Matplotlib
zLanqing/codex-claude-academic-skills
Low-level plotting library for full customization. An agent skill from zLanqing/codex-claude-academic-skills.
Decide whether quantitative data should be visualized, choose and design an analytically faithful chart or table, and reject misleading or low-information artifacts.
Install Data Visualization Quality
$ npx skills add swyxio/skills --skill data-visualization-quality -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install swyxio/skills data-visualization-quality --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/swyxio/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/data-visualization-quality .claude/skills/data-visualization-quality && 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 "data-visualization-quality" agent skill from https://github.com/swyxio/skills/tree/main/data-visualization-quality into .claude/skills/data-visualization-quality/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-visualization-quality", 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/swyxio/skills/tree/main/data-visualization-qualityType 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 swyxio/skills --skill data-visualization-quality -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install swyxio/skills data-visualization-quality --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/swyxio/skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/data-visualization-quality .agents/skills/data-visualization-quality && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "data-visualization-quality" agent skill from https://github.com/swyxio/skills/tree/main/data-visualization-quality into .agents/skills/data-visualization-quality/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-visualization-quality", 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 swyxio/skills --skill data-visualization-quality -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install swyxio/skills data-visualization-quality --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/swyxio/skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/data-visualization-quality .cursor/skills/data-visualization-quality && 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 "data-visualization-quality" agent skill from https://github.com/swyxio/skills/tree/main/data-visualization-quality into .cursor/skills/data-visualization-quality/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-visualization-quality", 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/swyxio/skills.git --path data-visualization-quality--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 swyxio/skills --skill data-visualization-quality -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install swyxio/skills data-visualization-quality --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/swyxio/skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/data-visualization-quality .gemini/skills/data-visualization-quality && 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 "data-visualization-quality" agent skill from https://github.com/swyxio/skills/tree/main/data-visualization-quality into .gemini/skills/data-visualization-quality/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-visualization-quality", 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 swyxio/skills data-visualization-qualityInstalls 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 swyxio/skills --skill data-visualization-quality -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/swyxio/skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/data-visualization-quality .github/skills/data-visualization-quality && 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 "data-visualization-quality" agent skill from https://github.com/swyxio/skills/tree/main/data-visualization-quality into .github/skills/data-visualization-quality/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-visualization-quality", 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 swyxio/skills --skill data-visualization-quality -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install swyxio/skills data-visualization-quality --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/swyxio/skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/data-visualization-quality .opencode/skills/data-visualization-quality && 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 "data-visualization-quality" agent skill from https://github.com/swyxio/skills/tree/main/data-visualization-quality into .opencode/skills/data-visualization-quality/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-visualization-quality", 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
data-visualization-quality- GitHub stars
- 172
- Token cost
- ~1.2k tokens
- SKILL.md length
- 576 words
- Files
- 3 (incl. references)
- Skills in repo
- 89
- Repo updated
- First seen
- Licence
- MIT
At a glance
Decide whether quantitative data should be visualized, choose and design an analytically faithful chart or table, and reject misleading or low-information artifacts.
- Works in 4 steps: Claim: the comparison, trend,… → Required evidence: the dimensions,… → Coverage: which required fields and… → …
- Analytical tables
- SKILL.md covers Decide before designing, Preserve semantic fidelity, Require a visual to earn its… and Choose and design the form, plus 2 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Data Visualization Quality is an agent skill from swyxio/skills. Decide whether quantitative data should be visualized, choose and design an analytically faithful chart or table, and reject misleading or low-information artifacts. Use for charts, dashboards, analytical tables, visualization specifications, or production visualization pipelines; do not use for generic page styling or interaction mechanics.
Its SKILL.md is about 1.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 4 other files, including reference files (for example `agents/openai.yaml` and `references/chart-design.md`).
It sits in Data & Analytics, covering Data visualization. The repository describes itself as: Agent skills for Claude Code and other AI agents. The licence is MIT.
When your agent uses it
- Analytical tables
- Visualization specifications
- Production visualization pipelines
- Do not use for generic page styling
Example prompts
- “/data-visualization-quality”
Workflow steps
4 steps, taken from the first numbered list in SKILL.md.
- Claim: the comparison, trend, distribution, composition, relationship,
- Required evidence: the dimensions, measures, units, denominators, time
- Coverage: which required fields and entities are complete, partial,
- Smallest useful surface: prose, compact table, one chart, small multiples,
What it can do on your machine
Read from SKILL.md and the folder at commit 038ef34. 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
No scripts in the folder and no shell commands in SKILL.md.
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 no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Data Visualization Quality loads about 1.2k tokens when it runs, and up to ~3.4k if it reads all its reference files. Until then it costs about 93 tokens; SKILL.md has 576 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); files beside SKILL.md are not scanned.
SKILL.md
The full file from swyxio/skills at commit 038ef34, republished under its MIT licence (© swyxio). 576 words, ~1,205 tokens.
Download SKILL.mdSave it as
.claude/skills/data-visualization-quality/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.- name
- data-visualization-quality
- description
- Decide whether quantitative data should be visualized, choose and design an analytically faithful chart or table, and reject misleading or low-information artifacts. Use for charts, dashboards, analytical tables, visualization specifications, or production visualization pipelines; do not use for generic page styling or interaction mechanics.
Data Visualization Quality
Create visualizations that answer the requested analytical question. A valid chart specification is not automatically publishable.
Decide before designing
Establish four things before choosing a visual:
- Claim: the comparison, trend, distribution, composition, relationship, or decision the user wants to understand.
- Required evidence: the dimensions, measures, units, denominators, time windows, and comparable populations needed to support that claim.
- Coverage: which required fields and entities are complete, partial, unavailable, pending, reported, or calculated.
- Smallest useful surface: prose, compact table, one chart, small multiples, or a genuinely interactive explorer.
Classify the outcome:
- Complete: available comparable data directly supports the requested claim.
- Partial: a clearly bounded subset supports a useful part of the claim.
- Unavailable: the central requested measure or denominator is absent.
- Unsupported: the source or analytical method cannot answer the request.
For unavailable or unsupported outcomes, do not create a chart or data explorer. Explain the missing evidence and offer the best recovery action. A private run receipt may preserve the attempt, but it is not a visualization.
Preserve semantic fidelity
- Never substitute an adjacent measure merely because it exists. Issued tickets are not attendance; totals are not concentration; cumulative counts are not velocity; ticket counts are not revenue.
- Never imply comparability across unlike cohorts, definitions, currencies, denominators, tier taxonomies, or time windows. Normalize, facet, or decline.
- Keep observed, reported, calculated, estimated, pending, and unavailable values distinguishable.
- Do not coerce missing evidence to zero.
- State material caveats next to the claim they limit.
Require a visual to earn its space
Use a visual only when it makes a meaningful relationship easier to understand than concise prose or a compact table.
Do not publish:
- a chart whose only surviving measure answers a different question;
- a table dominated by repeated
Unavailable,N/A, or coverage prose; - a large chart for a few values that are clearer in one sentence;
- prose, chart, and table that redundantly repeat the same information;
- decorative KPI cards, controls, legends, or panels that do not aid a decision;
- a normal success workspace around an unavailable result.
In production pipelines, implement deterministic publishability validation. Skill instructions and model self-assessment are not sufficient enforcement. Keep exact thresholds schema-aware and tested: absence of one central measure can invalidate an analysis even when most cells are populated.
Show full SKILL.md (197 more words)Show less
Choose and design the form
Read references/chart-design.md when selecting or reviewing chart form, scales, ordering, labels, interaction, responsive layout, accessibility, or exports. Skip it when the correct result is prose-only or an unavailable receipt.
Product-state contract
Match product status to analytical outcome, not merely execution completion.
- Complete visualizations may expose appropriate interactive and export actions.
- Partial visualizations must label their bounded coverage and omissions.
- Unavailable or unsupported results should be compact, remove irrelevant chart/share/export/filter controls, and offer a recovery action.
- Do not call a receipt-only result a dashboard, explorer, or completed analysis.
- Strip internal visualization specifications from fallback prose. Do not show a raw Markdown table when the product owns structured table rendering.
Verify
For production work, validate:
- the visual answers the current request rather than an adjacent one;
- units, scales, denominators, sorting, normalization, and precision;
- missing and partial coverage behavior;
- labels, marks, legends, tooltips, and accessible fallback text;
- realistic desktop and mobile rendering;
- malformed, oversized, duplicate, unavailable-dominated, and metric- substitution rejection paths;
- source validation, application publication, provider delivery, and visible rendering as separate claims.
Use demonstrated bad outputs as regression fixtures. A polished misleading visualization is a more dangerous failure than an explicit refusal to chart.
© swyxio, 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 2 other files (references) in data-visualization-quality of swyxio/skills.
- SKILL.md
- agents/openai.yaml
- references/chart-design.md
Open the folder on GitHubat commit 038ef34
Compare with similar skills
Data Visualization Quality 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 |
|---|---|---|---|---|---|---|
| Data Visualization Quality this skillswyxio/skills | 172 | — | ~1.2k | Automated safety check: Pass | MIT | |
| MatplotlibzLanqing/codex-claude-academic-skills | 4.6k | 17 repos | ~2.9k | Automated safety check: Pass | MIT | |
| Chart Visualizationbytedance/deer-flow | 83k | 2 repos | ~840 | Automated safety check: Pass | MIT | |
| Scientific Visualizationmims-harvard/OptimusKG | 146 | 19 repos | ~6.3k | Automated safety check: Pass | MIT | |
| SeabornzLanqing/codex-claude-academic-skills | 4.6k | 16 repos | ~4.9k | Automated safety check: Pass | BSD-3-Clause | |
| Plot From DataTrae1ounG/paper-plot-skills | 861 | 1 repos | ~583 | Automated safety check: Pass | None |
Similar skills
Chart Visualization
bytedance/deer-flow
Picks a suitable chart type from 26 options for your data, maps the data to that chart's parameters and generates a chart image through a JavaScript script.
Scientific Visualization
mims-harvard/OptimusKG
Create publication figures with matplotlib/seaborn/plotly. An agent skill from mims-harvard/OptimusKG.
Seaborn
zLanqing/codex-claude-academic-skills
Statistical visualization with pandas integration. An agent skill from zLanqing/codex-claude-academic-skills.
Plot From Data
Trae1ounG/paper-plot-skills
Generate publication-quality matplotlib figures by selecting a pre-built paper style and substituting user data.
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…
More from swyxio/skills
All 89 skills in this repoProgrammatic Agents
swyxio/skills
Run a selected coding-agent CLI programmatically, with latency, error, usage, cost, and trace logging.
Reserved Handle Policy
swyxio/skills
Design, implement, audit, or refresh protected username and handle namespaces for public products.
New Mac Setup
swyxio/skills
Fully automated new Mac setup for fullstack web developers and AI engineers.
Youtube API
swyxio/skills
Manage YouTube videos programmatically via the YouTube Data API v3 — upload video files, upload custom thumbnails, update video metadata (titles, descriptions, tags), and query video/channel info…
Youtube Studio Batch Upload
swyxio/skills
Batch YouTube Studio upload workflow for videos sourced from Airtable, Google Drive, Loom, YouTube, or local files.
Deep Trajectory Analysis
swyxio/skills
Reconstruct and visually analyze paired agent, game, or policy trajectories to determine whether changed actions produced their intended effects.
Related
Categories
Questions about Data Visualization Quality
What does Data Visualization Quality do?
Decide whether quantitative data should be visualized, choose and design an analytically faithful chart or table, and reject misleading or low-information artifacts. Data Visualization Quality is an agent skill from swyxio/skills. Decide whether quantitative data should be visualized, choose and design an analytically faithful chart or table, and reject misleading or low-information artifacts.
When should I use Data Visualization Quality?
Data Visualization Quality fits situations like: analytical tables; visualization specifications; production visualization pipelines; do not use for generic page styling.
How do I install Data Visualization Quality in Claude Code?
Run `npx skills add swyxio/skills --skill data-visualization-quality -a claude-code`. Or copy the skill folder (data-visualization-quality in swyxio/skills) into .claude/skills/data-visualization-quality in your project. Claude Code loads it when a task matches its description.
How do I install Data Visualization Quality in Codex?
Run `npx skills add swyxio/skills --skill data-visualization-quality -a codex`. Or copy the skill folder (data-visualization-quality in swyxio/skills) into .agents/skills/data-visualization-quality in your project. Codex loads it when a task matches its description.
Can I use Data Visualization Quality 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 swyxio/skills --skill data-visualization-quality -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/data-visualization-quality, .gemini/skills/data-visualization-quality, .github/skills/data-visualization-quality and .opencode/skills/data-visualization-quality in your project.
What does Data Visualization Quality need to run?
SKILL.md names no scripts, command-line tools or credentials: Data Visualization Quality is instructions for the agent only.
Does Data Visualization Quality 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 Data Visualization Quality 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. Review the folder before installing.
What licence does Data Visualization Quality use?
Data Visualization Quality 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 Data Visualization Quality use?
About 1.2k tokens (SKILL.md is roughly 4.8k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 2.2k tokens, read only when the agent opens those files.
What are the alternatives to Data Visualization Quality?
Skills that share tags, products or a category with Data Visualization Quality: Matplotlib (zLanqing/codex-claude-academic-skills, 4.6k stars), Chart Visualization (bytedance/deer-flow, 83k stars), Scientific Visualization (mims-harvard/OptimusKG, 146 stars) and Seaborn (zLanqing/codex-claude-academic-skills, 4.6k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Data Visualization Quality?
swyxio (a GitHub user) maintains it in swyxio/skills, which has 172 GitHub stars. The repository holds 89 skills in this directory. The repository was last updated on October 5, 2026.
Source: swyxio/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.