data-workflows: Skill for Claude Code

.claude/skills/dataset-recipes/SKILL.md

dataset-recipes is a skill for Claude Code from boettiger-lab/data-workflows. It costs 94 tokens per session (2,425 once invoked), scanned A, original, BSD-3-Clause.

A collection of complete examples for importing geographic and other large datasets. The recipes cover formats such as File Geodatabases, zipped Census files, Cloud-Optimized GeoTIFFs, and remote archives.

In plain words
What is it for?
Use it to choose an ingestion pattern, inspect remote archive contents, preprocess multi-file sources, and build repeatable dataset workflows.
Why use it?
It provides proven starting points for new data pipelines instead of requiring every import to be designed from scratch. One example shows how to inspect a small table inside a huge remote ZIP without downloading the whole archive.

Skill for Claude Code

Written for Claude Code: installed under .claude/. Also seen: positional $N argument.

This is boettiger-lab/data-workflows's own configuration. It tells Claude Code how to work on data-workflows itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything data-workflows configures →

Reuse

Borrowing it

Nothing to install: this file belongs to boettiger-lab/data-workflows. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/boettiger-lab/data-workflows/main/.claude/skills/dataset-recipes/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/boettiger-lab/data-workflows

Made for: Claude Code.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for dataset-recipes

README.md
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Your own site
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Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for dataset-recipes

Your own site · 80×15
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Per session 94 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,425 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00094 $0.02425
Opus 5.5 $0.00038 $0.00970
Sonnet 5.5 $0.00019 $0.00485
Haiku 4.5 $0.00009 $0.00243

Measured 8d ago against content hash 1943b3fb08cd, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

dataset-recipes scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

for id in 01 02 03; do curl -sS -O "https://example.com/data_${id}.zip" & done
.claude/skills/dataset-recipes/SKILL.md · 153 lines

How it starts

The opening of the file, as written. The whole thing — 153 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Dataset Recipes

Copy the closest-shaped recipe rather than deriving a pipeline from scratch.

💡 Read one small table out of a HUGE remote zip — range reads, no localize (#518)

To inspect a schema, a lookup/domain table, or one layer's coverage inside a multi-GB zipped GDB, do not localize the archive (a PVC + 30 GB download for a 126-row table). GDAL's /vsizip//vsicurl/ reads the zip central directory plus only the bytes it needs over HTTP range requests — a small cluster job, seconds to a couple of minutes:

# authoritative coded domain out of the archived 30 GB national GDB (internal endpoint)
SRC="/vsizip//vsicurl/http://rook-ceph-rgw-nautiluss3.rook/public-usgs-nhd/raw/NHD_H_National_GDB.zip/NHD_H_National_GDB.gdb"
ogr2ogr -f CSV /vsistdout/ "$SRC" NHDFCode          # 126 rows, ~25 s, no PVC
ogrinfo -ro -q "$SRC" -dialect SQLITE \
  -sql "SELECT COUNT(*), SUM(StreamOrde > 0) FROM NHDPlusFlowlineVAA"
  • Works on a public source URL too (/vsizip//vsicurl/https://prd-tnm.s3.amazonaws.com/...) — ideal for pre-flighting a candidate import before committing to a build.
  • Use -dialect SQLITE: OGR SQL has no CASE, and keep the SQL on one line (a folded YAML block mangles multi-line SQL). SUM(cond) works in the SQLITE dialect.
  • Full-table COUNT(*) over range reads is slow (minutes) because it decodes every feature; schema reads and small tables are fast. Aggregate on the small table, not the geometry layer.
  • Working manifests: catalog/usgs-nhd/k8s/extract-fcode-domain.yaml, catalog/usgs-nhd/k8s/preflight-nhdplus-hr-vaa.yaml.
  • ⛔ Never hand-write a coded domain from memory (#294) — this is how you get the real one.

💡 Check coverage against the source before you publish (#615)

A source file can be valid, readable, internally consistent — and still be a fraction of the dataset. Nothing downstream notices: convert, pmtiles, hex and verify-stac.py all pass on a short read, because none of them knows what the source was supposed to contain. fmmp-2022 shipped and was consumed for two months holding 45,285 of 127,133 features — 13 of 38 counties, missing Riverside, Monterey, Stanislaus, Sacramento and Imperial.

Read the full file on GitHub · 153 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 8d ago Changed 1943b3fb08cd
  2. 24d ago Changed · +43 lines · +12 tokens per session 22f9b7c2d136
  3. 2mo ago First seen · 110 lines · 82 tokens per session scan A 92da5f823852

Subscribe to this mod's changes

dataset-recipes is a skill published in the GitHub repository boettiger-lab/data-workflows (5 stars, last pushed 2d ago), licensed BSD-3-Clause. It adds 94 tokens to every session and 2,425 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

Related

dataset-recipes compared with skills from other repositories

dataset-recipes next to 6 skills from other repositories
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