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 skills add SteadfastAsArt/geoscience-skills --skill climate-analysisgit clone --depth 1 https://github.com/SteadfastAsArt/geoscience-skillsWrote 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/steadfastasart/geoscience-skills/climate-analysis)<a href="https://agentmods.dev/skills/steadfastasart/geoscience-skills/climate-analysis"><img src="https://agentmods.dev/badge/skills/steadfastasart/geoscience-skills/climate-analysis/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/steadfastasart/geoscience-skills/climate-analysis"><img src="https://agentmods.dev/badge/skills/steadfastasart/geoscience-skills/climate-analysis.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 | $0.00051 | $0.01225 |
| Opus 5.5 | $0.00020 | $0.00490 |
| Sonnet 5.5 | $0.00010 | $0.00245 |
| Haiku 4.5 | $0.00005 | $0.00122 |
Grade A, and why
climate-analysis 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 1mo 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 — 112 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Climate Analysis
Use xarray for labelled climate arrays and verde only when scattered spatial
observations need interpolation. Existing regular grids do not require a new
interpolation stage. Run the relevant stages in one agent; companion skills are
helpful references rather than mandatory registrations.
Define the data contract
Identify the variable, physical units, spatial support, timestamp meaning, calendar, time bounds, missing-value encoding and source/version before combining files. Check coordinate uniqueness and order, longitude convention, latitude direction, projection, vertical level and ensemble/member dimensions. Preserve non-Gregorian calendars; do not silently coerce them into ordinary timestamps.
Distinguish an instantaneous value, interval mean, accumulated amount and rate.
For precipitation or energy fluxes, use the time bounds and unit definition to
decide integration or summation. Do not apply the same resampling operation to
temperature means and accumulated rainfall. Record source URLs, licences and
checksums; use the pooch skill only when downloading is actually requested.
Select the analysis
| Deliverable | Main steps | Validation partition |
|---|---|---|
| Climatology/anomalies | Specify baseline years, calendar grouping, coverage and weights | Document baseline overlap; no forecasting claim from descriptive anomalies |
| Regional averages | Use cell areas or justified spatial weights and a fixed mask | Check missing coverage and area/weight normalisation for each period |
| Station-to-grid product | Project coordinates appropriately, fit a spatial model, mask unsupported regions | Withhold spatial blocks or stations before fitting any preprocessing |
| Forecast/downscaling evaluation | Define issue time, lead time and predictor availability, then fit | Withhold future time periods and unseen locations as required by deployment |
Aggregate without losing meaning
Choose an explicit coverage rule for incomplete months/seasons and retain a coverage/count variable. Weight monthly means by represented duration, respecting the dataset's calendar; the xarray seasonal-mean example illustrates calendar-aware weighting. Renormalise over valid support only when that matches the stated estimand; never turn an entirely missing region or period into a zero observation.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 1mo ago First seen · 112 lines · 51 tokens per session scan A 881d19e0c462
climate-analysis is a skill published in the GitHub repository SteadfastAsArt/geoscience-skills (70 stars, last pushed 1mo ago), licensed MIT. It adds 51 tokens to every session and 1,225 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-09-16.
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