Scientific Figure Making
ChenLiu-1996/figures4papers
Covers publication-ready matplotlib figures for academic papers, slides, and reports—bars, trends, scatter, heatmaps, and multi-panel layouts—with this…
Generate data visualization charts (line, scatter, bar, stem, fill-between, stackplot, stairs) from natural language descriptions using an LLM-powered matplotlib pipeline.
Install Dataviz AI
$ npx skills add AlexisZ12/DataVizAiAssistant --skill dataviz-ai -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install AlexisZ12/DataVizAiAssistant dataviz-ai --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/AlexisZ12/DataVizAiAssistant.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skill/dataviz-ai .claude/skills/dataviz-ai && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "dataviz-ai" agent skill from https://github.com/AlexisZ12/DataVizAiAssistant/tree/master/skill/dataviz-ai into .claude/skills/dataviz-ai/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dataviz-ai", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/AlexisZ12/DataVizAiAssistant/tree/master/skill/dataviz-aiType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add AlexisZ12/DataVizAiAssistant --skill dataviz-ai -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install AlexisZ12/DataVizAiAssistant dataviz-ai --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/AlexisZ12/DataVizAiAssistant.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skill/dataviz-ai .agents/skills/dataviz-ai && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "dataviz-ai" agent skill from https://github.com/AlexisZ12/DataVizAiAssistant/tree/master/skill/dataviz-ai into .agents/skills/dataviz-ai/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dataviz-ai", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add AlexisZ12/DataVizAiAssistant --skill dataviz-ai -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install AlexisZ12/DataVizAiAssistant dataviz-ai --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/AlexisZ12/DataVizAiAssistant.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skill/dataviz-ai .cursor/skills/dataviz-ai && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "dataviz-ai" agent skill from https://github.com/AlexisZ12/DataVizAiAssistant/tree/master/skill/dataviz-ai into .cursor/skills/dataviz-ai/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dataviz-ai", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/AlexisZ12/DataVizAiAssistant.git --path skill/dataviz-ai--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add AlexisZ12/DataVizAiAssistant --skill dataviz-ai -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install AlexisZ12/DataVizAiAssistant dataviz-ai --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/AlexisZ12/DataVizAiAssistant.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skill/dataviz-ai .gemini/skills/dataviz-ai && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "dataviz-ai" agent skill from https://github.com/AlexisZ12/DataVizAiAssistant/tree/master/skill/dataviz-ai into .gemini/skills/dataviz-ai/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dataviz-ai", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install AlexisZ12/DataVizAiAssistant dataviz-aiInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add AlexisZ12/DataVizAiAssistant --skill dataviz-ai -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/AlexisZ12/DataVizAiAssistant.git skills-src && mkdir -p .github/skills && cp -r skills-src/skill/dataviz-ai .github/skills/dataviz-ai && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "dataviz-ai" agent skill from https://github.com/AlexisZ12/DataVizAiAssistant/tree/master/skill/dataviz-ai into .github/skills/dataviz-ai/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dataviz-ai", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add AlexisZ12/DataVizAiAssistant --skill dataviz-ai -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install AlexisZ12/DataVizAiAssistant dataviz-ai --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/AlexisZ12/DataVizAiAssistant.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skill/dataviz-ai .opencode/skills/dataviz-ai && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "dataviz-ai" agent skill from https://github.com/AlexisZ12/DataVizAiAssistant/tree/master/skill/dataviz-ai into .opencode/skills/dataviz-ai/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dataviz-ai", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
dataviz-ai- GitHub stars
- 102
- Token cost
- ~662 tokens
- SKILL.md length
- 214 words
- Files
- 29 (incl. scripts)
- Skills in repo
- 1
- Repo updated
- First seen
- Licence
- MIT
At a glance
Generate data visualization charts (line, scatter, bar, stem, fill-between, stackplot, stairs) from natural language descriptions using an LLM-powered matplotlib pipeline.
- Works in 4 steps: Phase 1 — LLM selects the best chart… → Phase 2 — LLM extracts structured data… → Phase 3-5 — LLM designs style… → …
- Tasks that involve Data visualization
- SKILL.md covers Usage, Environment Variables, Supported Chart Types and How It Works, plus 1 more section
- Runs Python scripts from its folder; calls python; needs DATAVIZ_AI_API_KEY
What it does
Dataviz AI is an agent skill from AlexisZ12/DataVizAiAssistant. Generate data visualization charts (line, scatter, bar, stem, fill-between, stackplot, stairs) from natural language descriptions using an LLM-powered matplotlib pipeline. Supports 7 chart types via OpenAI-compatible APIs.
Its SKILL.md is about 660 tokens, which your agent loads only when the skill is triggered. The skill folder holds 30 other files, including scripts (for example `scripts/MatplotlibInterface.py`, `scripts/bar.py` and `scripts/dataviz_ai.py`).
It sits in Data & Analytics, covering Data visualization. It works with Matplotlib, OpenAI and Python. The repository describes itself as: AI-powered data visualization assistant - Automatically generate professional charts from natural language descriptions. Supports 7 chart types and OpenAI/DeepSeek/Ollama… The licence is MIT.
When your agent uses it
- Tasks that involve Data visualization
Example prompts
- “/dataviz-ai”
Requirements
- Python 3
- A credential in DATAVIZ_AI_API_KEY
Workflow steps
4 steps, taken from the first numbered list in SKILL.md.
- Phase 1 — LLM selects the best chart type (0-6) for the request
- Phase 2 — LLM extracts structured data (x, y values, labels) from the description
- Phase 3-5 — LLM designs style (markers/colors/line styles), axis ranges,
- Phase 6 — Matplotlib renders the chart and saves it as PNG
What it can do on your machine
Read from SKILL.md and the folder at commit 140b560. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Ships 18 files in scripts/ (Python, from the files we listed), which the agent can run.
Shell commands in SKILL.md call:
python
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names these keys or tokens, usually read from environment variables:
DATAVIZ_AI_API_KEY
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Dataviz AI loads about 662 tokens when it runs. Until then it costs about 58 tokens; SKILL.md has 214 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.
SKILL.md
The full file from AlexisZ12/DataVizAiAssistant at commit 140b560, republished under its MIT licence (© AlexisZ12). 214 words, ~662 tokens.
Download SKILL.mdSave it as
.claude/skills/dataviz-ai/SKILL.md (or your agent's skills folder). This skill also uses 28 other files; get the full folder from GitHub.- name
- dataviz-ai
- description
- Generate data visualization charts (line, scatter, bar, stem, fill-between, stackplot, stairs) from natural language descriptions using an LLM-powered matplotlib pipeline. Supports 7 chart types via OpenAI-compatible APIs.
DataViz AI Assistant Skill
Generate matplotlib charts from natural language descriptions using a multi-stage LLM pipeline. The skill analyzes your request, extracts data, designs the visual style, and outputs a PNG image.
Usage
python scripts/dataviz_ai.py "your chart description" [-o output.png]| Argument | Required | Description |
|---|---|---|
description | Yes | Natural language description of the chart |
-o, --output | No | Output image path (default: temp file) |
All diagnostic messages go to stderr. Only the image path is printed to stdout.
Example
python scripts/dataviz_ai.py \
"2024年各月销售额趋势，1月100,2月200,3月150,4月300,5月250,6月400"
python scripts/dataviz_ai.py \
"画出上海和北京各季度GDP对比" -o ./gdp_chart.pngEnvironment Variables
All three variables are required:
| Variable | Description |
|---|---|
DATAVIZ_AI_API_KEY | API key for the LLM service |
DATAVIZ_AI_BASE_URL | Base URL for OpenAI-compatible API |
DATAVIZ_AI_MODEL | Model name to use |
Supported Chart Types
| ID | Type | Best for |
|---|---|---|
| 0 | line plot | Trends and continuous data |
| 1 | scatter plot | Relationships, outliers, correlation |
| 2 | bar chart | Comparing categories |
| 3 | stem plot | Discrete data points with structure |
| 4 | fill between | Areas between curves, uncertainty bands |
| 5 | stackplot | Multiple series over a shared axis |
| 6 | stairs plot | Step changes, segmented data |
How It Works
- Phase 1 — LLM selects the best chart type (0-6) for the request
- Phase 2 — LLM extracts structured data (x, y values, labels) from the description
- Phase 3-5 — LLM designs style (markers/colors/line styles), axis ranges, and labels (title, axis labels) in parallel
- Phase 6 — Matplotlib renders the chart and saves it as PNG
Dependencies
openaimatplotlibnumpy
© AlexisZ12, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 28 other files (scripts) in skill/dataviz-ai of AlexisZ12/DataVizAiAssistant.
- SKILL.md
- scripts/MatplotlibInterface.py
- scripts/bar.py
- scripts/dataviz_ai.py
- scripts/fillbetween.py
- scripts/plot.py
- scripts/prompts/prompt1a.txt
- scripts/prompts/prompt2a0.txt
- scripts/prompts/prompt2a1.txt
- scripts/prompts/prompt2a2.txt
- scripts/prompts/prompt2a3.txt
- scripts/prompts/prompt2a4.txt
- scripts/prompts/prompt2a5.txt
- scripts/prompts/prompt2a6.txt
- scripts/prompts/prompt3a0.txt
- scripts/prompts/prompt3a1.txt
- scripts/prompts/prompt3a2.txt
- scripts/prompts/prompt3a3.txt
- scripts/prompts/prompt3a4.txt
- … and 10 more
Open the folder on GitHubat commit 140b560
Compare with similar skills
Dataviz AI next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Dataviz AI this skillAlexisZ12/DataVizAiAssistant | 102 | — | ~662 | Automated safety check: Pass | MIT | |
| Scientific Figure MakingChenLiu-1996/figures4papers | 8.3k | — | ~557 | Automated safety check: Pass | Custom licence | |
| Plot From ImageTrae1ounG/paper-plot-skills | 872 | 1 repos | ~868 | Automated safety check: Pass | None | |
| Python Executorcortega26/chile-hub | 113 | 2 repos | ~1.5k | Automated safety check: Pass | MIT | |
| FigMirror Figure Style TransferVILA-Lab/FigMirror | 521 | — | ~2.1k | Automated safety check: Pass | None | |
| Ieee Figure TableCloudWave818/ieee-skills | 359 | — | ~1k | Automated safety check: Pass | MIT |
Similar skills
Plot From Image
Trae1ounG/paper-plot-skills
Reproduce any academic paper figure from an uploaded image using accumulated style experience.
Python Executor
cortega26/chile-hub
Execute Python code in a safe sandboxed environment via [inference.sh](https://inference.sh).
FigMirror Figure Style Transfer
VILA-Lab/FigMirror
Redraws your data as a matplotlib figure in the visual style of a reference paper figure, using a drawer and reviewer loop.
Ieee Figure Table
CloudWave818/ieee-skills
Audit, redesign, generate, and improve IEEE manuscript figures, tables, captions, result presentation, plotting scripts, visual polish, hybrid Python/R plus vector-editor workflows…
FigMirror
VILA-Lab/FigMirror
Mirrors the visual style of a top-conference paper figure onto your own data, producing a camera-ready PDF and a self-contained matplotlib script.
Related
Works with
Categories
Questions about Dataviz AI
What does Dataviz AI do?
Generate data visualization charts (line, scatter, bar, stem, fill-between, stackplot, stairs) from natural language descriptions using an LLM-powered matplotlib pipeline. Dataviz AI is an agent skill from AlexisZ12/DataVizAiAssistant. Generate data visualization charts (line, scatter, bar, stem, fill-between, stackplot, stairs) from natural language descriptions using an LLM-powered matplotlib pipeline.
When should I use Dataviz AI?
Dataviz AI fits situations like: tasks that involve Data visualization.
How do I install Dataviz AI in Claude Code?
Run `npx skills add AlexisZ12/DataVizAiAssistant --skill dataviz-ai -a claude-code`. Or copy the skill folder (skill/dataviz-ai in AlexisZ12/DataVizAiAssistant) into .claude/skills/dataviz-ai in your project. Claude Code loads it when a task matches its description.
How do I install Dataviz AI in Codex?
Run `npx skills add AlexisZ12/DataVizAiAssistant --skill dataviz-ai -a codex`. Or copy the skill folder (skill/dataviz-ai in AlexisZ12/DataVizAiAssistant) into .agents/skills/dataviz-ai in your project. Codex loads it when a task matches its description.
Can I use Dataviz AI in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add AlexisZ12/DataVizAiAssistant --skill dataviz-ai -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/dataviz-ai, .gemini/skills/dataviz-ai, .github/skills/dataviz-ai and .opencode/skills/dataviz-ai in your project.
What does Dataviz AI need to run?
Going by SKILL.md and its folder, Dataviz AI needs Python for the scripts in its folder, the command-line tools its instructions call (python) and credentials named DATAVIZ_AI_API_KEY. Our summary lists: Python 3; A credential in DATAVIZ_AI_API_KEY.
Does Dataviz AI access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Dataviz AI safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
What licence does Dataviz AI use?
Dataviz AI is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Dataviz AI use?
About 662 tokens (SKILL.md is roughly 2.6k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Dataviz AI?
Skills that share tags, products or a category with Dataviz AI: Scientific Figure Making (ChenLiu-1996/figures4papers, 8.3k stars), Plot From Image (Trae1ounG/paper-plot-skills, 872 stars), Python Executor (cortega26/chile-hub, 113 stars) and FigMirror Figure Style Transfer (VILA-Lab/FigMirror, 521 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Dataviz AI?
AlexisZ12 (a GitHub user) maintains it in AlexisZ12/DataVizAiAssistant, which has 102 GitHub stars. The repository was last updated on August 9, 2026.
Source: AlexisZ12/DataVizAiAssistant on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.