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.
git 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/well-analysis)<a href="https://agentmods.dev/commands/steadfastasart/geoscience-skills/well-analysis"><img src="https://agentmods.dev/badge/commands/steadfastasart/geoscience-skills/well-analysis.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 5.1 | $0.00016 | $0.00501 |
| Opus 5 | $0.00008 | $0.00251 |
| Sonnet 5 | $0.00003 | $0.00100 |
| Haiku 4.5 | $0.00002 | $0.00050 |
Grade A, and why
well-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 7d 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.
What it actually says
Well Log Analysis Workflow
Guide the user through a well log analysis pipeline. Determine the appropriate skill chain based on their data format and analysis goals.
Decision Tree
-
What format is the well data?
- LAS (.las) → Use
lasioskill for loading - DLIS (.dlis) → Use
dlisioskill for loading - CSV / already loaded → Skip to processing with
welly
- LAS (.las) → Use
-
What analysis is needed?
- Multi-well management, curve QC → Use
wellyskill - Petrophysics (porosity, saturation, cutoffs) → Use
petropyskill - Lithology / stratigraphy intervals → Use
striplogskill
- Multi-well management, curve QC → Use
-
Visualization needs?
- Log plots, cross-plots → matplotlib via
welly - 3D wellbore trajectories → Use
pyvistaskill
- Log plots, cross-plots → matplotlib via
Skill Chain
lasio/dlisio (load) → welly (manage/QC) → petropy (petrophysics) → striplog (lithology)
↘ pyvista (3D viz)
Step Prompts
For each step, invoke the relevant domain skill and follow its guidance.
Step 1: Data Loading
- Load well log files and inspect available curves
- Check header metadata (well name, depth units, null values)
- Validate depth range and sampling interval
Step 2: Quality Control
- Identify missing or null values in key curves
- Check curve mnemonics and units consistency
- Merge or splice curves from multiple runs if needed
Step 3: Petrophysics
- Compute shale volume (Vsh) from GR or SP
- Calculate porosity (density, neutron, sonic methods)
- Estimate water saturation (Archie or dual-water)
- Apply net pay cutoffs
Step 4: Lithology Classification
- Define lithology intervals from log response
- Build striplog from interpreted zones
- Correlate across multiple wells
Step 5: Visualization
- Log plots with tracks (GR, resistivity, porosity, saturation)
- Cross-plots for mineral identification
- 3D wellbore rendering with property logs
$ARGUMENTS
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.
- 7d ago First seen · 63 lines · 16 tokens per session scan A 7373b48462f2
well-analysis is a command published in the GitHub repository SteadfastAsArt/geoscience-skills (57 stars, last pushed 5mo ago), licensed MIT. It adds 16 tokens to every session and 501 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.
Other commands, from other repositories
diff
Quantitative volume comparison between a CadQuery model and a reference STEP file.
graphite-morphology-classify
Classify graphite in a cast-iron micrograph per ASTM A247 / ISO 945-1, quantify nodularity, and read the matrix — the single most diagnostic observation in a cast-iron case.
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.