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 agentmods add skills/fmschulz/omics-skills/notebooksnpx skills add fmschulz/omics-skills --skill notebooksgit clone --depth 1 https://github.com/fmschulz/omics-skillsWrote 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/fmschulz/omics-skills/notebooks)<a href="https://agentmods.dev/skills/fmschulz/omics-skills/notebooks"><img src="https://agentmods.dev/badge/skills/fmschulz/omics-skills/notebooks.svg" alt="Measured on agentmods" 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 | $0.00034 | $0.03600 |
| Opus 5 | $0.00017 | $0.01800 |
| Sonnet 5 | $0.00007 | $0.00720 |
| Haiku 4.5 | $0.00003 | $0.00360 |
Grade A, and why
notebooks 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 4d 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 — 233 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Notebooks
A single skill for authoring, validating, and delivering reproducible analysis notebooks. Marimo is the default format; Jupyter is supported for existing .ipynb files and when a downstream tool requires JSON. Conversion between the two formats is part of this skill.
A notebook is not "done" until it has been executed end-to-end on a fresh kernel and every figure is embedded in the delivered file.
Instructions
-
Pick the format.
- New notebook: write a marimo
.pynotebook. Use the canonical cell layout (one concept per cell, final expression renders, noifguards around outputs, notry/exceptfor control flow). - Existing
.ipynbto extend or polish: keep it as Jupyter unless the user asks to convert. - Conversion: see "Convert between marimo and Jupyter" below.
- New notebook: write a marimo
-
Outline before coding. Write the notebook plan (purpose, data sources, analysis steps, expected outputs/plots) as the first markdown cell, then implement against that plan.
-
Keep marimo cells clean. These are hard rules for every
.pynotebook:- Markdown cells use one plain triple-quoted string:
mo.md(r"""...""")ormo.md(f"""...""")only when interpolation is required. Put the prose directly inside the string; never paste quoted string fragments such as" ... "lines inside the markdown body. - Do not leave empty generated cells, whitespace-only cells, or
@app.cell def _(): returnplaceholders. Remove them before final verification. - Do not accept a marimo "fix" prompt blindly. If one is accepted during interactive editing, inspect the diff immediately and remove unintended PEP 723/header/cell churn.
- Markdown cells use one plain triple-quoted string:
-
Set up the kernel and dependencies.
- Marimo. Pin dependencies in the PEP 723 script header at the top of the
.pyfile:
Run with# /// script # requires-python = ">=3.12" # dependencies = [ # "marimo", # "polars", # "duckdb", # "matplotlib", # # ... add every import used in the notebook # ] # ///uvx marimo run --sandbox <notebook.py>or edit interactively withuvx marimo edit --sandbox <notebook.py>. The sandbox reads the header and resolves the notebook environment. - Jupyter. Register a named ipykernel for the project's pixi env before the first execution and pin the kernel in the notebook metadata. The kernel name is mandatory — the generic
python3kernel leaks the system interpreter:
Then inpixi run python -m ipykernel install --user --name <project> --display-name "<project> (pixi)"<notebook>.ipynbconfirm:
Add every import used in the notebook to"kernelspec": {"name": "<project>", "display_name": "<project> (pixi)"}pixi.tomlso the kernel can resolve it from a clean install.
- Marimo. Pin dependencies in the PEP 723 script header at the top of the
What ships with it
23 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.
- fixtures/simple_jupyter.ipynb 390 B
- fixtures/simple_marimo.py 212 B runs code
- references/data_loading_duckdb.md 2.4 KB
- references/DEPLOYMENT.md 1.2 KB
- references/EXPORTS.md 2.2 KB
- references/latex.md 1.7 KB
- references/notebook_structure.md 2.8 KB
- references/pixi_jupyter.md 3.4 KB
- references/plot_style.md 3.3 KB
- references/PYTEST.md 5.2 KB
- references/SQL.md 1.6 KB
- references/STATE.md 2.5 KB
- references/TOP-LEVEL-IMPORTS.md 1.6 KB
- references/UI.md 3.6 KB
- references/verification.md 1.8 KB
- references/widgets.md 7.7 KB
- scripts/convert_notebook.py 1.3 KB runs code
- scripts/execute_notebook.py 2.1 KB runs code
- scripts/lint_notebook_structure.py 2.9 KB runs code
- templates/duckdb_bootstrap.sql 447 B
- templates/jupyter_kiss_template.py 4.5 KB runs code
- templates/marimo_notebook_template.py 1.9 KB runs code
- templates/pixi.toml 434 B
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.
- 4d ago First seen · 233 lines · 34 tokens per session scan A b33601307463
notebooks is a skill published in the GitHub repository fmschulz/omics-skills (7 stars, last pushed 10d ago), licensed MIT. It adds 34 tokens to every session and 3,600 once invoked, about $0.0002 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 2026-08-31.
Other skills, from other repositories
annotate-variants
Annotate variants with deterministic tools such as VEP and preserve exact tool versions and command provenance. Use after normalization and before prioritization.
detect-build-normalize
Detect the reference build and create a normalized derived copy without touching the raw input. Use before annotation or cross-tool comparison.
ingest-vcf
Validate and inventory VCF or BCF input files before downstream analysis. Use when a user provides a genome variant file or asks what data is inside it.
nextflow-runner
Run or prepare reproducible Nextflow workflows for heavy genomics jobs. Use only for explicit workflow execution, not routine question answering.
polygenic-risk
Run optional polygenic or trait-style scoring workflows and label all outputs as probabilistic. Use only when requested and keep results separate from direct variant findings.
query-variants
Answer targeted questions about genes, rsIDs, coordinates, or variant panels from local genome files. Use for focused exploration before full reporting.