bio-data-visualization-matplotlib-fundamentals

bio-data-visualization-matplotlib-fundamentals is a skill for Claude Code, Codex from PKU-YuanGroup/OpenAI4S. It costs 101 tokens per session (3,243 once invoked), scanned A, a copy of bio-data-visualization-matplotlib-fundamentals, MIT.

A guide to creating publication-ready scientific figures in Python with Matplotlib and Seaborn. It covers common charts, readable fonts, layout, and color palettes that remain distinguishable for people with color-vision deficiencies.

In plain words
What is it for?
Use it to make scatter, line, bar, and other static charts for reports and journal submissions.
Why use it?
It helps turn exploratory plots into consistent figures suitable for papers, including large scatter plots and journal font requirements.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to make scatter, line, bar, and other static charts for reports and journal submissions.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/pku-yuangroup/openai4s/bio-data-visualization-matplotlib-fundamentals
Install

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.

Any agent
npx skills add PKU-YuanGroup/OpenAI4S --skill bio-data-visualization-matplotlib-fundamentals
Clone the repo
git clone --depth 1 https://github.com/PKU-YuanGroup/OpenAI4S

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for bio-data-visualization-matplotlib-fundamentals

README.md
[![agentmods](https://agentmods.dev/badge/skills/pku-yuangroup/openai4s/bio-data-visualization-matplotlib-fundamentals/github.svg)](https://agentmods.dev/skills/pku-yuangroup/openai4s/bio-data-visualization-matplotlib-fundamentals)
Your own site
<a href="https://agentmods.dev/skills/pku-yuangroup/openai4s/bio-data-visualization-matplotlib-fundamentals"><img src="https://agentmods.dev/badge/skills/pku-yuangroup/openai4s/bio-data-visualization-matplotlib-fundamentals/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.

agentmods 80×15 button for bio-data-visualization-matplotlib-fundamentals

Your own site · 80×15
<a href="https://agentmods.dev/skills/pku-yuangroup/openai4s/bio-data-visualization-matplotlib-fundamentals"><img src="https://agentmods.dev/badge/skills/pku-yuangroup/openai4s/bio-data-visualization-matplotlib-fundamentals.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 101 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,243 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin 92% copy Near-identical to another mod in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00101 $0.03243
Opus 5 $0.00051 $0.01622
Sonnet 5 $0.00020 $0.00649
Haiku 4.5 $0.00010 $0.00324

Measured 9d ago against content hash e98a26b855d2, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

bio-data-visualization-matplotlib-fundamentals 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 9d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/matplotlib_phd.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

Origin

This is a copy

92% identical to bio-data-visualization-matplotlib-fundamentals — 12 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

skills/bioskills/bio-data-visualization-matplotlib-fundamentals/SKILL.md · 307 lines

How it starts

The opening of the file, as written. The whole thing — 307 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Version Compatibility

Reference examples tested with: matplotlib 3.8+, seaborn 0.13+, numpy 1.26+, pandas 2.2+.

Before using code patterns, verify installed versions match. If versions differ:

  • Python: pip show <package> then help(module.function) to check signatures

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

matplotlib Fundamentals

"Make a publication figure in Python" -> Build via the object-oriented Figure/Axes API (not pyplot state-machine), with constrained_layout for axes alignment, pdf.fonttype=42 for journal-compliant TrueType fonts, CVD-safe palettes, and rasterized point layers for large scatter. The pyplot interface is for notebook scratch; the Figure/Axes API is for reproducible figures.

  • Python: fig, ax = plt.subplots() -> ax.scatter / ax.plot / ax.bar; seaborn.objects (new grammar API) for ggplot-like

The Three Modern Defaults

  1. Object-oriented APIfig, ax = plt.subplots(figsize=(4, 3)) then ax.scatter(x, y), ax.set_xlabel(...). The pyplot state-machine (plt.scatter, plt.xlabel) hides which axes are being modified and breaks in multi-subplot figures.

  2. constrained_layoutplt.subplots(constrained_layout=True) automatically prevents axis-label clipping and tight-packs subplots. Replaces the older tight_layout() and is the default in matplotlib 3.6+.

  3. Type-42 (TrueType) font embeddingplt.rcParams['pdf.fonttype']=42 produces searchable/editable PDF text. Default Type-3 PostScript glyphs are not searchable and rejected by Nature, IEEE, ACM, and many other publishers.

Standard Setup for Publication

import matplotlib.pyplot as plt
import matplotlib as mpl

# rcParams for publication compliance
mpl.rcParams.update({
    'pdf.fonttype': 42,                 # TrueType -- searchable PDFs
    'ps.fonttype': 42,                  # TrueType in EPS
    'font.family': 'sans-serif',
    'font.sans-serif': ['Arial', 'Helvetica', 'DejaVu Sans'],
    'font.size': 7,                     # Nature requires 5-7 pt body text
    'axes.labelsize': 7,
    'axes.titlesize': 8,
    'xtick.labelsize': 6,
    'ytick.labelsize': 6,
    'legend.fontsize': 6,
    'figure.dpi': 100,                  # display
    'savefig.dpi': 300,                 # save
    'savefig.bbox': 'tight',
    'savefig.pad_inches': 0.05,
    'axes.linewidth': 0.5,
    'xtick.major.width': 0.5,
    'ytick.major.width': 0.5,
    'lines.linewidth': 1.0,
    'patch.linewidth': 0.5,
})

Read the full file on GitHub · 307 lines

Files

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.

Changes

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.

  1. 9d ago First seen · 307 lines · 101 tokens per session scan A e98a26b855d2

Subscribe to this mod's changes

bio-data-visualization-matplotlib-fundamentals is a skill published in the GitHub repository PKU-YuanGroup/OpenAI4S (407 stars, last pushed yesterday), licensed MIT. It adds 101 tokens to every session and 3,243 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. It is 92% identical to bio-data-visualization-matplotlib-fundamentals, differing in 12 lines, and is treated as a copy.

Related

Other skills, from other repositories

boltz-structure-prediction

Boltz-1 / Boltz-2 structure prediction for proteins, complexes, and ligand-aware validation. Use this skill when: (1) Predicting protein complex structures, (2) Validating designed binders, (3) Need open-source alternative to AF2, (4) Predicting protein-ligand complexes, (5) Using local GPU resources. For QC…

zongtingwei/Bioclaw_Skills_Hub · 121 tokens

imaging-data-commons

Query and download public cancer imaging data from NCI Imaging Data Commons using idc-index. Use for accessing large-scale radiology (CT, MR, PET) and pathology datasets for AI training or research. No authentication required. Query by metadata, visualize in browser, check licenses.

synthetic-sciences/openscience · 62 tokens

flow-cytometry-analysis

Complete flow cytometry analysis pipeline. FCS file handling, compensation, manual/automated gating, immunophenotyping, CFSE proliferation analysis, cell cycle analysis (Dean-Jett-Fox), and apoptosis assays. Extends flowio with analytical workflows. For raw FCS parsing only use flowio.

synthetic-sciences/openscience · 67 tokens

scientific-critical-thinking

Evaluate scientific claims and evidence quality. Use for assessing experimental design validity, identifying biases and confounders, applying evidence grading frameworks (GRADE, Cochrane Risk of Bias), or teaching critical analysis. Best for understanding evidence quality, identifying flaws. For formal peer review…

xintaofei/codeg · 63 tokens

cellxgene-census

Query the CELLxGENE Census (61M+ cells) programmatically. Use when you need expression data across tissues, diseases, or cell types from the largest curated single-cell atlas. Best for population-scale queries, reference atlas comparisons. For analyzing your own data use scanpy or scvi-tools.

synthetic-sciences/openscience · 67 tokens

glycobiology

Glycosylation site prediction and glycobiology analysis. N-glycosylation motif finding, O-glycosylation hotspot prediction, glycan structure resources. Lightweight, pure Python. For protein function queries use uniprot-database; for structure analysis use alphafold-database.

synthetic-sciences/openscience · 67 tokens