make-sci-data-figures

make-sci-data-figures is a skill for Codex from zhoy0409-debug/polish-sci-figures. It costs 93 tokens per session (3,774 once invoked), scanned A, original, MIT.

A workbench for turning raw CSV, TSV, or Excel tables into defensible scientific figures. It chooses charts based on the biological question and data structure rather than assuming one chart type fits every dataset.

In plain words
What is it for?
Use it for group comparisons, relationships, time courses, compositions, matrices, survival data, dose–response curves, ROC/PR analyses, and other specialist results that need publication figures.
Why use it?
It helps prevent misleading comparisons by requiring details such as the experimental unit, paired or independent design, time points, and authoritative group order before plotting.

Skill for Codex

Written for Codex: agents/openai.yaml present. Also seen: $skill-name invocation.

Good fit Use it for group comparisons, relationships, time courses, compositions, matrices, survival data, dose–response curves, ROC/PR analyses, and other specialist results that need publication figures.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/zhoy0409-debug/polish-sci-figures/make-sci-data-figures
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 zhoy0409-debug/polish-sci-figures --skill make-sci-data-figures
Clone the repo
git clone --depth 1 https://github.com/zhoy0409-debug/polish-sci-figures

Made for: 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 make-sci-data-figures

README.md
[![agentmods](https://agentmods.dev/badge/skills/zhoy0409-debug/polish-sci-figures/make-sci-data-figures/github.svg)](https://agentmods.dev/skills/zhoy0409-debug/polish-sci-figures/make-sci-data-figures)
Your own site
<a href="https://agentmods.dev/skills/zhoy0409-debug/polish-sci-figures/make-sci-data-figures"><img src="https://agentmods.dev/badge/skills/zhoy0409-debug/polish-sci-figures/make-sci-data-figures/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 make-sci-data-figures

Your own site · 80×15
<a href="https://agentmods.dev/skills/zhoy0409-debug/polish-sci-figures/make-sci-data-figures"><img src="https://agentmods.dev/badge/skills/zhoy0409-debug/polish-sci-figures/make-sci-data-figures.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 93 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,774 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 original No closer match found 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 $0.00093 $0.03774
Opus 5.5 $0.00037 $0.01510
Sonnet 5.5 $0.00019 $0.00755
Haiku 4.5 $0.00009 $0.00377

Measured 4d ago against content hash 3bc52fe85cbb, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

make-sci-data-figures 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.

The scan reads SKILL.md. This mod also ships 29 executable files (examples/make_advanced_examples.py, examples/make_family_examples.py, scripts/advanced_template_workbench.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.

skills/make-sci-data-figures/SKILL.md · 187 lines

How it starts

The opening of the file, as written. The whole thing — 187 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Make SCI Data Figures

Start from the biological question and experimental unit, not from a preferred chart type. Produce several honest candidates from the same data, keep their canvas and typography identical, and explain why each candidate is or is not appropriate.

An ordinary request such as “帮我把这张表画成适合论文的图” is enough to begin. Read the table before asking questions; when design is unresolved, ask concretely whether records describe the same objects before/after or different objects. Offer a small useful shortlist and explain what each reveals instead of requiring statistical jargon or an elaborate prompt.

Shared Figure Discovery

When a user says “不知道这些数据该怎么画”, call the same deterministic engine used by Figure Studio, from the suite root:

python sci_figures.py discover data.csv --json
python sci_figures.py discover data.xlsx --sheet 0 --context research-context.json --out discovery-results --json

Read the discovery contract and context examples. The CLI adapter requires Node.js 18+ in addition to the verified Python environment. CSV/TSV/XLSX are read locally. The ResearchDiscovery browser module is the sole scientific recommendation implementation; do not invent a second rule list in the prompt. Inspect profile.confirmation_needed and excluded before asking the few relevant design questions. Record only actual user declarations: column-name matches remain inferred_unverified. Never infer independence, pairing, biological replication, nesting or completed differential analysis from a keyword. Counts are observations or distinct IDs until experimental units are confirmed.

Use a local JSON context to declare column roles, raw/summary/results level, design, outcome type, technical replicates and nesting. Paired long tables require an explicit pair_order. ROC requires observed labels/scores and a positive class; volcano requires supplied adjusted P values and explicitly confirmed display thresholds. --out runs the existing workbenches, validates each real-data candidate and produces an editable Studio HTML plus a trace JSON. Check renderer_status: rule eligibility does not itself certify successful rendering. Existing specialist workbenches and the full manual template library remain available outside the initial curated discovery subset.

Read the full file on GitHub · 187 lines

Files

What ships with it

48 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. 4d ago Changed · +15 lines 3bc52fe85cbb
  2. 7d ago Changed 9e419be63617
  3. 8d ago Changed · +15 lines · +3 tokens per session ac8a6ce4a276
  4. 1mo ago First seen · 157 lines · 90 tokens per session scan A 93eda83b3d4a

Subscribe to this mod's changes

make-sci-data-figures is a skill published in the GitHub repository zhoy0409-debug/polish-sci-figures (58 stars, last pushed 2d ago), licensed MIT. It adds 93 tokens to every session and 3,774 once invoked, about $0.0004 per session on Opus 5.5. 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.

Related

Other skills, from other repositories

xiaobei-skill-image-to-vba

An image-to-VBA skill that converts academic figures, diagrams, screenshots, and slides into Office drawing code and editable shapes. VBA is the programming language built into Microsoft Office.

xiao24bei/xiaobei-skill · 86 tokens

xiaobei-skill-academic-paper-to-ppt

A workflow that turns academic papers, reports, and technical documents into presentation slides, optionally following a supplied PowerPoint template. It creates a written outline, full-slide images, and a PowerPoint file.

xiao24bei/xiaobei-skill · 101 tokens

xiaobei-skill-rebuild-image-in-powerpoint

Use when a user wants Codex to directly operate PowerPoint and reconstruct an entire reference image as editable PowerPoint objects while preserving its full layout, labels, connectors, and visual relationships. Do not use for paper-to-deck generation or VBA-only output.

xiao24bei/xiaobei-skill · 62 tokens

elsevier-figure-style

Generate, revise, and audit source-backed quantitative manuscript figures, and visually audit exported schematic/conceptual figures and graphical abstracts without redrawing them in v0.1. Use for Elsevier-style manuscript figure generation, matplotlib or ggplot2 result plots, line charts, heatmaps, bar charts, scatter…

guhou-hvi/elsevier-figure-style · 105 tokens

academic-figure

Create, advise on, or audit academic figures in four modes. advise profiles the data and recommends a chart type when none is fixed. journal-spec creates or reviews publication-ready figures for journal submission specs using matplotlib, seaborn, plotly, industrytslib, or the optional pubfig backend. from-data fills a…

bahayonghang/my-ai-cli-toolkit · 255 tokens

nice-figures

Generate matplotlib figures in the soft-pastel + warm-earth visual register used by AI alignment research blogs (Anthropic-style) — bold sans-serif display titles, scatter overlaid with smoothed trends and shaded confidence bands, minimal axes, '↓better' badges, warm coral/peach/sage/blue palettes, white background by…

Mapika/nice-figures · 183 tokens