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 lomb-scargle-periodogramgit 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/lomb-scargle-periodogram)<a href="https://agentmods.dev/skills/benchflow-ai/skillsbench/lomb-scargle-periodogram"><img src="https://agentmods.dev/badge/skills/benchflow-ai/skillsbench/lomb-scargle-periodogram/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/benchflow-ai/skillsbench/lomb-scargle-periodogram"><img src="https://agentmods.dev/badge/skills/benchflow-ai/skillsbench/lomb-scargle-periodogram.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00072 | $0.00869 |
| Opus 5 | $0.00036 | $0.00434 |
| Sonnet 5 | $0.00014 | $0.00174 |
| Haiku 4.5 | $0.00007 | $0.00087 |
Grade A, and why
lomb-scargle-periodogram 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 9d 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:
- lomb-scargle-periodogram — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 113 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Lomb-Scargle Periodogram
The Lomb-Scargle periodogram is the standard tool for finding periods in unevenly sampled astronomical time series data. It's particularly useful for detecting periodic signals in light curves from space missions like Kepler, K2, and TESS.
Overview
The Lomb-Scargle periodogram extends the classical periodogram to handle unevenly sampled data, which is common in astronomy due to observing constraints, data gaps, and variable cadences.
Basic Usage with Lightkurve
import lightkurve as lk
import numpy as np
# Create a light curve object
lc = lk.LightCurve(time=time, flux=flux, flux_err=error)
# Create periodogram (specify maximum period to search)
pg = lc.to_periodogram(maximum_period=15) # Search up to 15 days
# Find strongest period
strongest_period = pg.period_at_max_power
max_power = pg.max_power
print(f"Strongest period: {strongest_period:.5f} days")
print(f"Power: {max_power:.5f}")
Plotting Periodograms
import matplotlib.pyplot as plt
pg.plot(view='period') # View vs period (not frequency)
plt.xlabel('Period [days]')
plt.ylabel('Power')
plt.show()
Important: Use view='period' to see periods directly, not frequencies. The default view='frequency' shows frequency (1/period).
Period Range Selection
Choose appropriate period ranges based on your science case:
- Stellar rotation: 0.1 - 100 days
- Exoplanet transits: 0.5 - 50 days (most common)
- Eclipsing binaries: 0.1 - 100 days
- Stellar pulsations: 0.001 - 1 day
# Search specific period range
pg = lc.to_periodogram(minimum_period=2.0, maximum_period=7.0)
Interpreting Results
Power Significance
Higher power indicates stronger periodic signal, but be cautious:
- High power: Likely real periodic signal
- Multiple peaks: Could indicate harmonics (period/2, period*2)
- Aliasing: Very short periods may be aliases of longer periods
Common Patterns
- Single strong peak: Likely the true period
- Harmonics: Peaks at period/2, period*2 suggest the fundamental period
- Aliases: Check if period*2 or period/2 also show signals
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.
- 9d ago First seen · 113 lines · 72 tokens per session scan A 9c99004068e8
lomb-scargle-periodogram is a skill published in the GitHub repository benchflow-ai/skillsbench (1,764 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 72 tokens to every session and 869 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 2026-09-03.
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