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. Use gbrain research init to scaffold a new one.
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:
- Save raw source immediately (before any extraction)
- Extract fields using deterministic regex first, LLM fallback
- When summarizing batch results: re-read from saved files
- 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_datafor email bodies, API responsesfile_uploadfor PDF attachments, documents- Create
.redirect.yamlpointers 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/):
- investor-updates — extract MRR, ARR, growth, burn, runway, headcount from investor update emails
- expense-tracker — extract amounts, recipients, platforms from receipt emails (subscriptions, services, recurring charges)
- 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.