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 dlisiogit 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/dlisio)<a href="https://agentmods.dev/skills/steadfastasart/geoscience-skills/dlisio"><img src="https://agentmods.dev/badge/skills/steadfastasart/geoscience-skills/dlisio/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/dlisio"><img src="https://agentmods.dev/badge/skills/steadfastasart/geoscience-skills/dlisio.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 | $0.00115 | $0.01371 |
| Opus 5 | $0.00057 | $0.00685 |
| Sonnet 5 | $0.00023 | $0.00274 |
| Haiku 4.5 | $0.00012 | $0.00137 |
Grade A, and why
dlisio 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 10d 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 — 180 lines — stays where its author put it; the contents beside it link to each section on GitHub.
dlisio - DLIS/LIS File Reader
Quick Reference
import dlisio
# Open DLIS file (returns generator of logical files)
with dlisio.dlis.load('well.dlis') as (f, *rest):
frame = f.frames[0]
curves = frame.curves()
# Access by channel name
depth = curves['DEPTH']
gr = curves['GR']
# File metadata
for origin in f.origins:
print(origin.well_name, origin.field_name)
Key Classes
| Class | Purpose |
|---|---|
PhysicalFile |
Container returned by dlis.load() |
LogicalFile |
Independent dataset within physical file |
Frame |
Group of channels with common sampling |
Channel |
Individual log curve with metadata |
Origin |
Well and file metadata |
Essential Operations
Read Curves to DataFrame
import pandas as pd
with dlisio.dlis.load('well.dlis') as (f, *_):
frame = f.frames[0]
curves = frame.curves()
df = pd.DataFrame(curves)
df.set_index('DEPTH', inplace=True)
Access Channel and Origin Metadata
with dlisio.dlis.load('well.dlis') as (f, *_):
# Origin metadata
for origin in f.origins:
print(f"Well: {origin.well_name}, Field: {origin.field_name}")
# Channel properties
for ch in f.frames[0].channels:
print(f"{ch.name}: {ch.units}, dim={ch.dimension}")
Find Channels Across Frames
with dlisio.dlis.load('well.dlis') as (f, *_):
# By exact name or regex
channels = f.find('CHANNEL', '.*GR.*', regex=True)
# Find frame containing specific channel
for frame in f.frames:
if 'GR' in [ch.name for ch in frame.channels]:
curves = frame.curves()
break
Handle Array Channels
with dlisio.dlis.load('well.dlis') as (f, *_):
curves = f.frames[0].curves()
for name, data in curves.items():
if data.ndim > 1:
print(f"{name}: shape = {data.shape}") # Image/waveform
Common Object Types
| Object Type | Description |
|---|---|
| ORIGIN | File/well metadata |
| FRAME | Channel grouping with index |
| CHANNEL | Log curve definition |
| TOOL | Logging tool info |
| PARAMETER | Constants and settings |
What ships with it
3 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.
- 10d ago First seen · 180 lines · 115 tokens per session scan A 2a37c6b559b4
dlisio is a skill published in the GitHub repository SteadfastAsArt/geoscience-skills (58 stars, last pushed 5mo ago), licensed MIT. It adds 115 tokens to every session and 1,371 once invoked, about $0.0006 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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