Working with datasets using AI assistants

Datapressr is a skills repo — instead of a CLI, it provides AI assistant instructions that work across tools (Claude Code, Codex CLI, Gemini CLI, etc.).

Quick start

Install the skills into your agent once, then start a session:

npx skills add datasets/datapressr
mkdir world-gdp && cd world-gdp && claude

Then inside the session:

/init world-gdp

Your AI assistant will scaffold the directory, create datapackage.json, and explain next steps.

(Claude Code in this repo also picks the skills up directly via .claude/skills/ symlinks — no npx skills step needed when working inside datapressr itself.)

AGENTS.md

Every dataset directory should contain AGENTS.md — a knowledge file that gives your AI assistant immediate context about dataset structure, conventions, and the publish workflow. The /init command copies it automatically.

AGENTS.md is the standard cross-tool instruction file:

Skills

The skills/ directory holds one SKILL.md playbook per step, in the flat layout npx skills expects — so they install into any agent, not just Claude Code. See skills/README.md. In Claude Code the judgement-heavy playbooks (capture, archive, structure) and the mechanical last-mile steps below are all invocable as /<name> slash commands:

/init <name>

Scaffold a new dataset directory.

/init world-gdp

Creates:

world-gdp/
  datapackage.json              # dataset metadata and resource list
  data/                         # data files go here
  .datahubignore                # gitignore-style exclusions for dh push
  AGENTS.md                     # AI assistant context
  scripts/validate-datapackage.mjs   # the deterministic check /validate runs

/validate

Check datapackage.json for common issues before pushing.

Runs scripts/validate-datapackage.mjs (copied into the dataset by /init, zero dependencies, plain Node — no package.json needed to run it) and reports its output: errors (must fix) and warnings (worth fixing).

• Errors: missing file, invalid JSON, unsafe name, empty resources, a resource path that doesn't exist
• Warnings: missing title/description/status, unlisted files in data/, large files, missing licenses/sources past stub, resources with no typed schema or no primaryKey

The script itself has a test suite in the datapressr repo (npm test, using Node's built-in test runner against fixture datapackages in scripts/fixtures/) — it's the one piece of this project's tooling that's actually tested, rather than being an LLM re-deriving a checklist from prose each run.

/push

Push the current dataset to DataHub.

Requires env vars:

export DATAHUB_API_URL=https://datahub.io
export DATAHUB_API_TOKEN=<your-token>
export DATAHUB_PUBLICATION=<your-publication-slug>

Runs dh push . using the dh CLI.