bio-applied-deep-learning-for-biology

bio-applied-deep-learning-for-biology is a skill for Claude Code, Codex from Pavel-Kravchenko/Bioinformatics. It costs 61 tokens per session (2,652 once invoked), scanned A, original, no licence file.

A guide for training PyTorch deep-learning models on DNA and protein sequences. It covers sequence encoding, pattern detection, model explanations, and several biology prediction tasks.

In plain words
What is it for?
Use it to classify sequences, predict transcription-factor binding sites, denoise single-cell RNA data, or compare deep learning with traditional machine learning.
Why use it?
It helps choose and apply neural-network methods to biological sequence data without designing the workflow from scratch.

Skill for Claude CodeCodex

Which agent this was written for is unclear — body not stored (licence); the path alone says nothing.

Good fit Use it to classify sequences, predict transcription-factor binding sites, denoise single-cell RNA data, or compare deep learning with traditional machine learning.

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Install with agentmods
npx agentmods add skills/pavel-kravchenko/bioinformatics/bio-applied-deep-learning-for-biology
Install

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.

Any agent
npx skills add Pavel-Kravchenko/Bioinformatics --skill bio-applied-deep-learning-for-biology
Clone the repo
git clone --depth 1 https://github.com/Pavel-Kravchenko/Bioinformatics

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for bio-applied-deep-learning-for-biology

README.md
[![agentmods](https://agentmods.dev/badge/skills/pavel-kravchenko/bioinformatics/bio-applied-deep-learning-for-biology/github.svg)](https://agentmods.dev/skills/pavel-kravchenko/bioinformatics/bio-applied-deep-learning-for-biology)
Your own site
<a href="https://agentmods.dev/skills/pavel-kravchenko/bioinformatics/bio-applied-deep-learning-for-biology"><img src="https://agentmods.dev/badge/skills/pavel-kravchenko/bioinformatics/bio-applied-deep-learning-for-biology/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.

agentmods 80×15 button for bio-applied-deep-learning-for-biology

Your own site · 80×15
<a href="https://agentmods.dev/skills/pavel-kravchenko/bioinformatics/bio-applied-deep-learning-for-biology"><img src="https://agentmods.dev/badge/skills/pavel-kravchenko/bioinformatics/bio-applied-deep-learning-for-biology.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 61 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,652 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin unknown No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00061 $0.02652
Opus 5 $0.00030 $0.01326
Sonnet 5 $0.00012 $0.00530
Haiku 4.5 $0.00006 $0.00265

Measured 9d ago against content hash c576f2b7d516, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

bio-applied-deep-learning-for-biology 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.

Skills/bio-applied-deep-learning-for-biology/SKILL.md · 226 lines

The source is not reproduced here

No licence file

A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.

Read it on GitHub

Changes

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.

  1. 9d ago First seen · 226 lines · 61 tokens per session scan A c576f2b7d516

Subscribe to this mod's changes

bio-applied-deep-learning-for-biology is a skill published in the GitHub repository Pavel-Kravchenko/Bioinformatics (5 stars, last pushed 2mo ago), with no licence file. It adds 61 tokens to every session and 2,652 once invoked, about $0.0003 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.