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data-research

Automate structured data collection from emails, web sources, and APIs.

Updated Jun 20, 2026
Search Tags:#structured data#data extraction#research#deduplication#tracking#email parsing
One-click install
npx skills add https://github.com/Sigmacodeat/subsumio-web --skill data-research-sigmacodeat
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-research
Source: https://github.com/Sigmacodeat/subsumio-web/tree/main/server/skills/data-research
Command: npx skills add https://github.com/Sigmacodeat/subsumio-web --skill data-research-sigmacodeat

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill solves the challenge of manually collecting, organizing, and maintaining structured information from emails, web sources, and APIs by providing a repeatable research pipeline.

Core Features & Use Cases

  • Structured Data Research: Searches sources, extracts relevant fields, archives raw materials, and builds canonical tracker pages.
  • Deduplication and Validation: Prevents duplicate records and preserves source links to maintain reliable datasets.
  • Use Case: Track investor updates, company metrics, donations, expenses, or other recurring information streams by converting unstructured communications into organized knowledge.

Quick Start

Use the data-research skill to build a tracker from my latest investor update emails and extract key company metrics.

Frequently Asked Questions about data-research

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

FAQPage Schema
How do I extract structured data from emails and web sources automatically?▼

To extract structured data from emails and web sources, you need a configurable research pipeline that applies extraction schemas, archives raw materials, and updates a canonical tracker with linked evidence.

What is the best way to track recurring investor updates from unstructured communications?▼

Tracking recurring investor updates requires parsing unstructured communications, applying deduplication logic to prevent duplicate records, and organizing key metrics into a structured tracker.

Can I build a recurring information pipeline for donations and expenses without manual data entry?▼

Yes, building a recurring information pipeline for donations and expenses involves automating data collection from APIs and emails, validating fields, and maintaining a canonical dataset with preserved source links.

How do deduplication logic and source archiving work in a structured data research pipeline?▼

Deduplication logic prevents duplicate records by validating extracted fields against existing entries, while source archiving preserves original raw materials and links them as evidence to maintain reliable datasets.

Does this structured data extraction approach require predefined schemas for tracking company metrics?▼

Yes, structured data extraction for tracking company metrics requires configurable extraction schemas to define specific fields to pull, ensuring the canonical tracker updates accurately with relevant insights.

What are the limitations of automating data collection from APIs and web sources for research workflows?▼

Limitations of automating research workflows include handling inconsistent source formats, managing API rate limits, and ensuring the deduplication logic correctly validates fields to avoid duplicate or inaccurate records.

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