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 commands/steadfastasart/geoscience-skills/inversion-workflowgit 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/commands/steadfastasart/geoscience-skills/inversion-workflow)<a href="https://agentmods.dev/commands/steadfastasart/geoscience-skills/inversion-workflow"><img src="https://agentmods.dev/badge/commands/steadfastasart/geoscience-skills/inversion-workflow.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 | $0.00014 | $0.00530 |
| Opus 5 | $0.00007 | $0.00265 |
| Sonnet 5 | $0.00003 | $0.00106 |
| Haiku 4.5 | $0.00001 | $0.00053 |
Grade A, and why
inversion-workflow 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 — 64 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Geophysical Inversion Workflow
Guide the user through a geophysical inversion pipeline. Determine the appropriate skill chain based on survey type and inversion goals.
Decision Tree
-
What survey type?
- Electrical Resistivity Tomography (ERT) → Use
pygimliorsimpegskill - Magnetics / Gravity → Use
simpegskill withharmonicafor processing - Electromagnetic (EM), IP, or multi-physics → Use
simpegskill - Seismic Refraction Tomography (SRT) → Use
pygimliskill
- Electrical Resistivity Tomography (ERT) → Use
-
Which framework fits your problem?
- Multi-physics, large-scale, flexible regularization → Use
simpegskill - Near-surface ERT/SRT, streamlined API → Use
pygimliskill
- Multi-physics, large-scale, flexible regularization → Use
-
Post-processing and visualization?
- Grid scattered inversion results → Use
verdeskill - 3D model rendering → Use
pyvistaskill
- Grid scattered inversion results → Use
Skill Chain
simpeg (multi-physics inversion) → verde (gridding results) → pyvista (3D viz)
pygimli (ERT/SRT inversion) → verde (gridding results) → pyvista (3D viz)
harmonica (gravity/mag processing) → simpeg (inversion)
Step Prompts
For each step, invoke the relevant domain skill and follow its guidance.
Step 1: Data Loading
- Load survey data (electrode positions, observations, uncertainties)
- Assign data uncertainties (absolute or percentage)
- Inspect data quality and remove outliers
Step 2: Mesh Construction
- Build appropriate mesh (tensor, tree, or unstructured)
- Refine mesh near electrodes or survey points
- Set mesh padding for boundary conditions
Step 3: Forward Modelling
- Define starting model and physical property mapping
- Run forward simulation to verify data fit
- Compare predicted vs observed data
Step 4: Inversion
- Configure objective function and regularization
- Set convergence criteria (target misfit, max iterations)
- Run inversion and monitor convergence
Step 5: Post-Processing and Visualization
- Extract recovered model on mesh
- Grid results to regular grid with verde if needed
- Render 3D inversion results with pyvista
- Evaluate depth of investigation and model reliability
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 · 64 lines · 14 tokens per session scan A d5fc55b5b2eb
inversion-workflow is a command published in the GitHub repository SteadfastAsArt/geoscience-skills (56 stars, last pushed 5mo ago), licensed MIT. It adds 14 tokens to every session and 530 once invoked, about $0.0001 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 2026-08-30.
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