ground-truth

Create and refine ground truth labels for evaluation datasets with documented reasoning.

3|4|Updated Mar 11, 2026
One-click install
npx skills add https://github.com/nicsuzor/academicOps --skill ground-truth
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: ground-truth
Source: https://github.com/nicsuzor/academicOps/tree/main/archived/skills/ground-truth
Command: npx skills add https://github.com/nicsuzor/academicOps --skill ground-truth

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill ensures that evaluation datasets have accurate, consistent, and defensible ground truth labels by deriving them from authoritative guidelines rather than intuition.

Core Features & Use Cases

  • Guideline-Driven Labeling: Enforces labeling based on explicit rules, not subjective judgment.
  • Reproducible Reasoning: Documents the exact guideline provisions and rationale used for each label.
  • Ambiguity Management: Provides a structured way to identify and document unclear or conflicting guidelines.
  • Use Case: When training a content moderation AI, use this Skill to ensure all flagged content is labeled according to a predefined policy, minimizing bias and improving model fairness.

Quick Start

Use the ground-truth skill to establish labels for the dataset in the 'evaluation-data/' directory.

Frequently Asked Questions about ground-truth

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

FAQPage Schema
How do I create ground truth labels for an evaluation dataset?

Ground truth labeling enforces explicit rules rather than subjective judgment, ensuring consistency and auditability. It documents exact guideline provisions and rationale used for each label to minimize bias.

How do I document reasoning for data labeling decisions?

Document labeling reasoning by recording the exact guideline provisions and primary rationale used for each label. This Skill structures that documentation to ensure decisions are reproducible and defensible.

What is the best way to ensure consistency across evaluation datasets?

Ensuring consistency requires guideline-driven labeling. This Skill enforces explicit criteria and structured documentation of reasons for each label, preventing subjective bias and improving model fairness.

How do I handle ambiguity when labeling data for AI training?

Handling ambiguity requires a structured way to identify and document unclear or conflicting guidelines. This Skill provides that structure, allowing you to manage ambiguous cases systematically.

Can I use this for reviewing and updating existing ground truth labels?

Yes, you can use this Skill to review or update existing ground truth labels. It applies the same guideline-driven criteria and structured documentation to refine labels in judgment or reasoning tasks.