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 thesecondfox/skill --skill bio-reporting-jupyter-reportsgit clone --depth 1 https://github.com/thesecondfox/skillWrote 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/thesecondfox/skill/bio-reporting-jupyter-reports)<a href="https://agentmods.dev/skills/thesecondfox/skill/bio-reporting-jupyter-reports"><img src="https://agentmods.dev/badge/skills/thesecondfox/skill/bio-reporting-jupyter-reports/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/thesecondfox/skill/bio-reporting-jupyter-reports"><img src="https://agentmods.dev/badge/skills/thesecondfox/skill/bio-reporting-jupyter-reports.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 | $0.00045 | $0.00605 |
| Opus 5 | $0.00023 | $0.00302 |
| Sonnet 5 | $0.00009 | $0.00121 |
| Haiku 4.5 | $0.00005 | $0.00060 |
Grade A, and why
bio-reporting-jupyter-reports 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 5d 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 — 94 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Version Compatibility
Reference examples tested with: jupyter 1.0+, papermill 2.5+
Before using code patterns, verify installed versions match. If versions differ:
- Python:
pip show <package>thenhelp(module.function)to check signatures - CLI:
<tool> --versionthen<tool> --helpto confirm flags
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
Jupyter Reports with Papermill
"Generate reproducible analysis reports" → Execute parameterized Jupyter notebooks programmatically and export as HTML/PDF reports.
- Python:
papermill.execute_notebook(input, output, parameters={...}) - CLI:
jupyter nbconvert --to html notebook.ipynb
Parameterized Notebooks
import papermill as pm
# Execute notebook with parameters
pm.execute_notebook(
'analysis_template.ipynb',
'output_report.ipynb',
parameters={
'input_file': 'data/counts.csv',
'condition_col': 'treatment',
'fdr_threshold': 0.05
}
)
Creating Parameterized Templates
Mark a cell with the parameters tag in Jupyter:
# Parameters (tag this cell as "parameters")
input_file = 'default.csv'
output_dir = 'results/'
fdr_threshold = 0.05
Batch Processing
import papermill as pm
from pathlib import Path
samples = ['sample1', 'sample2', 'sample3']
for sample in samples:
pm.execute_notebook(
'qc_template.ipynb',
f'reports/{sample}_qc.ipynb',
parameters={'sample_id': sample}
)
Converting to HTML/PDF
# Single notebook
jupyter nbconvert --to html report.ipynb
# With execution
jupyter nbconvert --execute --to html report.ipynb
# PDF (requires pandoc + LaTeX)
jupyter nbconvert --to pdf report.ipynb
Best Practices
- Keep analysis code in cells, explanatory text in markdown
- Use parameters for all configurable values
- Include version information and timestamps
- Clear outputs before committing to version control
What ships with it
1 file 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.
- 5d ago First seen · 94 lines · 45 tokens per session scan A 52ff98feeb9d
bio-reporting-jupyter-reports is a skill published in the GitHub repository thesecondfox/skill (3 stars, last pushed 5mo ago), licensed MIT. It adds 45 tokens to every session and 605 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 2026-09-03.
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