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 GPTomics/bioSkills --skill timeseries-degit clone --depth 1 https://github.com/GPTomics/bioSkillsWrote 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/gptomics/bioskills/timeseries-de)<a href="https://agentmods.dev/skills/gptomics/bioskills/timeseries-de"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/timeseries-de/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/gptomics/bioskills/timeseries-de"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/timeseries-de.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 | $0.00166 | $0.05792 |
| Opus 5 | $0.00083 | $0.02896 |
| Sonnet 5 | $0.00033 | $0.01158 |
| Haiku 4.5 | $0.00017 | $0.00579 |
Grade A, and why
bio-differential-expression-timeseries-de 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 8d 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 — 369 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Version Compatibility
Reference examples tested with: DESeq2 1.42+, edgeR 4.0+, limma 3.58+, splines (base R), maSigPro 1.74+, ImpulseDE2 1.10+ (Bioconductor archive; verify availability), variancePartition / dream 1.32+, Mfuzz 2.62+, TCseq 1.26+, ggplot2 3.5+, pheatmap 1.0+
Before using code patterns, verify installed versions match. If versions differ:
- R:
packageVersion('<pkg>')then?function_nameto verify parameters
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
Time-Series Differential Expression
"Find genes that change over time" -> Define what "change" means -- any non-zero time effect (LRT), a smooth nonlinear trend (splines), a transient impulse (ImpulseDE2), or differing trajectories between groups (interaction LRT) -- and choose the model that asks that specific question while handling repeated-measures correctly.
The Single Most Important Modern Insight -- Most dedicated time-course tools UNDERPERFORM pairwise + LRT on short series
Spies, Renz, Beyer, Ciaudo 2019 Brief Bioinform 20:288 benchmarked dedicated time-course tools (ImpulseDE2, splineTC, maSigPro, EBSeqHMM, TimeReg) against naive DESeq2/edgeR pairwise comparisons + LRT for omnibus, on simulated and real time-courses. Finding: on short series (<8 time points), naive pairwise pattern-of-significance OUTPERFORMS dedicated TC tools because of high false-positive rates in the latter. The exception is ImpulseDE2, which holds up better than the others -- IF its impulse assumption (rise-then-plateau or fall-then-plateau) actually fits the biology.
For most experimental time courses (3-6 time points, common in pharmacology and developmental biology), the right tool is DESeq2 with test='LRT' and a sensible reduced model. Reserve splines for >5 evenly-spaced time points; reserve ImpulseDE2 for monotonic-then-saturating dynamics; reserve DREAM for repeated measures.
What ships with it
2 files 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.
- 8d ago First seen · 369 lines · 166 tokens per session scan A 7801a185317a
bio-differential-expression-timeseries-de is a skill published in the GitHub repository GPTomics/bioSkills (1,201 stars, last pushed 27d ago), licensed MIT. It adds 166 tokens to every session and 5,792 once invoked, about $0.0008 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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