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-comparative-genomics-comparative-annotation-projectiongit 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-comparative-genomics-comparative-annotation-projection)<a href="https://agentmods.dev/skills/pku-yuangroup/openai4s/bio-comparative-genomics-comparative-annotation-projection"><img src="https://agentmods.dev/badge/skills/pku-yuangroup/openai4s/bio-comparative-genomics-comparative-annotation-projection/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-comparative-genomics-comparative-annotation-projection"><img src="https://agentmods.dev/badge/skills/pku-yuangroup/openai4s/bio-comparative-genomics-comparative-annotation-projection.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 | $0.00196 | $0.06790 |
| Opus 5 | $0.00098 | $0.03395 |
| Sonnet 5 | $0.00039 | $0.01358 |
| Haiku 4.5 | $0.00020 | $0.00679 |
Grade A, and why
bio-comparative-genomics-comparative-annotation-projection scanned grade A with 1 finding 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
wget https://gemoma.de/jcag/gemoma.zip && unzip gemoma.zip This is a copy
95% identical to bio-comparative-genomics-comparative-annotation-projection — 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 — 432 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Version Compatibility
Reference examples tested with: TOGA 1.1.7+ (hillerlab/TOGA; Kirilenko 2023 Science 380:eabn3107), CESAR 2.0 (Sharma, Schwede & Hiller 2017 Bioinformatics 33:3985), LiftOff 1.6.3+ (Shumate & Salzberg 2021 Bioinformatics 37(12):1639-1643), Comparative Annotation Toolkit (CAT) 2.4+, GeMoMa 1.9+ (Keilwagen 2019 Methods Mol Biol 1962:161), UCSC liftOver 2024+, Cactus 2.9.1+ (for HAL input), HAL toolkit 2.3+, NextFlow 24+ for TOGA pipeline, BUSCO 5.7+ / Compleasm 0.2.7+ for QC, Luigi + Toil for CAT, R 4.4+. The current TOGA expects HAL from Cactus 2.5+; older HAL formats may fail.
Before using code patterns, verify installed versions match. If versions differ:
- CLI:
toga.py --help;cesar --help;liftoff --version - Python:
pip show liftoff - Java:
gemoma --help(Java 11+)
If code throws TOGA chain file missing, CESAR fragment not found, LiftOff annotation not parsed, the toolchain expects specific input formats: TOGA needs HAL or chain/net files from Cactus / LASTZ; CESAR needs exon-level GFF; LiftOff needs reference GFF and aligned FASTA. Pre-process with the appropriate format conversion.
Comparative Annotation Projection
"Annotate this new genome using my well-annotated reference" -> Annotation projection from a reference is the modern alternative to de novo gene prediction; it produces high-quality, comparable annotations across genomes by leveraging evolutionary conservation. The 2023-era standard is TOGA + CESAR 2.0 (Kirilenko 2023 Science 380:eabn3107), which uses whole-genome alignment chains + ML classification + codon-aware exon projection to scale to hundreds of genomes (Zoonomia: 488 mammals; Bird10000 Genomes: 501 birds). For ortholog-based projection (no WGA needed), LiftOff (Shumate & Salzberg 2021 Bioinformatics 37(12):1639) is the standard. The critical decision is WGA-anchored (TOGA) vs ortholog-anchored (LiftOff): TOGA explicitly classifies gene intactness vs loss using the alignment chains, LiftOff relies on reciprocal-best-hit equivalents.
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 · 432 lines · 196 tokens per session scan A 85ed3b6ac1e3
bio-comparative-genomics-comparative-annotation-projection is a skill published in the GitHub repository PKU-YuanGroup/OpenAI4S (407 stars, last pushed yesterday), licensed MIT. It adds 196 tokens to every session and 6,790 once invoked, about $0.0010 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). It is 95% identical to bio-comparative-genomics-comparative-annotation-projection, differing in 12 lines, and is treated as a copy.
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