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/hex-tuningnpx skills add boettiger-lab/data-workflows --skill hex-tuninggit 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/hex-tuning)<a href="https://agentmods.dev/skills/boettiger-lab/data-workflows/hex-tuning"><img src="https://agentmods.dev/badge/skills/boettiger-lab/data-workflows/hex-tuning.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 | $0.00080 | $0.03525 |
| Opus 5 | $0.00040 | $0.01762 |
| Sonnet 5 | $0.00016 | $0.00705 |
| Haiku 4.5 | $0.00008 | $0.00352 |
Grade A, and why
hex-tuning scanned grade A with 0 findings 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 4d 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 216 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Hex Tuning
How to size a vector hex job, and how to choose resolutions that stay joinable to the rest of the catalog.
Memory and Chunking Mental Model
RAM is driven by the H3 cell count of the single largest feature in a chunk — not dataset size or bounding box.
⛔ MEASURE EVERY dimension you request — never inherit one from a neighbouring job
kubectl top pod columns are NAME CPU MEMORY — cpu is $2, memory is $3. Reading $1
gives you the pod name and every number comes out zero.
# memory: handle both Mi and Gi — kubectl top mixes them, and a naive gsub(/Mi/,"") reports
# a peak BELOW the mean
kubectl -n geo-workflows top pod --no-headers | grep '^<prefix>' | awk '{
v=$3; if(v~/Gi$/){gsub(/Gi/,"",v); v=v*1024} else gsub(/Mi/,"",v);
if(v+0>m)m=v+0; s+=v; n++} END {printf "n=%d peak=%.2fGi mean=%.2fGi\n", n, m/1024, s/n/1024}'
# cpu
kubectl -n geo-workflows top pod --no-headers | grep '^<prefix>' | awk '{
c=$2; gsub(/m/,"",c); if(c+0>m)m=c+0; s+=c; n++} END {
printf "n=%d peak=%.2f mean=%.2f cores\n", n, m/1000, s/n/1000}'
Measured on the CHELSA hex (one raster per job), against what was requested:
| dimension | requested | measured | over-ask |
|---|---|---|---|
| memory | 32 Gi | peak 5.2 Gi, mean 3.5 Gi | ~6x |
| cpu | 8 cores | peak 8.6, mean 3.3 cores | ~2.4x |
Both were inherited rather than measured — memory by halving a 64 Gi figure sized for a
35-raster chain rather than the one raster a job runs, cpu by copying the same job's 8. Only 19
of 100 pods used more than 7 cores; 64 used fewer than 4.
CPU over-asks are easy to miss because a job is genuinely parallel in its hot loop. The
exact_extract phase does use its workers, but everything around it — the rclone localize, the
metadata read, the DuckDB mask, the upload — is single-threaded, so the average over the pod's
lifetime is far below the peak. Size on the mean plus headroom, not on what the hot loop can use.
An over-request throttles your own throughput, because the request decides how many pods a shared cluster can hold. At 32 Gi the Armada scheduler reported "4,231 jobs do not fit on any node". Aim for measured peak + ~50%, then re-measure.
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.
- 4d ago First seen · 216 lines · 80 tokens per session scan A c4ce2b3fd20c
hex-tuning is a skill published in the GitHub repository boettiger-lab/data-workflows (5 stars, last pushed 4d ago), licensed BSD-3-Clause. It adds 80 tokens to every session and 3,525 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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