Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/boettiger-lab/data-workflows/dataset-recipesnpx skills add boettiger-lab/data-workflows --skill dataset-recipesgit 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.svg" alt="Measured on agentmods" 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 | $0.00082 | $0.01728 |
| Opus 5 | $0.00041 | $0.00864 |
| Sonnet 5 | $0.00016 | $0.00346 |
| Haiku 4.5 | $0.00008 | $0.00173 |
Grade A, and why
dataset-recipes scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
for id in 01 02 03; do curl -sS -O "https://example.com/data_${id}.zip" & done How it starts
The opening of the file, as written. The whole thing — 110 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.
Step 1c: Preprocessing multi-file zipped datasets
cng-convert-to-parquet rejects multiple .zip URLs. For per-state/per-region zips, preprocess: download in parallel, unzip, pass shapefiles (the tool merges them automatically):
for id in 01 02 03; do curl -sS -O "https://example.com/data_${id}.zip" & done
wait && unzip -q -o "*.zip"
cng-convert-to-parquet /tmp/data/*.shp s3://bucket/output.parquet
See catalog/census/k8s/tract/preprocess-tract.yaml for a complete k8s job.
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.
- 5d 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 yesterday), licensed BSD-3-Clause. It adds 82 tokens to every session and 1,728 once invoked, about $0.0004 per session on Opus 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.
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