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 flopygit 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/flopy)<a href="https://agentmods.dev/skills/steadfastasart/geoscience-skills/flopy"><img src="https://agentmods.dev/badge/skills/steadfastasart/geoscience-skills/flopy/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/flopy"><img src="https://agentmods.dev/badge/skills/steadfastasart/geoscience-skills/flopy.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 | $0.00052 | $0.00734 |
| Opus 5.5 | $0.00021 | $0.00294 |
| Sonnet 5.5 | $0.00010 | $0.00147 |
| Haiku 4.5 | $0.00005 | $0.00073 |
Grade A, and why
flopy 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 — 68 lines — stays where its author put it; the contents beside it link to each section on GitHub.
FloPy
Produce a reproducible groundwater model and a checked water budget. FloPy writes and reads MODFLOW files; solving also requires a compatible MODFLOW executable.
Inputs and choices
- Establish horizontal CRS, vertical datum, length/time units, layer tops/bottoms, active cells, hydraulic properties, stresses and observation uncertainties.
- Head is an elevation relative to a datum. Depth below a well collar must first be converted with the collar elevation; a LAS depth curve is not a head series.
- Choose steady or transient conditions from the question and stress history. Distinguish hydraulic conductivity (length/time), transmissivity (length²/time), recharge (length/time) and pumping (length³/time; extraction is negative).
- Keep MODFLOW 6 packages separate from older MODFLOW model/package APIs. Use an isolated project environment and record the executable version and origin.
Workflow
- Audit geometry, layer ordering and boundary coverage before assigning values.
- Build the simulation, temporal discretization, solver, flow model and packages. Specify convertible/confined cells deliberately, with storage for transients.
- Write inputs, run the declared executable, and fail if it reports failure.
- Inspect head/budget files and listing convergence; mask dry/inactive sentinel values before statistics. Do not treat a successful process as calibration.
- Compare observations with uncertainties, inspect budget discrepancy and run grid/time-step sensitivity. Hold out wells or periods before tuning parameters.
Small executable benchmark
Run scripts/confined_flow.py in an empty output folder:
python confined_flow.py --workspace ./flow-check --exe /path/to/mf6
The script models a synthetic confined strip: 11 cells, each 10 m long and 10 m wide, 20 m thick, K = 1 m/day, with fixed heads of 10 and 9 m at the end cell centres. The 100 m separation implies a linear head profile and a flow of 2 m³/day. The layer spans elevations -20 to 0 m relative to an arbitrary local datum, so both imposed heads are above the aquifer top. It checks the head profile and flow against actual MODFLOW output. No recharge, pumping, storage or heterogeneous calibration is implied by this benchmark.
What ships with it
1 file 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 · 68 lines · 52 tokens per session scan A 5f10cc466ec0
flopy is a skill published in the GitHub repository SteadfastAsArt/geoscience-skills (70 stars, last pushed 1mo ago), licensed MIT. It adds 52 tokens to every session and 734 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.
Other skills, from other repositories
glm-lake-mendota
Simulate vertical water temperature profiles for lakes using the General Lake Model (GLM), calibrate key parameters to minimize RMSE against field observations, and produce validated NetCDF output.
obspy-seismology
Seismological data analysis with ObsPy — FDSN waveform download, response removal, phase picking, moment tensor inversion, and seismicity mapping.
xarray-netcdf
Labeled multi-dimensional array analysis with xarray: NetCDF/HDF5 I/O, lazy Dask loading, rechunking, Zarr stores, and CF conventions.
soil-data
Soil data analysis via SoilGrids 2.0 REST API and SSURGO: SOC stocks, texture classification, kriging interpolation, and soil profile visualization.
instrument-data-to-allotrope
Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full…
exploratory-data-analysis
Performs bounded, local exploratory analysis of explicitly supported scientific files. Supports redacted CSV/TSV/JSON profiles; optional NumPy, HDF5, FASTA/FASTQ, and basic image metadata inspection; missingness/leakage audits; outlier and transformation sensitivity; and rigorous EDA report scaffolds. Other domain…