Use when collecting data for a research project, downloading time series, building a dataset, accessing economic or social data APIs, or scraping data from a non-API source. Handles source discovery, respectful collection, local caching, and manifest documentation.
Start a fresh conversation. Replace the highlighted placeholders.
Use data-collection to build a documented, locally cached dataset for [research question] covering [variables, geography, and period], preferring suitable APIs and delivering raw versionable data plus a manifest.
Required inputs
[research question]
[variables, geography, and period]
User-approved starter prompt based on a pinned source version; check current skill requirements before running. Task evidence ↗
SKILL.md
name: data-collectiondescription: Use when collecting data for a research project, downloading time series, building a dataset, accessing economic or social data APIs, or scraping data from a non-API source. Handles source discovery, respectful collection, local caching, and manifest documentation.
Data Collection
Overview
This skill guides data collection from the research question to a versionable artifact in data/raw/. It is field-agnostic and open-ended about sources — the references/common-sources.md file is a starting point, not a boundary. For any research question, the skill uses web search to find appropriate sources beyond the common list.
When to Use
"I need data on X"
"Download the unemployment series"
"Build a dataset of country-level GDP"
"Scrape this website for paper data"
"Where can I get data about Y?"
Any new project's collection phase
Mandatory Steps
Identify data needs from the research question. Variables, units (country, firm, individual, pixel), frequency, period, geography, and any necessary keys for merging across sources.
Find appropriate sources. Start with references/common-sources.md. If the user's needs are not covered there, search the web for the relevant source. Never invent a URL or API endpoint from memory.
Prefer APIs over scraping. APIs are versioned, documented, and legal. Scraping is the last resort when no API is available.
When scraping is necessary, be respectful:
Check and honor robots.txt
Rate limit — minimum one request per second, often slower
Exponential backoff on errors (e.g., 1s, 2s, 4s, 8s, cap at 60s)
Identifiable user agent with contact information
Cache aggressively — never re-download unnecessarily
Never scrape a source that explicitly prohibits automated access in its terms of service
Save raw data in data/raw/ in a versionable format. Parquet is preferred for tabular data; CSV is acceptable for small datasets. Never edit raw files by hand.
Document every dataset in data/manifest.md following the format from replication-driven-research: name, source, URL or API endpoint, collection date, variables used, frequency, period, license or usage notes.
Cache locally. Check data/raw/ before fetching. Only invoke the network if the file is missing or the user has explicitly requested a refresh.
Source Discovery Process
Follow this order when looking for a data source:
Check references/common-sources.md for known sources in the relevant domain.
Web-search for "<topic>" open data API or "<topic>" dataset download.
Check open science repositories: Harvard Dataverse, Zenodo, Open Science Framework, figshare.
Check the topic's primary institutional source (central bank, statistical agency, international organization, regulatory body).
If none of the above work, escalate to the user — manual acquisition may be required (e.g., contacting authors, subscribing to a database).
Anti-Patterns
Scraping a source whose robots.txt prohibits it
Hitting an API without rate limiting
Re-downloading data that already exists in data/raw/
Raw data without a manifest entry
Editing a raw data file manually to "fix" issues
Collecting data without specifying the period and frequency upfront
Trusting a URL invented from memory
Merging datasets without documenting the merge keys
Verification Before Completion
Data saved under data/raw/ in a versionable format (parquet preferred)
Manifest entry added for every new dataset
Collection logic lives in a script under code/, not an interactive session
Source license checked and recorded in the manifest