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 OpenLAIR/OpenSkill --skill evo-seismic-data-preprocessinggit clone --depth 1 https://github.com/OpenLAIR/OpenSkillWrote 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/openlair/openskill/evo-seismic-data-preprocessing)<a href="https://agentmods.dev/skills/openlair/openskill/evo-seismic-data-preprocessing"><img src="https://agentmods.dev/badge/skills/openlair/openskill/evo-seismic-data-preprocessing/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/openlair/openskill/evo-seismic-data-preprocessing"><img src="https://agentmods.dev/badge/skills/openlair/openskill/evo-seismic-data-preprocessing.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 | $0.00060 | $0.00495 |
| Opus 5 | $0.00030 | $0.00247 |
| Sonnet 5 | $0.00012 | $0.00099 |
| Haiku 4.5 | $0.00006 | $0.00049 |
Grade A, and why
evo-seismic-data-preprocessing 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 yesterday.
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
evo-seismic-data-preprocessing
Overview
This skill handles all data I/O from .npz seismic trace files and transforms them into properly formatted ObsPy Stream objects with correct channel ordering (Z, N, E), sampling rate (100 Hz), and data types (float32).
Key Knowledge
- NPZ files contain: data (12000x3), dt (sampling interval), channels (comma-separated like "DPE,DPN,DPZ")
- SeisBench models expect Z, N, E channel order - must reorder from whatever order is in the file
- Channel codes ending in Z=Vertical, N=North, E=East
- Data must be float32 for PyTorch models
- Detrend (demean) before inference
- Resample to 100 Hz if not already at that rate
Usage
import sys
sys.path.insert(0, '/app/environment/skills/evo-seismic-data-preprocessing/scripts')
from utils import load_and_prepare_stream, load_npz_trace, build_obspy_stream, preprocess_stream, identify_channel_order
# Full pipeline
stream, trace_info = load_and_prepare_stream('/root/data/somefile.npz')
# Or step by step
trace_info = load_npz_trace('/root/data/somefile.npz')
z_idx, n_idx, e_idx = identify_channel_order(trace_info['channels'])
stream = build_obspy_stream(trace_info['data'], trace_info['dt'], trace_info['channels'])
stream = preprocess_stream(stream)
Functions
load_npz_trace(npz_path)- Load npz file, return dict of all fieldsidentify_channel_order(channels_str)- Parse channel string, return (z_idx, n_idx, e_idx)build_obspy_stream(trace_data, dt, channels_str, ...)- Build ObsPy Stream in ZNE orderpreprocess_stream(stream, target_rate=100.0)- Detrend and resampleload_and_prepare_stream(npz_path)- Full pipeline convenience function
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.
- yesterday First seen · 41 lines · 60 tokens per session scan A 0d1f72250280
evo-seismic-data-preprocessing is a skill published in the GitHub repository OpenLAIR/OpenSkill (90 stars, last pushed 2d ago), licensed Apache-2.0. It adds 60 tokens to every session and 495 once invoked, about $0.0003 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 2026-09-11.
Other skills, from other repositories
fba-simulator
Run Flux Balance Analysis (FBA) and related constraint-based simulations using COBRApy. Covers standard FBA, parsimonious FBA (pFBA), Flux Variability Analysis (FVA), loopless FBA, gene/reaction knockouts, and carbon source swapping. Outputs flux distributions and CSV files.
gsmm-validator
Validate a COBRApy genome-scale metabolic model for mass/charge balance, stoichiometric consistency, biomass producibility, dead-end metabolites, thermodynamic loops, and GPR rule formatting. Outputs a structured validation report with errors and warnings.
gsmm-builder
Build or load a genome-scale metabolic model (GSMM) using COBRApy. Covers loading from BIGG, constructing minimal models from scratch, setting medium constraints, and exporting validated .json model files.
stat-result-validator
Validate statistical research outputs for formulation quality, method-to- problem alignment, theory presence, experimental evidence, fair comparison, artifact completeness, and final-claim consistency.
statistical-theory-analysis
Analyze theoretical properties of statistical methods under the formal formulation: identifiability, bias, variance, consistency, asymptotics, coverage, error bounds, robustness, and limitations.
meta-analysis
Statistical methods for combining results across multiple studies. Use when aggregating cross-study or cross-experiment results.