stk-data-annotations

Name: stk-data-annotations
Availability: InStock
Author: SALK

Annotate survey meta JSON files and validate YAML frontmatter.

1|Updated Nov 4, 2023

Search Tags:#alignment #annotation #data-validation #survey-data #json-meta #read_and_process_data #infer_meta

Ready to run

npx skills add https://github.com/salk-ee/salk_toolkit --skill stk-data-annotations

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: stk-data-annotations
Source: https://github.com/salk-ee/salk_toolkit/tree/main/.cursor/skills/stk-data-annotations
Command: npx skills add https://github.com/salk-ee/salk_toolkit --skill stk-data-annotations

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

STK Data Meta Annotations streamline the creation, validation, and alignment of JSON-based data meta annotations for survey datasets, ensuring provenance and consistency across raw data and processed outputs.

Core Features & Use Cases

Create and validate DataMeta schemas for *_meta.json files used by salk_toolkit io and validation modules.
Align annotations across waves or survey modes (e.g., CATI, WEB) and load them with read_and_process_data.
Audit category mappings, translations, and top-k / maxdiff configurations to prevent semantic drift in analyses.

Quick Start

Load an existing annotation with read_annotated_data and validate it to identify category mappings and transform adjustments.

Frequently Asked Questions about stk-data-annotations

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

FAQPage Schema

How do I align and validate survey data meta JSON files?▼

To align and validate survey data meta JSON files, apply bootstrapping and validation workflows to standardize category mappings and processing rules, ensuring YAML frontmatter drives discovery and prevents semantic drift in analyses.

What is category mapping alignment for survey waves?▼

Category mapping alignment for survey waves is the process of auditing annotations across different survey modes like CATI and WEB to standardize mappings and prevent semantic drift during data analysis.

How do I audit category mappings and top-k maxdiff configurations in JSON metadata?▼

Audit category mappings and top-k maxdiff configurations in JSON metadata by applying validation workflows that check annotations for semantic drift and ensure transform adjustments align across multiple surveys and waves.

Can I use read_and_process_data to load aligned annotations from multiple survey modes?▼

Yes, you can use read_and_process_data to load aligned annotations from multiple survey modes, applying bootstrapping and validation workflows to standardize category mappings and processing rules across datasets.

Do I need YAML frontmatter to define name and description for survey data annotations?▼

Yes, you need YAML frontmatter to define name and description for survey data annotations, as the frontmatter drives discovery and validation workflows while ensuring provenance and consistency across raw data and processed outputs.

What's the best way to prevent semantic drift in survey data annotations?▼

The best way to prevent semantic drift in survey data annotations is to audit category mappings, translations, and top-k maxdiff configurations using validation workflows that align annotations across multiple surveys and waves.