Evidence Auditor

Audit capture artifacts and classify evidence grades against raw data.

Updated Dec 7, 2025

Search Tags:#data integrity #data verification #qualification #evidence audit #capture quality #geometry validation

Ready to run

npx skills add https://github.com/ognjhunt/BlueprintCapturePipeline --skill evidence-auditor-ognjhunt

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: Evidence Auditor
Source: https://github.com/ognjhunt/BlueprintCapturePipeline/tree/main/.agents/skills/evidence_auditor
Command: npx skills add https://github.com/ognjhunt/BlueprintCapturePipeline --skill evidence-auditor-ognjhunt

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill ensures that all claims made about capture evidence are rigorously verified against the actual data, preventing unsupported assertions and ensuring data integrity.

Core Features & Use Cases

Evidence Verification: Audits specific fields within various artifacts like capture_qa_scorecard.json, geometry_evidence.json, and scene_graph.json.
Grade Classification: Assigns an evidence grade (metric, estimated, inferred, absent) to each artifact based on its quality and source.
Gap Identification: Pinpoints missing evidence, unsupported claims, and hidden zones that impact qualification.
Use Case: Before a blueprint capture is approved, this Skill checks if the claimed route width is supported by high-confidence metric measurements in the geometry evidence and if the capture quality meets minimum registration rates.

Quick Start

Use the Evidence Auditor skill to verify the geometry evidence for the capture.

Frequently Asked Questions about Evidence Auditor

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

FAQPage Schema

How do I verify capture evidence against claims in geometry validation?▼

Capture evidence verification audits geometry, QA scorecards, scene graphs, and route graphs against raw data to identify unsupported assertions. This process enforces strict validation rules to ensure data integrity and accurate qualification decisions.

What is evidence grading for capture artifacts and how does it work?▼

Evidence grading classifies capture artifacts into metric, estimated, inferred, or absent categories based on quality and source. Grading analyzes fields within geometry evidence, QA scorecards, and scene graphs to enforce strict validation rules for qualification decisions.

How do I identify gaps and low confidence measurements in capture QA scorecards?▼

Gap identification pinpoints missing evidence, unsupported claims, and hidden zones by analyzing capture QA scorecards and geometry evidence. This data verification process highlights low confidence measurements that impact blueprint capture qualification and prescreening outcomes.

Can I use evidence auditing to check route width claims against geometry evidence?▼

Evidence auditing checks if claimed route width is supported by high-confidence metric measurements in geometry evidence. It verifies capture quality meets minimum registration rates before a blueprint capture is approved for qualification.

What are the limitations of data verification for hidden zones in scene graphs?▼

Data verification limitations arise when scene graphs contain hidden zones that impact qualification. Evidence auditing identifies these gaps but strict validation rules may flag unsupported assertions as absent, preventing prescreening approval until raw data supports the claims.