Create a brand-new OWID dataset in ETL from a data file the user provides — a local CSV/Excel, a downloaded file, or a web link to data (snapshot → meadow → garden → grapher → PR → staging server). Use when someone has data they want in ETL so they can build charts. Designed for non-technical "Cloud co-work" users: infer aggressively, build a working dataset first, then ask the person to review and correct.
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Create a brand-new OWID dataset in ETL from a data file the user provides — a local CSV/Excel, a downloaded file, or a web link to data (snapshot → meadow → garden → grapher → PR → staging server). Use when someone has data they want in ETL so they can build charts. Designed for non-technical "Cloud co-work" users: infer aggressively, build a working dataset first, then ask the person to review and correct.
metadata
{"internal":true}
Create a dataset
Turn data the user provides into a fully wired-up OWID dataset — snapshot, meadow, garden, grapher, DAG entries, a draft PR, and a staging server where the user can build charts.
The input can be anything: a local CSV or Excel file, a file they just downloaded, or a link to a page/file on the web. Whatever the form, the job is the same — get the tabular data into a snapshot, then build the chain on top of it.
This skill is for people who are not ETL experts (typically working in Claude Code on the web / "Cloud co-work"). They may know some metadata (units, what the columns mean) or they may just have a link to the source page. They should not be quizzed field-by-field.
Paired skill — keep in sync./create-snapshot is the canonical owner of the snapshot-creation conventions this skill consumes in Step 4: whenever snapshot conventions change here or there, mirror the change in the other file in the same commit — see the mirror note there. The update-side skills are part of the same family: this skill points into /update-dataset's canonical sections (§5b-bis sanity bounds, §5c harmonization audit, §6b metadata quality, §6c metadata checklist + link verification, §6d scheduled issues), whose outcomes verifies — when those sections change, check whether this file needs a matching edit.
Build first, review later. Don't interview the user for every detail up front. Inspect the file, infer everything you reasonably can, fill sensible defaults for the rest, and build a working dataset end-to-end. Then hand the user a concise review so they correct a finished thing rather than imagine an abstract one.
Ask rarely, and ask all at once. There is exactly one required checkpoint with the user before building (the consolidated confirmation in Step 2), and exactly one after building (the review handoff in Steps 7–8, which also tells them how to publish to live). Don't drip-feed questions. If you can guess it, guess it and flag the guess for review.
Never block on a missing detail. If you can't infer a field, use a clearly-marked placeholder (e.g. attribution_short: TBD), note it in the review, and keep going. A dataset that's 80% right and on staging beats a perfect one that never ships.
Surface every guess. The review in Step 7 must list what you inferred vs. what the user gave you, so nothing silently ships wrong.
Inputs
Required (one of):
A local data file — absolute path to a CSV, Excel, etc.
A web link — a URL pointing at a data file (CSV/Excel/JSON) or a page that links to one.
Optional:
metadata_url — a link to the source page / documentation (if different from a data link). If given, fetch it for metadata (producer, citation, license, definitions).
Anything the user volunteers (units, column meanings, namespace, title…).
Workflow
Step 0 — Pull the latest master first
Users are often not experienced with git and may be sitting on a stale checkout. Before doing anything, get the repo onto a fresh master so the new branch and PR are based on current code:
If they have uncommitted local changes or a detached HEAD that blocks this, surface it plainly and ask how they want to proceed — don't force it. (When testing/iterating in an unusual git state, this step may be intentionally skipped — but for a real user, always start here.)
Step 1 — Get the data into reach and inspect it (no questions yet)
If the input is a web link, first obtain the actual data file: download a direct CSV/Excel/JSON URL (use etl.http's session for OWID hosts; plain download otherwise), or if it's a landing page, WebFetch it to find the download link. Save it locally so the snapshot can ingest it. If the input is already a local file, use it directly.
Then read the file and figure out its shape. Do not ask the user anything in this step.
Load it. Read the first ~50 rows with pandas to see headers, dtypes, and a sample. Then compute, per column: dtype, min/max (numeric), distinct count, % null. Use the duckdb skill or a short .venv/bin/python snippet — whatever's quickest.
Identify the entity (country) column. Look for a column named (case-insensitive) entity, country, nation, location, geo, area, or region. OWID-exported CSVs usually call it Entity. The right column holds country-like names, not codes. If there's both a name column and an ISO code column, use the name column and drop the code.
Identify the time column. Look for year, date, time, period. A column of 4-digit integers in ~1500–2100 is a year; ISO-date strings are a date. If the time dimension is a date (not a plain year), the garden step must keep it as date and .format(["country", "date"]).
Everything else is an indicator column. For each, record name, dtype, value range, and null %.
Detect extra dimensions. If a non-numeric column repeats per (country, year) — e.g. sex, age, variant, fuel_type — it's a dimension, not an indicator. The table is then long-format keyed by ["country", "year", <dim>...]. Most simple CSVs have none; handle them only if present (see the long-format note in /update-dataset).
Infer units from names + ranges (these are guesses — flag them for review):
Signal in column name / values
Inferred unit / short_unit
share, pct, percent, _rate, and values mostly in 0–100
% / %
share / proportion with values in 0–1
flag: is this a fraction (×100 → %) or already %?
usd, gdp, price, cost, $
US dollars / $
population, count, number, _n, integer counts
leave unit descriptive (e.g. cars), short_unit empty
tonnes, kg, kwh, gwh, co2, emissions
physical unit from the name
can't tell
leave unit: "", flag in review
Derive naming defaults from the filename. A filename like Electric car sales (IEA, 2026) - data.csv implies: title Electric car sales, producer IEA, year 2026. Propose:
short_name — snake_case of the core title (electric_car_sales).
namespace — the producer's slug if one already exists under snapshots/ (check ls snapshots/), else a sensible new one.
version — today's date (date -u +"%Y-%m-%d").
Write a one-paragraph internal summary of what you found before moving on.
Step 2 — One consolidated confirmation (the only pre-build checkpoint)
Now ask the user once, with all your best guesses pre-filled, using AskUserQuestion where it fits. Frame it as "here's what I figured out — correct anything that's wrong, otherwise I'll build it." Before listing the specifics, set expectations in one plain sentence so the end isn't a surprise — e.g. "I'll build the dataset and put it on a staging server for you to review; once you're happy you merge the PR and it goes live." Then keep the questions to the few things that genuinely can't be guessed or that would be expensive to get wrong:
Namespace + short_name + dataset title (show your proposal; let them override).
What the data is / source — confirm the producer and, if not already provided, ask for a metadata_url (the source page). If they give one, fetch it now for citation, license, and column definitions.
License — show your best guess (default CC BY 4.0 for academic/IGO sources if unknown) and let them correct.
Other data files the source ships — when the landing page or repository carries several data files (a companion index, summary tables), list them with a default of "not ingesting these" so the user can opt in with one word. Persist the skips as the companion-files # NOTE: in the snapshot .dvc (Step 4 follows /create-snapshot's convention) — that NOTE, not the PR body, is the baseline the update and review workflows diff against; mention them in the PR body as well for the reviewer.
Any column meanings you couldn't infer — only ask about the genuinely ambiguous ones (e.g. "is ev_sales_share a percentage 0–100 or a fraction 0–1?"). Don't ask about columns you're confident on.
Everything else (units you inferred, descriptions, topic tags) you'll fill in and surface for review later — don't ask now.
If a metadata_url was provided, fetch it (WebFetch) and extract producer, citation_full, attribution_short, date_published, license, and any column definitions — same fields as /create-snapshot Step 1.
Step 3 — Create a working branch
On PR permissions: opening the draft PR and pushing to staging is this skill's deliverable. If a session-level rule says "don't open PRs unless explicitly asked," treat the user invoking this skill as that explicit request — go ahead and open the PR. The one exception is when you can't (e.g. the session pins you to a fixed pre-assigned branch you must not leave): in that case build on the current branch, open the PR from it if you can, and say clearly in the Step 7 handoff what you did and didn't do — don't silently skip the PR.
etl pr needs a branch (it fails on a detached HEAD). Create the PR scaffold up front so the rest of the work lands on a branch:
.venv/bin/etl pr"Add <dataset title> (<producer>)" data
This creates the branch + a draft PR and does not commit. (If the user is on a detached HEAD or the working tree is dirty in a way that blocks this, create a branch with git checkout -b data-<short_name> first and tell them you'll open the PR after the build.)
Step 4 — Build the snapshot
This is a manual-import snapshot (you hand the snapshot a local file rather than relying on a stable download URL — even for web inputs, you've already saved the file locally in Step 1). Use the file's real extension in the .dvc name (.csv, .xlsx, …). Follow /create-snapshot conventions, with these specifics:
snapshots/<namespace>/<version>/<short_name>.py — the manual-import script.
Generate both files with the command in /create-snapshot step 3, passing dataset_manual_import: True and dvc_only: False. It runs the wizard's snapshot cookiecutter, which emits the path_to_file variant of run() and — the part that is easy to get wrong by hand — nests license inside origin rather than at the top level. Don't hand-write either file, and don't copy a template into this skill: the cookiecutter is the single source of truth, and copies drift.
Scaffold the three steps with /create-etl-steps (DAG file = the topic that best fits, e.g. energy, health; ask in Step 2 if unclear). Then adapt:
Meadow — load the snapshot, rename the entity column to country if needed, cast low-cardinality string columns (country, dims) to category, tb.format(["country", "year"]) (or ["country", "date"], plus any dims). Keep it light.
Garden — paths.regions.harmonize_names(tb, country_col="country", countries_file=paths.country_mapping_path), then tb.format(...). Add sanity_check_inputs / sanity_check_outputs if the step does more than load-and-format (see CLAUDE.md "Sanity checks"); ground every threshold in the built data and pick value bounds from the by-indicator-type table in /update-dataset §5b-bis, then negative-test the checks. Don't strip origins — follow the metadata-preserving patterns in CLAUDE.md (pr.concat, no np.where, etc.).
Grapher — pass the garden table through unchanged.
Metadata (<short_name>.meta.yml in garden) — generate it with /owid-metadata-generation, then verify it against the mandatory-fields checklist in /update-dataset §6c. Fill title, unit, short_unit, description_short, description_key (free-form markdown prose; non-empty), display.name, display.numDecimalPlaces, display.tolerance per indicator; topic_tags and processing_level in definitions.common; presentation.attribution_short explicitly under definitions.common.presentation — it does not inherit from the origin's attribution_short. In the dataset block, set update_period_days plus owners: the user is the new dataset's first owner — resolve their canonical OWID name from git config user.name via etl.owners.resolve_owner (must match the schemas/dataset-schema.json enum; add the mapping in etl/owners.py + an enum row if missing), mirroring /update-dataset step 1a-bis. Use the units you inferred in Step 1; mark anything uncertain so it shows up in the review.
Outdated-practices check — run /check-outdated-practices on every new step file (including any helper modules, and the Step 4 snapshot .py if one was written) and fix findings before the first run, per /update-dataset step 1b — run the skill, don't eyeball the patterns.
DAG form — write the new chain in the nested (compact) DAG form (grapher → garden → meadow → snapshot declared inline; example in /update-dataset "Removing the old version & reordering the DAG", step 4) and verify it parses: .venv/bin/python -c "from etl.dag_helpers import load_dag; load_dag()".
Fix whatever breaks (trace upstream, never mask). The most common task is country harmonization: the garden run logs unmatched country names that need mapping into <short_name>.countries.json.
Shortcut for OWID-exported CSVs: if the file came out of OWID (entity column literally named Entity), the country names are almost always already canonical. Don't sit through an interactive etl harmonize — auto-build the mapping by matching each entity against the canonical regions (and their aliases), and only fall back to manual mapping for the leftovers:
import json
from pathlib import Path
from owid.catalog import Dataset
import pandas as pd
# Needs the regions dataset built locally:# .venv/bin/etlr data://garden/regions/2023-01-01/regions --private
tb_regions = Dataset(str(sorted(Path("data/garden/regions").glob("*/regions"))[-1]))["regions"]
canonical = set(tb_regions["name"].dropna().astype(str))
alias_map = {}
for name, al in tb_regions[["name", "aliases"]].dropna(subset=["aliases"]).itertuples(index=False):
for a in json.loads(al):
alias_map[a] = name
entities = sorted(pd.read_csv("<path_to_csv>")["Entity"].unique())
mapping, unmatched = {}, []
for e in entities:
if e in canonical: mapping[e] = e
elif e in alias_map: mapping[e] = alias_map[e]
else: unmatched.append(e) # decide these by hand: typo, alias, custom aggregate, or exclude
Resolve unmatched by hand against canonical regions; follow the harmonization-audit guidance in /update-dataset (Step 5c). Residual aggregates the producer defines (e.g. Rest of World, European Union (27)) can be kept as custom entities (map to themselves) — they won't join with population/region data, so note that in the review. Don't silently drop countries — if you exclude any, list them.
After the garden step builds, also run the garden-output entity check from /update-dataset §5c (Python check #5): load the built garden tables and confirm every country value is canonical (regions + latest income groups). It catches what the mapping file can't see — inline tb["country"] = ... assignments and post-harmonization mutations. List any non-canonical entity (including the custom aggregates you kept) in the Step 7 review and the PR body as living outside the canonical system.
Then build and upload the grapher step to staging — target the grapher/... path (no --only, so the grapher:// MySQL upsert step actually runs):
Confirm the upsert actually succeeded before moving on: it should print the dataset's admin URL / id (…/admin/datasets/<id>). Capture that <id> — you'll hand it to the user in Step 7. If the upsert errored or printed no dataset, fix it now rather than handing over a link that won't resolve.
Step 6b — Adversarial fact-check of data and metadata (optional — offer it in the handoff)
Optionally run /adversarial-data-review on garden/<namespace>/<version>/<short_name>. It's not part of the default build because it can consume many tokens (~12–20 web calls even for a small dataset) — mention it as an offer in the Step 7 handoff ("I can also fact-check the data and metadata against the source's documentation and independent sources — say the word") and run it when the user opts in, or proactively when the build surfaced red flags (values that look implausible, a source page contradicting the file).
When it runs: since the dataset is brand-new (no charts yet, few indicators), review all indicators. This catches the two things the Step 7 review table can't: metadata you inferred that the source's own documentation contradicts (units, definitions, scope), and values the source itself got wrong (unit slips, wrong-year rows) — verified against independent sources online. Fold the findings into the handoff in plain language ("I double-checked the numbers against — X and Y match; Z looks off, here's why"), and route confirmed source errors to <short_name>.corrections.yml per that skill's routing table rather than editing the data inline.
Step 7 — Commit, push, and hand off for review
Quality pass before handoff. Run all five checks from /update-dataset §6b on the new garden + grapher .meta.yml files — /check-metadata-typos, /check-metadata-spacing, /check-metadata-style, the general-audience clarity checklist, and the dimension sweep. Include the Step 4 .dvc in the /check-metadata-typos scope: its description / title / citation_full are user-facing too, and no other check spell-checks them (/create-snapshot step 5 makes the same pass for a standalone snapshot — keep the two consistent). Leave citation_full alone unless the producer's own page has the word right; it is verbatim producer text whenever a piece of user-facing text is shared across sibling variants — Jinja over a dimension (the long-format case Step 1 item 5 detects) or a definitions: key that several indicator blocks reuse, which a wide-format file with one column per subgroup produces without any extra-dimension column to detect. A sentence written for one variant renders on all its siblings, where what each view already restricts can make it false; render the text per variant and read each output as a reader of that chart. A brand-new dataset is exactly where such shared text gets written for the first time, so it needs the sweep as much as an update does. Then run the link-verification loop from /update-dataset §6c on every URL in the new .dvc and .meta.yml files, including its anchor-fragment pass for URLs with #… (a curl non-2xx is a signal, not proof — escalate WebFetch → Wayback availability API, and per §6c no automated signal is decisive: a link failing every automated check goes to the user for a browser confirmation, never auto-marked broken or replaced). After any .meta.yml edit, re-run the affected step (--grapher for grapher) so the built catalog and staging reflect it.
Run make check, confirm you're still on the work branch (git branch --show-current — a branch switch in the user's IDE silently moves your shell too), then commit and push:
Update the PR description (attribution blockquote per CLAUDE.md "Team"; what the dataset is, source, coverage, indicator count). Then post @codex review as a separate PR comment; while the user reviews, address any valid findings and resolve the threads per /update-dataset step 10 — don't block the handoff on the review round.
Hand the user a review — this is the second and final checkpoint. Present a compact table they can scan and correct:
Indicator (column)
Title
Unit
Decimals
Inferred?
Plus a short list of:
Things I guessed (units, descriptions, license, topic tags) — "tell me if any are wrong."
Things I couldn't determine (any TBD placeholders).
Countries that didn't match / were excluded, if any.
Always tell them where the metadata file lives locally, so they (or you) can edit it directly:
Metadata YAML:etl/steps/data/garden/<namespace>/<version>/<short_name>.meta.yml — this is the file to edit for titles, units, descriptions, and decimals.
Country mapping: etl/steps/data/garden/<namespace>/<version>/<short_name>.countries.json
Then give them the staging links so they can build charts. Paste the actual URLs directly into the chat — don't tell them to "open the PR" or "go to the staging admin"; non-experts won't know where those are, and they're unlikely to open the PR at all:
Dataset in staging admin: the dataset page printed by the grapher upsert (the <id> you captured in Step 6), https://staging-site-<branch>/admin/datasets/<id>. This is where they create charts from the new indicators.
Set the right expectation about timing. The staging server rebuilds for a few minutes after each push, so this link will 404 (or show a "site not found" page) until the build finishes — that's normal, not a broken link. Tell them to wait ~5 minutes and refresh. Better yet, before you hand the link over, poll it yourself until the staging host responds (e.g. curl -so /dev/null -w "%{http_code}" https://staging-site-<branch>/admin/ stops returning a connection error / 404 "site not found") so you only give them a link that already works. Handing over a link before the build is ready — with no signal that waiting will fix it — reliably reads as "it's broken / I don't see my dataset."
Ask for corrections in plain terms ("anything in the table look wrong? any column you'd describe differently?"). Apply their feedback by editing the .meta.yml / .countries.json and re-running the affected step (--grapher for grapher), then push again.
Step 8 — Tell them how to go live (don't assume they know)
The dataset and any charts they build live on the staging server, not on ourworldindata.org. Getting them to live is a manual step the user has to take, and the workflow (approve charts in chart-diff, then merge the PR) is unfamiliar to non-experts — they will not discover it on their own. Spell it out explicitly in the handoff, with the real links pasted in:
Charts must be approved in chart-diff before they sync to live — and this is a human step, not yours. If the user creates any charts on the staging admin, those charts only reach production if they're approved in chart-diff first. Never approve charts yourself (no CLI, no script, no direct DB write) — chart-diff exists so a person eyeballs each chart before it goes live, and that review must stay human. Give them the direct link and tell them to click Approve on each chart themselves:
Creating the charts is also something the user does in the admin — assume they'll need to visit the admin at some point regardless; your job is to make that the only thing they have to do there.
Merging the PR is what publishes the dataset to live. No external review is required for a data PR like this. The user can click "Ready for review" then "Squash and merge" on the PR themselves (link the PR URL) — but you can also do the merge for them from the session, if they explicitly ask you to and the checks are green. This is the one outward-facing action where, given an explicit "merge it" from the user, you should go ahead — it saves a non-expert a trip to GitHub. Do it carefully:
gh pr checks <number> # confirm checks are green first
gh pr ready <number> # take it out of draft
gh pr merge <number> --squash # publish
Only merge on an explicit request from the user ("merge it", "go live", "ship it") — never proactively, and never as the default end of the skill.
Only merge if checks are green. If a check is red or still running, say so and don't merge — surface what's failing and let them decide. (Don't merge a red PR just because they asked; tell them what's red first.)
If they'd rather click it themselves, that's fine — point them at the PR.
The dataset (and any approved charts) land on ourworldindata.org a few minutes after the merge.
Offer a scheduled update issue if the data refreshes regularly. If update_period_days is roughly in [2, 366] (updated at least yearly but less than daily), the dataset qualifies for a scheduled "Data update" issue in owid/owid-issues, so future refreshes don't depend on anyone's memory. Offer to create the workflow per /update-dataset §6d — cron timed shortly after the producer's expected release window, issue body pointing the next updater at /update-dataset <short_name> — and only create it with the user's sign-off (the commit goes straight to owid-issues main).
Make this a short, plain-language checklist at the end of your handoff — e.g. "When you're happy: (1) approve your charts here «chart-diff link», (2) then either merge the PR yourself «PR link» or just tell me to merge it and I'll do it once the checks are green — it's live a few minutes later." Paste the real URLs, not placeholders.
Notes & gotchas
Snapshot is raw passthrough. Don't sum, dedupe, relabel, or convert period labels in the snapshot — that's garden's job (see CLAUDE.md "Snapshot is raw passthrough only").
Entity → country. OWID-exported files name the country column Entity; rename it in meadow.
Shares: 0–1 vs 0–100. The single most common unit mistake. If a "share"/"%" column tops out near 1, it's a fraction (multiply by 100 in garden, or set the unit to fraction); if it tops out near 100, it's already a percentage. When unsure, this is worth one of your Step 2 questions.
Start on fresh master (Step 0). Saves you from basing the branch/PR on a stale checkout — common with non-git-savvy users. etl pr and staging also need a real branch (not a detached HEAD).
Keep the user oriented. They're not ETL experts. When you report progress, say what you did in plain language and what they can do next (review the table, build charts on staging) — not a wall of pipeline jargon.
