hex-tuning

hex-tuning is a skill for Claude Code, Codex from boettiger-lab/data-workflows. It costs 80 tokens per session (3,525 once invoked), scanned A, original, BSD-3-Clause.

A technical guide for sizing H3 hex-generation jobs, where geographic data is divided into hexagonal map cells. It explains memory, chunking, resolution choices, and retrying failed work.

In plain words
What is it for?
Use it to measure resource use, choose H3 resolutions, configure vector-processing jobs, and reprocess failed chunks.
Why use it?
It helps prevent jobs from using too much memory and keeps generated cells compatible with other datasets.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/boettiger-lab/data-workflows/hex-tuning
Any agent
npx skills add boettiger-lab/data-workflows --skill hex-tuning
Clone the repo
git clone --depth 1 https://github.com/boettiger-lab/data-workflows

Made for: Claude Code, Codex.

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 hex-tuning

README.md
[![agentmods](https://agentmods.dev/badge/skills/boettiger-lab/data-workflows/hex-tuning.svg)](https://agentmods.dev/skills/boettiger-lab/data-workflows/hex-tuning)
Your own site
<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>
Per session 80 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,525 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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 $0.00080 $0.03525
Opus 5 $0.00040 $0.01762
Sonnet 5 $0.00016 $0.00705
Haiku 4.5 $0.00008 $0.00352

Measured 4d ago against content hash c4ce2b3fd20c, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

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.

.claude/skills/hex-tuning/SKILL.md · 216 lines

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 MEMORYcpu 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.

Read the full file on GitHub · 216 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. 4d ago First seen · 216 lines · 80 tokens per session scan A c4ce2b3fd20c

Subscribe to this mod's changes

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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