Information Processing

Convert raw inputs into structured, traceable claims with source evidence.

Updated Apr 22, 2026
One-click install
npx skills add https://github.com/Trong-Tra/agent-skills --skill information-processing
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: Information Processing
Source: https://github.com/Trong-Tra/agent-skills/tree/main/researcher/information-processing
Command: npx skills add https://github.com/Trong-Tra/agent-skills --skill information-processing

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Converts messy, conflicting, or incomplete inputs into structured, verifiable knowledge that can be reviewed and trusted.

Core Features & Use Cases

  • Ingests diverse sources (papers, notes, conversations) and catalogs claims with IDs for traceability.
  • Deconstructs information into atomic claims and cross-references them to surface inconsistencies.
  • Generates traceable outputs with sources and evidence, enabling auditability, revision, and knowledge management workflows.

Quick Start

Ingest a sample document and run the processing pipeline to produce a traceable claim set.

Frequently Asked Questions about Information Processing

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

FAQPage Schema
How do I convert messy inputs into structured knowledge with traceable sources?

To convert messy inputs into structured knowledge, this Skill ingests raw data, deconstructs it into atomic claims, cross-references evidence, and synthesizes verifiable outputs with traceable source IDs.

What is the best way to decompose research notes into atomic claims for verification?

The best way to decompose research notes into atomic claims is through a processing pipeline that triages ingested sources, extracts discrete assertions, and cross-references them to surface inconsistencies.

Can I use this information processing pipeline for journalism and research tasks?

Yes, you can use this information processing pipeline for journalism and research tasks, as it is designed to weigh conflicting evidence and generate publicly auditable results across multiple sources.

How do I verify conflicting data sources and generate auditable results?

To verify conflicting data sources, the pipeline catalogs claims with unique IDs, cross-references atomic assertions, weighs the gathered evidence, and synthesizes an output that enables full auditability.

Does this claim decomposition approach work with unstructured conversations and papers?

Yes, this claim decomposition approach works with unstructured conversations and papers, ingesting diverse source formats to catalog and triage information before extracting atomic claims for knowledge management.