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 PKU-YuanGroup/OpenAI4S --skill bio-data-visualization-sequence-logosgit clone --depth 1 https://github.com/PKU-YuanGroup/OpenAI4SWrote 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/pku-yuangroup/openai4s/bio-data-visualization-sequence-logos)<a href="https://agentmods.dev/skills/pku-yuangroup/openai4s/bio-data-visualization-sequence-logos"><img src="https://agentmods.dev/badge/skills/pku-yuangroup/openai4s/bio-data-visualization-sequence-logos/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/pku-yuangroup/openai4s/bio-data-visualization-sequence-logos"><img src="https://agentmods.dev/badge/skills/pku-yuangroup/openai4s/bio-data-visualization-sequence-logos.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 | $0.00088 | $0.03390 |
| Opus 5 | $0.00044 | $0.01695 |
| Sonnet 5 | $0.00018 | $0.00678 |
| Haiku 4.5 | $0.00009 | $0.00339 |
Grade A, and why
bio-data-visualization-sequence-logos 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.
This is a copy
95% identical to bio-data-visualization-sequence-logos — 12 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 286 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Version Compatibility
Reference examples tested with: ggseqlogo 0.2 (CRAN; per Wagih 2017), Logomaker 0.8+ (Python), WebLogo 3.7+ (CLI), Biopython 1.83+ (motif parsing), MEME suite 5.5+.
Before using code patterns, verify installed versions match. If versions differ:
- Python:
pip show <package>thenhelp(module.function)to check signatures - R:
packageVersion('<pkg>')then?function_name
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
Sequence Logos
"Plot a sequence motif" -> Render a per-position stack of letters whose total height encodes information content (Schneider-Stephens 1990 Nucleic Acids Res 18:6097) and individual letter height is proportional to base/aa frequency. The information-content encoding makes conserved positions visually tall and variable positions visually short — the visual is the conservation profile.
- R:
ggseqlogo::ggseqlogo(Wagih 2017 Bioinformatics 33:3645) - Python:
logomaker.Logo - CLI:
weblogo(Crooks 2004 Genome Res 14:1188)
The Single Most Important Modern Insight -- Bits vs Probability Are Different Visualizations
A sequence logo can encode each position as bits (information content) or probability (raw frequency). They look superficially similar; they communicate different things.
- Bits (Schneider-Stephens 1990): position height =
R = log2(K) − H(p)where K=4 for DNA, H is Shannon entropy. Maximum 2 bits for DNA, 4.3 bits for protein. A fully conserved position is 2 bits; a uniform position is 0. This is the canonical motif encoding. - Probability: position height = 1.0; letter height = frequency. Every position has the same total height. Cannot distinguish "conserved A" from "variable" — both can show 100% A at a position.
- EDLogo (enrichment-depletion): Dey et al. 2018 — uses log-odds of observed vs background, supporting depleted-residue display.
Default to bits unless a specific reason exists otherwise. Bits is what reviewers expect to see for a TF binding site, splice site, or CRISPR spacer composition.
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.
- 9d ago First seen · 286 lines · 88 tokens per session scan A 411a452b85e7
bio-data-visualization-sequence-logos is a skill published in the GitHub repository PKU-YuanGroup/OpenAI4S (407 stars, last pushed yesterday), licensed MIT. It adds 88 tokens to every session and 3,390 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 95% identical to bio-data-visualization-sequence-logos, differing in 12 lines, and is treated as a copy.
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