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 skills/wangyendt/wayne-skills/plotnpx skills add wangyendt/wayne-skills --skill plotgit clone --depth 1 https://github.com/wangyendt/wayne-skillsWhat 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 | $0.00087 | $0.01613 |
| Opus 5 | $0.00044 | $0.00807 |
| Sonnet 5 | $0.00017 | $0.00323 |
| Haiku 4.5 | $0.00009 | $0.00161 |
Grade A, and why
pywayne-plot 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 2d 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 — 225 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Pywayne Plot
Enhanced spectrogram visualization tools for professional time-frequency analysis.
Quick Start
import matplotlib.pyplot as plt
from pywayne.plot import regist_projection, parula_map
import numpy as np
# Register custom projection
regist_projection()
# Create spectrogram
fig, ax = plt.subplots(subplot_kw={'projection': 'z_norm'})
spec, freqs, t, im = ax.specgram(
x=signal_data,
Fs=100,
NFFT=128,
noverlap=96,
cmap=parula_map,
scale='dB'
)
ax.set_ylabel('Frequency (Hz)')
plt.colorbar(im, label='Magnitude (dB)')
plt.show()
Functions
regist_projection
Register the custom SpecgramAxes projection. Must be called before using the enhanced specgram functionality.
from pywayne.plot import regist_projection
regist_projection()
SpecgramAxes.specgram
Enhanced spectrogram with advanced features.
Key Parameters:
| Parameter | Description | Default |
|---|---|---|
NFFT |
FFT window length (points) | 256 |
Fs |
Sampling frequency (Hz) | 2 |
noverlap |
Overlap points between windows | 128 |
cmap |
Colormap (use parula_map) |
- |
mode |
'psd', 'magnitude', 'angle', 'phase' | 'psd' |
scale |
'dB' or 'linear' | 'dB' |
normalize |
'global', 'local', 'none' | 'global' |
freq_scale |
Frequency scaling factor | 1.0 |
Fc |
Center frequency offset (Hz) | 0 |
Returns:
spec- 2D spectrogram array (n_freqs, n_times)freqs- Frequency axis arrayt- Time axis arrayim- matplotlib image object (for colorbar)
get_specgram_params
Auto-recommend STFT parameters based on signal characteristics.
from pywayne.plot import get_specgram_params
params = get_specgram_params(
signal_length=10000,
sampling_rate=100,
time_resolution=0.1 # or freq_resolution=0.5
)
# Returns: NFFT, noverlap, actual_freq_res, actual_time_res, n_segments
parula_map
MATLAB-style perceptually uniform colormap for scientific visualization.
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.
- 2d ago First seen · 225 lines · 87 tokens per session scan A 5124b1d2ebb8
pywayne-plot is a skill published in the GitHub repository wangyendt/wayne-skills (8 stars, last pushed 5d ago), licensed MIT. It adds 87 tokens to every session and 1,613 once invoked, about $0.0004 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 2026-08-31.
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