skills.rest
omosb1-sys
omosb1-sysCOMMUNITY
Explore all skills by this author →
GitHub

precision-extractor

Convert unstructured text into structured JSON/TOON assets with entity resolution.

Updated Jan 14, 2026
Search Tags:#JSON#entity-resolution#precision-extraction#unstructured-data#confidence-tagging#data-asset
One-click install
npx skills add https://github.com/omosb1-sys/epl-data-pipeline --skill precision-extractor
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: precision-extractor
Source: https://github.com/omosb1-sys/epl-data-pipeline/tree/main/epl_project/.agent/skills/precision-extractor
Command: npx skills add https://github.com/omosb1-sys/epl-data-pipeline --skill precision-extractor

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill converts unstructured data such as news articles and reviews into structured JSON/TOON assets to enable search, analysis, and archival workflows.

Core Features & Use Cases

  • Structured extraction: Transform free-form text into JSON/TOON schemas with entity-level representations and confidence scores.
  • Entity resolution: Map key entities (persons, organizations, events) to stable IDs to ensure database consistency.
  • Asset-ready output: Produce machine-actionable assets ready for downstream pipelines (fixtures, transfers, etc).

Quick Start

Run the precision-extractor on a text corpus to generate structured assets in JSON format.

Frequently Asked Questions about precision-extractor

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

FAQPage Schema
How do I convert unstructured text into structured JSON assets?▼

Converting unstructured text into structured JSON assets involves parsing long-form content like news articles and reviews, applying entity resolution and confidence tagging to output machine-actionable JSON/TOON compliant assets.

What is entity resolution for unstructured data extraction?▼

Entity resolution in unstructured data extraction maps key entities like persons, organizations, and events to stable IDs, ensuring database consistency across your structured JSON/TOON outputs.

How do I extract entities from news articles with confidence tagging?▼

Extracting entities from news articles with confidence tagging transforms free-form text into JSON/TOON schemas, generating entity-level representations complete with specific confidence scores for downstream pipelines.

Does this unstructured data extraction tool work for long-form reviews?▼

Yes, unstructured data extraction works for long-form reviews. The Skill processes news articles and reviews, transforming them into searchable data stores with precise entity mapping and asset-ready outputs.

What is the best way to prepare unstructured data for downstream pipelines?▼

The best way to prepare unstructured data for downstream pipelines is converting it into JSON/TOON compliant assets, providing machine-actionable formats with stable entity IDs and confidence tagging for seamless integration.

When do I need JSON TOON compliant assets for data extraction?▼

You need JSON TOON compliant assets for data extraction when downstream workflows require searchable data stores with precise entity resolution and confidence tagging to maintain database consistency.

Related Skills

Explore More Data & Analytics Skills→
lucifertrjlucifertrj
17

nltk-nlp-text-cleaning

Clean, tokenize, and normalize English text with NLTK for NLP pipelines.

Community
Advanced
HuaGCSHuaGCS

memory

Store stable facts in memory and isolate transient data for retrieval.

Community
Advanced
yarangyarang

moai-formats-data

Encode, validate, and convert data between JSON, YAML, and TOON formats.

Community
Advanced
himanshu231204himanshu231204
2

regex-vs-llm-structured-text

Route low-confidence regex extractions to LLM validation for structured text parsing.

Community
Advanced