data-doc
Document datasets, variables, sources, and merge keys for replication
Install / Use
npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill data-docInstalls into whichever agent you are using.
SKILL.md
Installable skill definition
Quality Score
Category
Content & MediaSupported Platforms
Our assessment of data-doc
data-doc scores 86/100 on our quality scale, 332nd of 574 Content & Media skills we index.
Its SKILL.md is 4.0 KB long, well organised into 11 sections with 2 code examples: a solid amount of guidance for an agent.
With 4,360 GitHub stars, it is one of the more widely adopted skills in the catalogue.
Maintenance, license and trust
- The repository was last updated 3 days ago, so data-doc is actively maintained.
- No license is declared. By default that means all rights are reserved: you can read it, but reusing or redistributing it is not clearly permitted. Ask the author before building on it commercially.
- Its trust signals score 88/100, with 1 caution from licensing, adoption, age or documentation. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.
data-doc compared with similar skills
All 4 of these similar skills score higher than data-doc; compare them before choosing.
| Skill | Score | Stars | Updated | Format |
|---|---|---|---|---|
| data-doc (this skill)by brycewang-stanford | 86 | 4.4k | 3d ago | SKILL.md |
| siyuanby siyuan-note | 100 | 46.5k | today | MCP Server |
| algorithmic-artby anthropics | 100 | 177.9k | 4d ago | SKILL.md |
| pptxby anthropics | 100 | 177.9k | 4d ago | SKILL.md |
| designby nextlevelbuilder | 100 | 130.2k | 5d ago | SKILL.md |
Frequently asked questions
- How do I install data-doc?
- Run
npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill data-doc. The install tabs above show the steps for each supported agent. - Which AI agents does data-doc work with?
- It is written for Universal, as a SKILL.md file. Other agents that read the same format can often use it too.
- Is data-doc safe to use?
- It declares no license and scores 88/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
- Is data-doc still maintained?
- The repository was last updated 3 days ago, so data-doc is actively maintained.
Skill content
View source on GitHubname: data-doc description: Document datasets, variables, sources, and merge keys for replication
Data Documentation Assistant
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
| Source | Access | Raw File | Notes |
|--------|--------|----------|-------|
| WRDS Compustat | Subscription | raw/compustat_funda.csv | Annual fundamentals |
| Revelio Labs | Licensed | raw/revelio_positions.parquet | Via BU server |
| Hand-collected | Manual | raw/manual_coding.xlsx | See coding protocol |
3. Key Variables
| Variable | Type | Description | Source | Notes |
|----------|------|-------------|--------|-------|
| gvkey | str | Compustat firm identifier | Compustat | Primary key |
| fyear | int | Fiscal year | Compustat | |
| at | float | Total assets ($ millions) | Compustat | Winsorized 1/99 |
| treated | int | =1 if treated firm | Constructed | See 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:
| Filter | Observations Dropped | Remaining | |--------|---------------------|-----------| | Raw data | - | 150,000 | | Drop financials (SIC 6000-6999) | 25,000 | 125,000 | | Require non-missing assets | 5,000 | 120,000 | | Require 2+ years in panel | 10,000 | 110,000 |
6. Merge Keys
| Dataset A | Dataset B | Key(s) | Match Rate | Notes | |-----------|-----------|--------|------------|-------| | Compustat | CRSP | gvkey | 95% | Via CCM link table | | Compustat | PitchBook | EIN | 72% | Manual cleaning needed | | Revelio | Compustat | company_name | 68% | 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
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Trust signals
From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.
