SkillsBench is a benchmark for measuring how effectively AI agents use modular skills—folders containing instructions, scripts, and resources—to complete specialized tasks. It helps researchers and developers evaluate both skill quality and agent behavior, including tasks that require combining multiple skills. The catalogue’s skills and instructions are evaluated as part of this workflow.
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 benchflow-ai/skillsbench --skill obspy-data-apigit clone --depth 1 https://github.com/benchflow-ai/skillsbenchWrote 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/benchflow-ai/skillsbench/obspy-data-api)<a href="https://agentmods.dev/skills/benchflow-ai/skillsbench/obspy-data-api"><img src="https://agentmods.dev/badge/skills/benchflow-ai/skillsbench/obspy-data-api.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.01509 |
| Opus 5 | $0.00032 | $0.00754 |
| Sonnet 5 | $0.00013 | $0.00302 |
| Haiku 4.5 | $0.00006 | $0.00151 |
Grade A, and why
obspy-data-api 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- obspy-data-api — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 130 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ObsPy Data API
Waveform Data
Summary
Seismograms of various formats (e.g. SAC, MiniSEED, GSE2, SEISAN, Q, etc.) can be imported into a Stream object using the read() function.
Streams are list-like objects which contain multiple Trace objects, i.e. gap-less continuous time series and related header/meta information.
Each Trace object has the attribute data pointing to a NumPy ndarray of the actual time series and the attribute stats which contains all meta information in a dict-like Stats object. Both attributes starttime and endtime of the Stats object are UTCDateTime objects.
A multitude of helper methods are attached to Stream and Trace objects for handling and modifying the waveform data.
Stream and Trace Class Structure
Hierarchy: Stream → Trace (multiple)
Trace - DATA:
data→ NumPy arraystats:network,station,location,channel— Determine physical location and instrumentstarttime,sampling_rate,delta,endtime,npts— Interrelated
Trace - METHODS:
taper()— Tapers the data.filter()— Filters the data.resample()— Resamples the data in the frequency domain.integrate()— Integrates the data with respect to time.remove_response()— Deconvolves the instrument response.
Example
A Stream with an example seismogram can be created by calling read() without any arguments. Local files can be read by specifying the filename, files stored on http servers (e.g. at https://examples.obspy.org) can be read by specifying their URL.
>>> from obspy import read
>>> st = read()
>>> print(st)
3 Trace(s) in Stream:
BW.RJOB..EHZ | 2009-08-24T00:20:03.000000Z - ... | 100.0 Hz, 3000 samples
BW.RJOB..EHN | 2009-08-24T00:20:03.000000Z - ... | 100.0 Hz, 3000 samples
BW.RJOB..EHE | 2009-08-24T00:20:03.000000Z - ... | 100.0 Hz, 3000 samples
>>> tr = st[0]
>>> print(tr)
BW.RJOB..EHZ | 2009-08-24T00:20:03.000000Z - ... | 100.0 Hz, 3000 samples
>>> tr.data
array([ 0. , 0.00694644, 0.07597424, ..., 1.93449584,
0.98196204, 0.44196924])
>>> print(tr.stats)
network: BW
station: RJOB
location:
channel: EHZ
starttime: 2009-08-24T00:20:03.000000Z
endtime: 2009-08-24T00:20:32.990000Z
sampling_rate: 100.0
delta: 0.01
npts: 3000
calib: 1.0
...
>>> tr.stats.starttime
UTCDateTime(2009, 8, 24, 0, 20, 3)
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 · 130 lines · 65 tokens per session scan A 669584da3b2f
obspy-data-api is a skill published in the GitHub repository benchflow-ai/skillsbench (1,748 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 65 tokens to every session and 1,509 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 2026-09-03.
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