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-clip-seq-clip-deep-learninggit 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-clip-seq-clip-deep-learning)<a href="https://agentmods.dev/skills/pku-yuangroup/openai4s/bio-clip-seq-clip-deep-learning"><img src="https://agentmods.dev/badge/skills/pku-yuangroup/openai4s/bio-clip-seq-clip-deep-learning/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-clip-seq-clip-deep-learning"><img src="https://agentmods.dev/badge/skills/pku-yuangroup/openai4s/bio-clip-seq-clip-deep-learning.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 | $0.00123 | $0.04715 |
| Opus 5 | $0.00062 | $0.02357 |
| Sonnet 5 | $0.00025 | $0.00943 |
| Haiku 4.5 | $0.00012 | $0.00471 |
Grade A, and why
bio-clip-seq-clip-deep-learning 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.
This is a copy
98% identical to bio-clip-seq-clip-deep-learning — 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 — 324 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Version Compatibility
Reference examples tested with: RBPNet (Horlacher et al 2023 github), RNAProt 0.5+, GraphProt2 (Uhl et al 2021 github), DeepCLIP 1.0+ (Gronning 2020), DeepRiPe (Ohler lab), pytorch 2.2+, tensorflow 2.15+, scikit-learn 1.4+, biopython 1.83+, transformers 4.40+ (for RNA foundation models).
Before using code patterns, verify installed versions match. If versions differ:
- Python:
pip show <package>thenhelp(module.function)to check signatures - Frameworks: check pytorch / tensorflow versions; reproducibility depends on framework version
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
CLIP-seq Deep Learning
"Predict RBP binding from RNA sequence using deep learning" -> Train or apply neural networks that learn the sequence (and optionally structure) preference of an RBP from CLIP-seq peaks or single-nucleotide crosslink sites. The output is per-base or per-site binding probability for any input sequence, enabling: (a) variant-effect prediction at heterozygous SNPs; (b) in silico binding-site discovery on transcripts not covered by CLIP; (c) systematic comparison across RBPs via shared model architectures; (d) interpretation via attribution / saliency to recover RBP-specific motifs and structural preferences. Modern models (RBPNet 2023) predict per-nucleotide crosslink count distributions rather than binary peak/non-peak, providing single-nt resolution outputs.
- Python (RBPNet sequence-to-CL signal):
import rbpnet; model = rbpnet.load_pretrained('RBP_name'); predictions = model.predict(sequence)produces per-base CL count distribution - Python (RNAProt RNN classifier):
RNAProt train -i peaks.bed -t background.bed -g genome.fa -o model/thenRNAProt predict -m model/ -i query_sequences.fa -o predictions.tsv - Python (GraphProt2 GCN with structure):
graphprot2 train -i peaks.bed -bg shuffled.bed -g genome.fa --structure -o model/ - Python (DeepCLIP for binding probability):
deepclip --train --train_data train.fa --validation_data val.fa --predict --predict_data test.fa --output_dir output/ - Python (DeepRiPe multi-modal CNN):
from deepripe import DeepRiPe; model.train(X_train, y_train); predictions = model.predict(X_test)
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 · 324 lines · 123 tokens per session scan A f5f6207cb366
bio-clip-seq-clip-deep-learning is a skill published in the GitHub repository PKU-YuanGroup/OpenAI4S (403 stars, last pushed yesterday), licensed MIT. It adds 123 tokens to every session and 4,715 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. It is 98% identical to bio-clip-seq-clip-deep-learning, differing in 12 lines, and is treated as a copy.
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