data-research

name	data-research
version	1.0.0
description	Structured data research: search sources, extract structured data, archive raw sources, maintain canonical tracker pages, deduplicate. Parameterized via YAML recipes for investor updates, donations, company updates, or any email-to-structured-data pipeline.
triggers	["research","track","extract from email","investor updates","donations","build a tracker","data dig"]
tools	["search","query","get_page","put_page","add_link","add_timeline_entry","put_raw_data","file_upload"]
mutating	true

Data Research

Structured research pipeline: search sources, extract structured data, archive raw, deduplicate, update canonical trackers, backlink entities.

Contract

One skill for any email-to-structured-data pipeline. The only differences between tracking investor updates, expenses, and company metrics are the search queries, extraction schemas, and tracker page format. All three use the same 7-phase pipeline with parameterized recipes.

When to Use

• User wants to track structured data from email, web, or API sources
• User says "research", "track", "extract from email", "build a tracker"
• User mentions investor updates, donations, company metrics, filings
• User wants to set up recurring data collection (with cron recipe)

Phases

Phase 1: Define Research Recipe

Ask the user what they want to track. Either:

• Pick a built-in recipe: investor-updates, expense-tracker, company-updates
• Define a custom recipe with: source queries, classification rules, extraction schema, tracker page path, tracker format

Recipes are YAML files at ~/.gbrain/recipes/{name}.yaml. Scaffold a new one by copying a built-in recipe file and editing its fields.

Phase 2: Search Sources

Brain first (maybe we already have this data). Then:

• Email via credential gateway: windowed queries (quarterly, monthly if truncated)
• Web via search: public filings, press releases, regulatory data
• APIs: any structured data source the recipe defines
• Attachments: PDF extraction, HTML stripping

Phase 3: Classify

Deterministic first (regex patterns from recipe), LLM fallback. Log every LLM fallback for future regex improvement (fail-improve loop). Skip marketing, newsletters, noise based on recipe's classification rules.

Phase 4: Extract Structured Data

EXTRACTION INTEGRITY RULE:

1. Save raw source immediately (before any extraction)
2. Extract fields using deterministic regex first, LLM fallback
3. When summarizing batch results: re-read from saved files
4. Never trust LLM working memory after batch processing

This prevents a known hallucination bug where batch-processed amounts were 13/13 wrong from LLM working memory while saved files were correct.

Phase 5: Archive Raw Sources

• put_raw_data for email bodies, API responses
• file_upload for PDF attachments, documents
• Create .redirect.yaml pointers for large files in storage
• Every tracker entry must link back to its raw source

Phase 6: Deduplicate

Before adding to tracker:

• Exact match (same key fields) → skip
• Fuzzy match (same entity + date + similar amount within tolerance) → flag for review
• Different amount for same entity+date → add with note (could be correction)

Phase 7: Update Canonical Tracker + Backlink

• Parse existing tracker page (markdown table)
• Append new entries in correct section (grouped by year/quarter/entity)
• Compute running totals
• Backlink every mentioned entity (person → people/ page, company → companies/ page)
• Uses enrichment service for entity pages

Built-In Recipes

Three example recipes ship with GBrain (see ~/.gbrain/recipes/):

1. investor-updates — extract MRR, ARR, growth, burn, runway, headcount from investor update emails
2. expense-tracker — extract amounts, recipients, platforms from receipt emails (subscriptions, services, recurring charges)
3. company-updates — extract revenue, users, key metrics from portfolio company update emails

Anti-Patterns

• Trusting LLM working memory for amounts after batch processing (use extraction integrity rule)
• Creating tracker entries without raw source links
• Running without deduplication (leads to double-counted entries)
• Hardcoding source-specific patterns in the pipeline code (use recipes)

Output Format

Brain page at the recipe's tracker_page path with markdown tables:

### 2026

| Date | Company | MRR | ARR | Growth | Status |
|------|---------|-----|-----|--------|--------|
| 2026-04-01 | Example Co | $188K | $2.3M | +14.7% MoM | [Source](link) |

Each entry links to its raw source. Running totals at the bottom of each section.

Conventions

References skills/conventions/quality.md for citation and back-linking rules.