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 gprpygit 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/gprpy)<a href="https://agentmods.dev/skills/steadfastasart/geoscience-skills/gprpy"><img src="https://agentmods.dev/badge/skills/steadfastasart/geoscience-skills/gprpy/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/gprpy"><img src="https://agentmods.dev/badge/skills/steadfastasart/geoscience-skills/gprpy.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 | $0.00065 | $0.01175 |
| Opus 5.5 | $0.00026 | $0.00470 |
| Sonnet 5.5 | $0.00013 | $0.00235 |
| Haiku 4.5 | $0.00006 | $0.00118 |
Grade A, and why
gprpy 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 — 103 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Auditable GPR processing
GPRPy operates on arrays shaped (samples, traces). Inspect acquisition
metadata before assigning physical axes: a trace index is not distance, and a
radar vendor's SEG-Y interval field may need a documented time scale. Preserve
unknown units explicitly. Do not obtain a site velocity from a generic material
lookup and present it as measured.
Read and select operations
GPRPy 1.0.14 natively reads paired MALA .rad/.rd3, Sensors & Software
.DT1/.HD, GSSI .DZT, and its documented BSQ inputs. Native .gpr files are
Python pickle sessions, not a MALA exchange format; read only trusted sessions.
GPRPy does not natively read or export SEG-Y.
import gprpy.gprpy as gp
profile = gp.gprpyProfile()
profile.importdata('profile.rad') # requires matching profile.rd3
raw = profile.data.copy()
profile.dewow(window=64) # argument is sample count, not ns
Select operations to answer the user's question. dewow(window) and
agcGain(window) take sample counts; remMeanTrace(ntraces) takes traces.
Background subtraction may remove real continuous reflectors. Time-power gain
changes amplitude scaling and is not a calibrated attenuation correction.
Keep the raw arrays and record every applied operation. Window edge behaviour
and the effective interior support need particular care; see the
processing reference.
setVelocity(velocity) uses m/ns and computes the zero-offset constant-velocity
scenario depth = twtt * velocity / 2; it does not estimate velocity or migrate
reflectors. Resolve time zero and antenna-offset assumptions first. Retain
native two-way time alongside any assumed depth, and state which independent
measurements support velocity and its uncertainty.
Reproducible helper
The processing helper supports tested .rad/.rd3 and
.sgy/.segy routes. The latter explicitly uses segyio before real GPRPy
processing. It writes profile.npz and processing.json and reads them back;
NPZ loading uses allow_pickle=False. Outputs include raw amplitudes,
intermediate arrays, final amplitudes, original sample/trace indices, available
physical axes, source checksums, parameters and selected encoded header fields.
A new output directory prevents silent replacement of a previous result.
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 Changed · -61 lines · -61 tokens per session 27c74dc3a05d
- 1mo ago Changed · +2 lines · +1 tokens per session 2b120c4d6f1a
- 2mo ago First seen · 162 lines · 125 tokens per session scan A e7633e9a0627
gprpy is a skill published in the GitHub repository SteadfastAsArt/geoscience-skills (73 stars, last pushed 1mo ago), licensed MIT. It adds 65 tokens to every session and 1,175 once invoked, about $0.0003 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 2026-08-30.
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