay skill supports applying a rubric tailored to your specific candidate assessment criteria.

Why does my assay skill fail to evaluate candidate viability?▼

Your assay skill may fail to evaluate candidate viability if the required validate_assay_scorecard dependency is missing or if the Python-based scorecard validation tools are not properly configured.