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Claude Code on the web

Create (simple) datasets from your browser — or your phone — with no local setup at all: no VSCode, no terminal, no sandbox. Claude Code on the web runs sessions in a cloud environment with the ETL repository checked out, creates a pull request with a staging server, and fills in the metadata for you.

This is the low-friction alternative to Fast-track (no spreadsheets, full traceability in ETL) and to the terminal-based AI workflow (which remains the right tool for power users).

Set up the environment (once)

  1. Open https://claude.ai/code.
    • First-time users are redirected to an onboarding flow: use the name etl and Trusted or Full network access (you can switch to Full later if Trusted turns out to be limiting).
    • Existing users won't see onboarding: click the environment selector ("☁ Default") above the chat input and create a new etl environment with the same settings.

  2. Edit the etl environment and paste the environment variables from 1Password into Environment variables (three lines, R2_ENDPOINT=...) → Save changes.

    Warning

    Environment variables are visible to anyone who can edit the environment — only put values there that are okay to share within the org (like the 1Password ones above).

  3. Next to the environment selector is the repository picker — choose owid/etl.

Create a dataset

Drag a CSV into the chat (or give Claude a URL with data) and ask:

Create a dataset from the attached CSV

Claude will create a pull request with a staging server and fill in all the metadata. It might ask a few clarifying questions, and you can steer it however you like — ask it to edit metadata, visualise the data in the chat, add custom processing, and so on.

From there:

  1. Create or edit charts on the staging server via its Admin (link in the PR).
  2. When you're happy, approve your changes in chart-diff (also linked in the PR).
  3. Merge the PR — this syncs your charts to production.

Feedback

This workflow is actively evolving. If you try it, share your session or reach out in #data-scientists on Slack — every attempt improves the dataset creation skill.