Intake Normalizer

Normalize site intake JSON and manifest fields for qualification analysis.

Updated Dec 7, 2025
One-click install
npx skills add https://github.com/ognjhunt/BlueprintCapturePipeline --skill intake-normalizer-ognjhunt
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: Intake Normalizer
Source: https://github.com/ognjhunt/BlueprintCapturePipeline/tree/main/.agents/skills/intake_normalizer
Command: npx skills add https://github.com/ognjhunt/BlueprintCapturePipeline --skill intake-normalizer-ognjhunt

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill ensures that all incoming qualification data is standardized and structured, preventing downstream failures due to inconsistent or missing information.

Core Features & Use Cases

  • Data Validation: Checks for mandatory fields and their quality, failing closed if critical information is missing.
  • Environment & Modality Normalization: Standardizes environment types and classifies capture modalities for appropriate downstream processing.
  • Use Case: Before a new capture is analyzed for robotic qualification, this Skill ensures the workflow, zone, and success_criteria are clearly defined and properly formatted, preventing the analysis skill from getting stuck on ambiguous inputs.

Quick Start

Use the Intake Normalizer skill to normalize the attached site intake JSON file.

Frequently Asked Questions about Intake Normalizer

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

FAQPage Schema
How do I standardize site intake JSON for qualification analysis?

To validate intake data, this Skill checks for mandatory fields and fails closed if critical information is missing, ensuring structured workflow and success criteria are clearly defined before downstream qualification processing.

How do I validate mandatory fields in a capture package manifest file?

To validate intake data, this Skill checks for mandatory fields and fails closed if critical information is missing, ensuring structured workflow and success criteria are clearly defined before downstream qualification processing.

What is the best way to normalize environment types and capture modalities before processing?

Normalizing environment types and capture modalities is handled by standardizing classification data within your site intake JSON, ensuring appropriate downstream processing and preventing ambiguous inputs in qualification tasks.

Why does my qualification analysis get stuck on ambiguous intake inputs?

Qualification analysis gets stuck when incoming data lacks consistent structure, which is why normalizing structured workflow and success criteria from intake JSON prevents downstream failures due to missing information.

Can I use this to flag known blockers in site intake JSON?

Yes, you can use this Skill to flag known blockers during intake normalization, validating hard-required and soft-required fields to ensure complete data readiness before qualification analysis begins.