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.
curl -O https://raw.githubusercontent.com/boettiger-lab/data-workflows/main/.claude/skills/dataset-recipes/SKILL.mdgit clone --depth 1 https://github.com/boettiger-lab/data-workflowsWrote 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.
[](https://agentmods.dev/skills/boettiger-lab/data-workflows/dataset-recipes)<a href="https://agentmods.dev/skills/boettiger-lab/data-workflows/dataset-recipes"><img src="https://agentmods.dev/badge/skills/boettiger-lab/data-workflows/dataset-recipes/github.svg" alt="Measured on agentmods" height="20"></a>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.
<a href="https://agentmods.dev/skills/boettiger-lab/data-workflows/dataset-recipes"><img src="https://agentmods.dev/badge/skills/boettiger-lab/data-workflows/dataset-recipes.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once 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 | $0.00243 |
Grade A, and why
dataset-recipes scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
for id in 01 02 03; do curl -sS -O "https://example.com/data_${id}.zip" & done 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 noCASE, 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.
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.
- 8d ago Changed 1943b3fb08cd
- 24d ago Changed · +43 lines · +12 tokens per session 22f9b7c2d136
- 2mo ago First seen · 110 lines · 82 tokens per session scan A 92da5f823852
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.
dataset-recipes compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| dataset-recipesboettiger-lab/data-workflows | Skill | 5 | 2d ago | BSD-3-Clause | A | 94 | Claude Code |
| arboretoK-Dense-AI/scientific-agent-skills | Skill | 56k | 4d ago | MIT | A | 55 | Claude Code, Codex |
| deepspot-mK-Dense-AI/scientific-agent-skills | Skill | 56k | 4d ago | MIT | A | 70 | Claude Code |
| torchdrugK-Dense-AI/scientific-agent-skills | Skill | 56k | 4d ago | MIT | A | 64 | Claude Code |
| pyhealthK-Dense-AI/scientific-agent-skills | Skill | 56k | 4d ago | MIT | A | 84 | Claude Code, Codex |
| nemo-mbridge-perf-expert-parallel-overlapNVIDIA/skills | Skill | 3.9k | yesterday | Apache-2.0 | A | 56 | Claude Code |
| ml-training-recipesOrchestra-Research/AI-Research-SKILLs | Skill | 14k | 26d ago | MIT | A | 88 | Claude Code |