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 thesecondfox/skill --skill bio-temporal-genomics-periodicity-detectiongit clone --depth 1 https://github.com/thesecondfox/skillWrote 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/thesecondfox/skill/bio-temporal-genomics-periodicity-detection)<a href="https://agentmods.dev/skills/thesecondfox/skill/bio-temporal-genomics-periodicity-detection"><img src="https://agentmods.dev/badge/skills/thesecondfox/skill/bio-temporal-genomics-periodicity-detection/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/thesecondfox/skill/bio-temporal-genomics-periodicity-detection"><img src="https://agentmods.dev/badge/skills/thesecondfox/skill/bio-temporal-genomics-periodicity-detection.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 | $0.00111 | $0.02464 |
| Opus 5 | $0.00056 | $0.01232 |
| Sonnet 5 | $0.00022 | $0.00493 |
| Haiku 4.5 | $0.00011 | $0.00246 |
Grade A, and why
bio-temporal-genomics-periodicity-detection 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 6d 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 — 261 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Version Compatibility
Reference examples tested with: matplotlib 3.8+, numpy 1.26+, pwr 1.3+, scipy 1.12+, statsmodels 0.14+
Before using code patterns, verify installed versions match. If versions differ:
- Python:
pip show <package>thenhelp(module.function)to check signatures
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
Periodicity Detection
"Find periodic patterns of unknown period in my time-series data" → Compute frequency spectra using Lomb-Scargle periodograms (handles irregular sampling), identify significant spectral peaks, and detect transient periodicity via continuous wavelet transforms.
- Python:
scipy.signal.lombscargle()for Lomb-Scargle periodogram - Python:
pywt.cwt()for wavelet time-frequency decomposition
Discovers periodic signals of unknown frequency in time-series omics data. Handles irregular sampling, identifies dominant oscillation periods, and detects transient or time-varying periodicity through spectral and time-frequency methods.
Core Workflow
- Prepare time-series expression data (handle missing values, detrend if needed)
- Compute frequency spectrum (Lomb-Scargle, Welch, or autocorrelation)
- Identify significant spectral peaks
- Assess statistical significance (false alarm probability, permutation FDR)
- For transient periodicity, apply wavelet time-frequency decomposition
Lomb-Scargle Periodogram (scipy)
Goal: Discover periodic signals of unknown frequency in time-series expression data, especially with irregular sampling.
Approach: Compute a Lomb-Scargle periodogram over a frequency grid spanning biologically plausible periods, identify statistically significant spectral peaks using false alarm probabilities, and convert peak frequencies to period estimates.
Handles irregularly sampled time series without interpolation. Standard method for astronomical and biological time-series with uneven sampling.
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.
- 6d ago First seen · 261 lines · 111 tokens per session scan A 60b583a3053f
bio-temporal-genomics-periodicity-detection is a skill published in the GitHub repository thesecondfox/skill (3 stars, last pushed 5mo ago), licensed MIT. It adds 111 tokens to every session and 2,464 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 2026-09-03.
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