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 HolobiomicsLab/asb-skill-collections --skill python-pandas-data-manipulationgit clone --depth 1 https://github.com/HolobiomicsLab/asb-skill-collectionsWrote 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/holobiomicslab/asb-skill-collections/python-pandas-data-manipulation)<a href="https://agentmods.dev/skills/holobiomicslab/asb-skill-collections/python-pandas-data-manipulation"><img src="https://agentmods.dev/badge/skills/holobiomicslab/asb-skill-collections/python-pandas-data-manipulation/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/holobiomicslab/asb-skill-collections/python-pandas-data-manipulation"><img src="https://agentmods.dev/badge/skills/holobiomicslab/asb-skill-collections/python-pandas-data-manipulation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Output Handling · line 79 Output size or generation rate is not bounded. Unbounded output enables denial-of-service through resource exhaustion, log flooding, or context-window stuffing.Fix: Set explicit limits on output length, generation count, and rate. Use max_tokens and truncation to prevent unbounded output.
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 | $0.00067 | $0.01449 |
| Opus 5 | $0.00034 | $0.00724 |
| Sonnet 5 | $0.00013 | $0.00290 |
| Haiku 4.5 | $0.00007 | $0.00145 |
Grade A, and why
python-pandas-data-manipulation 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 3d 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 — 98 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Python pandas data manipulation
Summary
Use Python and pandas to load, transform, and export tabular datasets (TSV/CSV) containing precomputed genomic statistics, applying binning, smoothing, and aggregation operations to prepare contact frequency curves for downstream analysis.
When to use
You have precomputed expected contact frequency tables (TSV format with columns like dist_bp, contact_frequency, n_valid) and need to apply log-binning and smoothing to group distance values into log-spaced bins, aggregate statistics within each bin, and export a cleaned, annotated output table for visualization or further analysis.
When NOT to use
- Input is already a log-binned or aggregated contact frequency curve; re-binning would introduce redundant or conflicting bin boundaries.
- Contact frequency data is in cooler (.cool/.mcool) HDF5 format; use cooler API methods directly rather than manual TSV export and pandas re-import.
- Analysis goal is real-time or streaming; pandas requires full dataset in memory.
Inputs
- TSV table with columns: dist_bp (genomic distance), contact_frequency, n_valid (bin size/validity counts)
- Logarithmic binning parameters (e.g., log base, minimum/maximum bin edges)
- Optional: precomputed smoothing kernel or smoothing algorithm specification
Outputs
- Log-binned TSV table with columns: dist_bp (bin representative or range), count.avg.smoothed (mean contact frequency per bin), and bin statistics (e.g., bin size, variance)
- Optionally: metadata or column headers documenting bin structure and smoothing method
How to apply
Load the precomputed expected_cis table from TSV format into a pandas DataFrame. Apply logarithmic binning to group genomic distances into log-spaced bins using the cooltools logbin_expected function or equivalent binning logic. Compute aggregated statistics within each bin (e.g., mean smoothed contact frequency, bin counts) using pandas groupby and aggregation operations. Generate an output DataFrame with columns including binned distance, mean smoothed contact frequency, and bin statistics. Ensure numeric precision and proper column headers, then export to TSV format for downstream use.
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.
- 3d ago First seen · 98 lines · 67 tokens per session scan A b2fd93cbcaf1
python-pandas-data-manipulation is a skill published in the GitHub repository HolobiomicsLab/asb-skill-collections (15 stars, last pushed 4d ago), licensed Apache-2.0. It adds 67 tokens to every session and 1,449 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 2026-09-06.
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