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 zongtingwei/Bioclaw_Skills_Hub --skill machine-learning-for-omicsgit clone --depth 1 https://github.com/zongtingwei/Bioclaw_Skills_HubWrote 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/zongtingwei/bioclaw_skills_hub/machine-learning-for-omics)<a href="https://agentmods.dev/skills/zongtingwei/bioclaw_skills_hub/machine-learning-for-omics"><img src="https://agentmods.dev/badge/skills/zongtingwei/bioclaw_skills_hub/machine-learning-for-omics/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/zongtingwei/bioclaw_skills_hub/machine-learning-for-omics"><img src="https://agentmods.dev/badge/skills/zongtingwei/bioclaw_skills_hub/machine-learning-for-omics.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 | $0.00027 | $0.00862 |
| Opus 5 | $0.00014 | $0.00431 |
| Sonnet 5 | $0.00005 | $0.00172 |
| Haiku 4.5 | $0.00003 | $0.00086 |
Grade A, and why
machine-learning-for-omics 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 13d 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 — 138 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Machine Learning For Omics
Version Compatibility
Reference examples assume recent stable releases of the preferred tools, especially scikit-learn and the other tools listed below.
Before using code or command patterns, verify installed versions match the environment:
- Python:
python -c "import <module>; print(<module>.__version__)" - CLI:
<tool> --version - If signatures differ, inspect the installed help or API and adapt the pattern instead of retrying unchanged.
Overview
Workflow for predictive modeling, biomarker discovery, survival modeling, and explainability over omics-derived features.
When To Use This Skill
- use when the task is supervised learning on omics features
- use when the user needs a model, validation metrics, and interpretable feature importance
- use when the modeling objective is biomarker discovery, classification, regression, or survival prediction
Quick Route
- If the input is raw or minimally processed data, start with validation and QC before any modeling.
- If the input is already processed, skip directly to the first workflow step that matches the user goal.
- If the user asks for a biological conclusion, always produce at least one QC or confidence artifact alongside the final result.
Progressive Disclosure
- Read
references/technical_reference.mdwhen you need deeper tool-selection rules, environment adaptation notes, or extra validation guidance. - Keep
SKILL.mdas the main execution path and load the reference file only when the task or failure mode needs the extra detail.
Default Rules
- Prefer Python-first workflows unless the task explicitly requires something else.
- Keep intermediate and final outputs separated.
- Record software versions, reference builds, and key parameters when they affect interpretation.
- Favor reproducible tables and figures over one-off interactive-only outputs.
Expected Inputs
- feature matrix
- labels or outcomes
- split or validation design
Expected Outputs
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.
- 13d ago First seen · 138 lines · 27 tokens per session scan A 0b93eac9d494
machine-learning-for-omics is a skill published in the GitHub repository zongtingwei/Bioclaw_Skills_Hub (26 stars, last pushed 5mo ago), licensed MIT. It adds 27 tokens to every session and 862 once invoked, about $0.0001 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 2026-08-30.
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