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/thesecondfox/skill/bio-data-visualization-interactive-visualizationnpx skills add thesecondfox/skill --skill bio-data-visualization-interactive-visualizationgit clone --depth 1 https://github.com/thesecondfox/skillWhat 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 | $0.00051 | $0.01673 |
| Opus 5 | $0.00026 | $0.00837 |
| Sonnet 5 | $0.00010 | $0.00335 |
| Haiku 4.5 | $0.00005 | $0.00167 |
Grade A, and why
bio-data-visualization-interactive-visualization 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 2d 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 — 210 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Version Compatibility
Reference examples tested with: ggplot2 3.5+, pandas 2.2+
Before using code patterns, verify installed versions match. If versions differ:
- Python:
pip show <package>thenhelp(module.function)to check signatures - R:
packageVersion('<pkg>')then?function_nameto verify parameters
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
Interactive Visualization
"Create interactive plots" → Build zoomable, hoverable visualizations for exploring large datasets in notebooks or HTML reports.
- Python:
plotly.express,bokeh,altair - R:
plotly::ggplotly(),htmlwidgets
plotly (Python)
import plotly.express as px
import plotly.graph_objects as go
import pandas as pd
# Scatter plot
fig = px.scatter(df, x='PC1', y='PC2', color='condition', hover_data=['sample'],
title='PCA Plot')
fig.write_html('pca_interactive.html')
fig.show()
Interactive Volcano Plot
Goal: Create a zoomable, hoverable volcano plot for exploring differential expression results.
Approach: Compute significance categories, build a plotly scatter with gene names on hover and metadata tooltips, add threshold reference lines, and export as standalone HTML.
import plotly.express as px
df['neg_log_pval'] = -np.log10(df['pvalue'])
df['significant'] = (df['padj'] < 0.05) & (abs(df['log2FoldChange']) > 1)
fig = px.scatter(df, x='log2FoldChange', y='neg_log_pval',
color='significant', hover_name='gene',
hover_data=['baseMean', 'padj'],
color_discrete_map={True: 'red', False: 'grey'},
title='Interactive Volcano Plot')
fig.add_hline(y=-np.log10(0.05), line_dash='dash', line_color='grey')
fig.add_vline(x=-1, line_dash='dash', line_color='grey')
fig.add_vline(x=1, line_dash='dash', line_color='grey')
fig.update_layout(xaxis_title='Log2 Fold Change', yaxis_title='-Log10 P-value')
fig.write_html('volcano_interactive.html')
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.
- 2d ago First seen · 210 lines · 51 tokens per session scan A e17e07a6172d
bio-data-visualization-interactive-visualization is a skill published in the GitHub repository thesecondfox/skill (3 stars, last pushed 5mo ago), licensed MIT. It adds 51 tokens to every session and 1,673 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 2026-08-31.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
babysit-pr
Babysit a GitHub pull request after creation by continuously polling review comments, CI checks/workflow runs, and mergeability state until the PR is merged/closed or user help is required. Diagnose failures, retry likely flaky failures up to 3 times, auto-fix/push branch-related issues when appropriate, and keep…
imagegen
Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output…