find-data

name	find-data
description	Find a dataset for a Data2Story blog. Accepts a topic, a URL, or a DIP-style category. Downloads + validates against 4 completeness gates before handing off to /data2story-pro. Local-first: searches Economist/Pudding/TidyTuesday clones before going online. Supports --validate-only to audit a folder you already have.
argument-hint	<topic | URL | category | folder-path> [--mode single|theme] [--source Economist|Pudding|tidytuesday] [--out ./datasets/<name>] [--validate-only]
allowed-tools	Bash(python:*), Read, Write, Glob, Grep, WebSearch, WebFetch, AskUserQuestion

find-data

Turn an idea, URL, or category into a phase2/datasets/<name>/ folder that the /data2story-pro pipeline can run on without crashing.

You are the gatekeeper before the 7-agent newsroom. Detective, Analyst, Editor, Designer, Programmer, Auditor, Inspector all assume the data is already there, parseable, and provenanced. Your job is to make sure that's true before they start.

Refuse to mark a folder ready until it passes 4 gates. See references/completeness_gates.md for the criteria. The gates are codified in tools/audit.py, not in this prose.

Prerequisites

Python deps for the tools/:

• pandas — required (audit.py reads/inspects CSV/JSON).
• openpyxl — required only when a source is .xlsx (audit.py's pd.ExcelFile); a mid-run missing-openpyxl is the usual cause of an XLSX audit error.
• urllib — stdlib, no install (fetch.py downloads, audit.py HEAD-checks).

One install line:

pip install pandas openpyxl

After editing anything under tools/, run the no-network regression suite: py tools/selftest.py (exit 0 = pass).

Resolve paths first

• SKILL_DIR = directory containing this SKILL.md
• WORKSPACE = ancestor that contains phase2/datasets/ (the parent repo root), if one exists
• DATASETS_ROOT = WORKSPACE/phase2/datasets when WORKSPACE exists; otherwise ./datasets (clone-relative, under the current working dir) — the open-source clone has no phase2/datasets
• BLOGS_ROOT = WORKSPACE/phase2/blogs when WORKSPACE exists
• OUT_DIR default = DATASETS_ROOT/<name> (so ./datasets/<name> on an OSS clone). Callers may override with an explicit --out, which always wins.
• INPUT = first positional argument from $ARGUMENTS
• Parse flags from $ARGUMENTS: --mode, --source, --out, --validate-only

Never hard-code machine paths. Resolve at runtime by walking up from SKILL_DIR.

Step 0 — Classify the input

Decision tree (the FIRST condition that matches wins):

1. --validate-only flag present → MODE = validate-only. INPUT must be an existing folder path.
2. INPUT is an existing folder path (and --validate-only absent) → still ask the user before re-fetching; default to validate-only behaviour with a confirmation.
3. INPUT starts with http:// or https:// → MODE = url.
4. INPUT (case-insensitive) exactly matches a DIP category from phase2/datasets/data_is_plural/dip_category_summary.md → MODE = category.
5. Otherwise → MODE = topic.

State your classification out loud in one line before doing anything else.

No local corpora (open-source clone): on a machine without the Economist/, Pudding/, tidytuesday/ clones and without the Data-is-Plural CSV under phase2/datasets/, browse_local.py and dip_query.py both return empty (no crash). In that case there is nothing to match locally, so topic/category discovery degrades straight to WebSearch (Step 1). No flag is needed — detect it from the empty browse_local.py + dip_query.py results. Keep the local-first path intact for machines that DO have the corpora.

Step 1 — Branch on mode

validate-only mode

Just audit. Skip discovery, skip fetch.

python "SKILL_DIR/tools/audit.py" "<folder>"

This writes <folder>/validate.json and prints the gate summary. Read it back with Read, surface the verdict, and stop.

If overall.ready_for_data2story is true → print the /data2story-pro <folder> command for the user to copy.

If it's false → list the specific gate failures and recommend remediations.

url mode

Determine OUT_DIR (use --out if given, else derive a slug from the URL path's last meaningful segment, store under DATASETS_ROOT/<slug>/ — i.e. ./datasets/<slug>/ on an open-source clone with no phase2/datasets).

Branch on URL shape:

URL pattern	Tool
github.com/{owner}/{repo}/tree/{branch}/{path}	python fetch.py github-folder <url> <out_dir>
github.com/{owner}/{repo}/blob/{branch}/{path}	Same (fetch.py auto-detects blob)
github.com/{owner}/{repo} with no path	Refuse — tell user to narrow to a folder
Anything else	python fetch.py url <url> <out_dir>

fetch.py is source-aware: if the URL points to TheEconomist/graphic-detail-data, the-pudding/data, or rfordatascience/tidytuesday AND the local clone already contains that path, it copies from the local clone (no network). Otherwise it fetches via the GitHub API. This happens automatically — do not handle it yourself.

URL normalization (share-page vs direct-download)

Several hosts serve a human-facing page at the obvious URL, not the raw bytes, so a naive download returns HTML instead of a .csv/.json/.xlsx. fetch.py auto-rewrites GitHub /blob/ URLs to raw.githubusercontent.com and, as a backstop, warns on stderr if a data-extension download comes back as an HTML page (<!doctype html> / <html). The other common share-hosts are not auto-rewritten — pass the direct-download form yourself:

Host	Page URL (returns HTML)	Direct-download form to use
GitHub	github.com/<o>/<r>/blob/<branch>/<path>	auto-handled → raw.githubusercontent.com/<o>/<r>/<branch>/<path>
Google Drive	drive.google.com/file/d/<id>/view	drive.google.com/uc?export=download&id=<id>
Dropbox	...?dl=0	swap to ...?dl=1 (or dl.dropboxusercontent.com)
OneDrive	share link (1drv.ms/...)	append &download=1 to the direct link
Kaggle	dataset page (kaggle.com/datasets/...)	the file API / CLI download — the page is not a file

If a download trips the HTML warning, you almost certainly handed fetch.py a share/page URL — fix the URL to its direct-download form and re-fetch.

After fetch: check whether the folder has a README.md.

• If yes → leave it alone, jump to Step 2.
• If no → generate one from tools/README_template.md. Auto-fill what you can (title from folder name, column headers from the first CSV, source URL from the manifest). Leave the codebook definition cells as {TODO} for the user to fill in. Tell the user explicitly that README is a stub.

category mode

INPUT is one of the 14 DIP categories. Run both browse and DIP query in parallel:

python tools/browse_local.py "<INPUT>" --top 10
python tools/dip_query.py --category "<INPUT>" --top 10

Both emit JSON. Merge:

• Local candidates (already-cloned folders) score higher than DIP leads (which still need fetching).
• For each local candidate, run a cheap pre-score (does Gate 1 trivially pass — is there ≥ 1 CSV in the folder?).

Present top 5 to the user as a numbered list via AskUserQuestion:

• For each candidate, show: source · title · date · top-3 files · 1-line readme excerpt
• Options: pick 1–5, or "all" (theme mode), or "skip" (abort)

Then route the pick:

• Local candidate → copy folder into OUT_DIR (or in-place audit if user accepts).
• DIP lead → extract URL from the candidate's links array. If multiple URLs, pick the one with a data-file extension (csv/xlsx/json) or the GitHub one. Route to url mode for that URL.

topic mode

Same as category mode but use full-text query:

python tools/browse_local.py "<INPUT>"
python tools/dip_query.py "<INPUT>" --top 10

If BOTH return empty (no local hits, no DIP hits), only THEN do WebSearch: <INPUT> open dataset CSV site:ourworldindata.org OR site:github.com OR site:data.gov. Cap web results at 5. Surface them with a "no local matches found, querying web" disclaimer.

From the WebSearch results, pick the one whose URL ends in a data-file extension (.csv / .xlsx / .json / .tsv) or is a github.com/{owner}/{repo}/tree/{branch}/{path} folder. Route that URL through url mode: python tools/fetch.py url <url> <OUT_DIR> for a direct data file, or python tools/fetch.py github-folder <url> <OUT_DIR> for a GitHub folder. Then continue to the README step (Step 2) and the audit step (Step 3) exactly as in url mode — this closes the topic → web → fetch → audit loop. If no result has a usable data URL, report that honestly and do not fabricate one.

Step 2 — Optional: generate / repair the README

After fetch, the OUT_DIR may or may not have a README. Decide:

• README present + has codebook table + has source mention → leave alone
• README missing → generate from tools/README_template.md
• README present but no codebook table → ADD a codebook section, do not overwrite the existing prose

When generating, fill in:

• {TITLE}: human-readable from folder slug (snake_case → "snake case", title-cased)
• {PRIMARY SOURCE URL}: from manifest.json items' urls
• {filename.csv} rows: scan each CSV's columns
• {Codebook}: list each column with {TODO: definition} for unknown ones; you may guess from column names (launch_year → "year of launch (integer)") but flag guesses with a {?} prefix so the user can correct.

Step 3 — Audit (the 4 gates)

python "SKILL_DIR/tools/audit.py" "<OUT_DIR>"

This writes <OUT_DIR>/validate.json. Read it back.

For Gate 4 (multimodal — advisory only), audit.py cannot judge by itself. Do your own quick check after reading validate.json:

• From the CSV columns and entity samples, classify subjects:

  • Concrete (people / places / species / events / specific objects) — e.g., "launches.csv has agency names like SpaceX, NASA, Roscosmos → concrete"
  • Abstract (rates, indices, generic counts) — e.g., "GDP per capita time series → abstract"

• For concrete subjects, do ONE WebSearch with site:commons.wikimedia.org to verify at least 1 reference photo exists for a representative subject. Do NOT download photos — just verify they exist. That's Detective's job.

• Write your Gate 4 finding back into validate.json under gates.multimodal.info.

Step 4 — Verdict

Read the final validate.json. Print a concise summary:

=== find-data verdict ===
folder: <OUT_DIR>
files: <N>
[OK ] Technical
[WARN] Story material
       - <filename.csv>: only 47 rows (< 50)
[OK ] Provenance
[INFO] Multimodal: concrete subjects (agencies, rocket types)

overall: READY
next:    /data2story-pro <OUT_DIR>

If overall is BLOCKED, do NOT print the /data2story-pro command. Instead print:

overall: BLOCKED
to fix: <specific remediations from the fails list>

Suggest concrete fixes:

• "README missing → run /find-data --validate-only after adding README"
• "Primary URL dead → find an alternate source via /find-data --source-finder"
• (or open-ended: "drop a corrected file into and re-run")

Step 5 — Save a digest

Append a one-line entry to DATASETS_ROOT/_find-data-log.md (create if not present) so the user has a running history:

- 2026-05-25T14:00Z · single · datasets/<name> · READY · invoked via URL

Constraints

• Never overwrite an existing README.md without asking. Generate-from-template only fires when README is absent. If README exists but is thin, ADD sections, don't replace.
• Never modify the skill repo itself. Your only writes are inside the dataset output directory (DATASETS_ROOT/<OUT_DIR>/) and the digest log.
• Never fabricate a source URL or license. If you can't find a real source for a generated README, leave the {PRIMARY SOURCE URL} placeholder and warn the user.
• Cap WebSearch at 5 results, cap WebFetch at 3 calls per invocation. This is a gating skill, not a research tool — depth is Detective's job.
• Do not run /data2story-pro yourself. Your last word is the verdict + the command for the user to copy. Hand-off is manual.

Reference files

• references/completeness_gates.md — exact pass/warn/fail criteria for each gate
• references/input_mode_dispatch.md — full URL classification table
• references/examples/good_economist.md — what single mode output should look like
• references/examples/good_theme.md — what theme mode output should look like
• tools/audit.py — deterministic gate evaluator
• tools/fetch.py — source-aware downloader (manifest / github-folder / single-url modes)
• tools/browse_local.py — index Economist/Pudding/TidyTuesday for candidate matching
• tools/dip_query.py — filter dip_categorized.csv by category or topic
• tools/README_template.md — graphic-detail-style skeleton for auto-generation