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.