Data Documentation Assistant

SkillDatabases & data

data-doc is a skill that has your AI write clear documentation for the datasets it works with in empirical research. Once added, your AI records each dataset's variables, sources, and merge keys in structured templates, so the work can be replicated by others later.

Available today. Use it from your connected AI after setup.

Add the skill, then ask your AI to document a dataset you are working with. It will follow the structured template to capture the variables, sources, and merge keys.

Then ask your AI: use the Data Documentation Assistant skill

What your AI can do with it

  • Document datasets and their variables in structured templates
  • Record where each dataset came from
  • List the merge keys that connect datasets to each other
  • Produce documentation that supports replication of the analysis

What this skill tells your AI

The instructions your AI receives, as published by brycewang-stanford/auto-empirical-research-skills in skills/30-zirui-song-claude-skills/data-doc/SKILL.md and read by ahel’s review.

Help document datasets systematically for replication packages, co-author handoffs, and future reference.

Dataset Documentation Template

When documenting a dataset, capture:

1. Dataset Overview

  • Name: (e.g., firm_year_panel.parquet)
  • Location: (relative path from project root)
  • Created by: (script that generates it)
  • Last updated:
  • Unit of observation: (firm-year, loan-quarter, etc.)
  • N observations:
  • Time coverage:

2. Source Data

SourceAccessRaw FileNotes
WRDS CompustatSubscriptionraw/compustat_funda.csvAnnual fundamentals
Revelio LabsLicensedraw/revelio_positions.parquetVia BU server
Hand-collectedManualraw/manual_coding.xlsxSee coding protocol

3. Key Variables

VariableTypeDescriptionSourceNotes
gvkeystrCompustat firm identifierCompustatPrimary key
fyearintFiscal yearCompustat
atfloatTotal assets ($ millions)CompustatWinsorized 1/99
treatedint=1 if treated firmConstructedSee section 4

4. Variable Construction

For constructed/derived variables, document:

Variable: treated
Definition: =1 if firm received first PE investment in year t
Construction:
  1. Merge PitchBook deals to Compustat on EIN
  2. Keep first deal per firm
  3. Flag year of first investment
Script: 2a_construct_treatment.py, lines 45-78

5. Sample Filters

Document all filters applied:

FilterObservations DroppedRemaining
Raw data-150,000
Drop financials (SIC 6000-6999)25,000125,000
Require non-missing assets5,000120,000
Require 2+ years in panel10,000110,000

6. Merge Keys

Dataset ADataset BKey(s)Match RateNotes
CompustatCRSPgvkey95%Via CCM link table
CompustatPitchBookEIN72%Manual cleaning needed
RevelioCompustatcompany_name68%Fuzzy match, see script

Quick Commands

  • "document this dataset" - Generate full template for a dataset
  • "variable list" - Create variable table only
  • "merge documentation" - Focus on merge keys and match rates
  • "sample flow" - Generate sample filter table
  • "codebook" - Formal codebook format for replication package

Codebook Format (for Replication Packages)

================================================================================
CODEBOOK: firm_year_panel.dta
Generated: 2026-01-15
================================================================================

IDENTIFICATION
  gvkey         Compustat permanent firm identifier
  fyear         Fiscal year

OUTCOME VARIABLES
  roa           Return on assets = ni/at (winsorized 1/99)
  investment    Capex/lagged assets = capx/L.at

TREATMENT VARIABLES
  post          =1 for years after treatment
  treated       =1 for firms ever treated
  treat_post    Interaction: treated × post (DiD coefficient)

CONTROLS
  size          Log total assets = ln(at)
  leverage      Book leverage = (dltt+dlc)/at
  mtb           Market-to-book = (prcc_f×csho)/ceq

FIXED EFFECTS
  ff48          Fama-French 48 industry classification
  state         State of incorporation
================================================================================

Data Provenance Checklist

Before finalizing a dataset, verify:

  • All source files documented with access instructions
  • Variable definitions are unambiguous
  • Sample filters documented with observation counts
  • Merge match rates reported
  • Winsorization/trimming documented
  • Missing value treatment documented
  • Script that creates dataset is identified
  • Date of creation recorded

Signals

GitHub stars
4k
Forks
476
Last commit
Sep 2026
Advanced
Catalog kind
skill
Gateway key
data-doc
Source
github.com/brycewang-stanford/auto-empirical-research-skills