Matplotlib
zLanqing/codex-claude-academic-skills
Low-level plotting library for full customization. An agent skill from zLanqing/codex-claude-academic-skills.
decision-lab house figure style for matplotlib. An agent skill from pymc-labs/decision-lab.
$ npx skills add pymc-labs/decision-lab --skill dlab-figure-style -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install pymc-labs/decision-lab dlab-figure-style --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/pymc-labs/decision-lab.git skills-src && mkdir -p .claude/skills && cp -r skills-src/dlab/data/figure_style .claude/skills/dlab-figure-style && 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 "dlab-figure-style" agent skill from https://github.com/pymc-labs/decision-lab/tree/main/dlab/data/figure_style into .claude/skills/dlab-figure-style/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dlab-figure-style", 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/pymc-labs/decision-lab/tree/main/dlab/data/figure_styleType 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 pymc-labs/decision-lab --skill dlab-figure-style -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install pymc-labs/decision-lab dlab-figure-style --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/pymc-labs/decision-lab.git skills-src && mkdir -p .agents/skills && cp -r skills-src/dlab/data/figure_style .agents/skills/dlab-figure-style && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "dlab-figure-style" agent skill from https://github.com/pymc-labs/decision-lab/tree/main/dlab/data/figure_style into .agents/skills/dlab-figure-style/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dlab-figure-style", 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 pymc-labs/decision-lab --skill dlab-figure-style -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install pymc-labs/decision-lab dlab-figure-style --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/pymc-labs/decision-lab.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/dlab/data/figure_style .cursor/skills/dlab-figure-style && 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 "dlab-figure-style" agent skill from https://github.com/pymc-labs/decision-lab/tree/main/dlab/data/figure_style into .cursor/skills/dlab-figure-style/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dlab-figure-style", 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/pymc-labs/decision-lab.git --path dlab/data/figure_style--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 pymc-labs/decision-lab --skill dlab-figure-style -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install pymc-labs/decision-lab dlab-figure-style --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/pymc-labs/decision-lab.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/dlab/data/figure_style .gemini/skills/dlab-figure-style && 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 "dlab-figure-style" agent skill from https://github.com/pymc-labs/decision-lab/tree/main/dlab/data/figure_style into .gemini/skills/dlab-figure-style/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dlab-figure-style", 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 pymc-labs/decision-lab dlab-figure-styleInstalls 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 pymc-labs/decision-lab --skill dlab-figure-style -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/pymc-labs/decision-lab.git skills-src && mkdir -p .github/skills && cp -r skills-src/dlab/data/figure_style .github/skills/dlab-figure-style && 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 "dlab-figure-style" agent skill from https://github.com/pymc-labs/decision-lab/tree/main/dlab/data/figure_style into .github/skills/dlab-figure-style/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dlab-figure-style", 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 pymc-labs/decision-lab --skill dlab-figure-style -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install pymc-labs/decision-lab dlab-figure-style --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/pymc-labs/decision-lab.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/dlab/data/figure_style .opencode/skills/dlab-figure-style && 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 "dlab-figure-style" agent skill from https://github.com/pymc-labs/decision-lab/tree/main/dlab/data/figure_style into .opencode/skills/dlab-figure-style/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dlab-figure-style", 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.
dlab-figure-styledecision-lab house figure style for matplotlib. An agent skill from pymc-labs/decision-lab.
Dlab Figure Style is an agent skill from pymc-labs/decision-lab. decision-lab house figure style for matplotlib. Use whenever creating, styling, or saving any matplotlib figure, chart, or plot. The environment is already styled — this skill covers only the rules the style config cannot enforce.
Its SKILL.md is about 1.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 3 other files (for example `__init__.py` and `dlab_plotstyle.py`).
It sits in Data & Analytics, covering Data visualization. It works with Matplotlib. The repository describes itself as: Run tested, autonomous agent workflows on your data for meaningful decision-making. The licence is Apache-2.0.
9 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit a68f132. It shows what the files ask for, not the result of running them.
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.
Ships script files (Python), which the agent can run.
From the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Dlab Figure Style loads about 1.1k tokens when it runs. Until then it costs about 62 tokens; SKILL.md has 512 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.
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.
The full file from pymc-labs/decision-lab at commit a68f132, republished under its Apache-2.0 licence (© pymc-labs). 512 words, ~1,061 tokens.
.claude/skills/dlab-figure-style/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.The session environment already styles every matplotlib figure (palette color cycle, fonts, clean spines, marker edges, legend, figure size) via an rc file activated through the MATPLOTLIBRC environment variable. Your job is to NOT undo that, and to follow the few rules the rc cannot enforce.
sns.set_theme(), sns.set(), sns.set_style(), or
plt.style.use(...) — one such call silently destroys the entire house
style. If you use seaborn, import it plainly (import seaborn as sns) and
pass colors/axes explicitly; do not apply its themes.rcParams in plotting scripts. The environment already did."red", "tab:blue").
Colors come from the palette (below) or from the default cycle.import dlab_plotstyle # activates clean fill_between edges; always import it
from dlab_plotstyle import PALETTE, PALETTE_LIGHT, PALETTE_DARKPALETTE.PALETTE_LIGHT for filled bands and backgrounds, PALETTE_DARK for
emphasis and outlines.ax.scatter(..., color=X) sets face AND edge to X,
silently cancelling the thin white marker edge. For colored markers pass
edgecolor="white", linewidth=0.6 explicitly; for white/open markers use
facecolor="white", edgecolor=PALETTE_LIGHT["petrol"], linewidth=0.9.fill_between one z-layer below
its own line (zorder=2*i for the fill, 2*i+1 for the line), reference
lines (axhline/axvline) on top.ax.grid(True)) or explicit reference lines (axhline/axvline
for bounds, quartiles, thresholds), never the two together. If you draw
reference lines, turn the grid off for that panel.frameon=True
to legend() and never wrap text/annotations in a bbox box (both are
stripped by the environment anyway). Keep legends at their default size.ax.set_xlim(x.min(), x.max())), then call
dlab_plotstyle.add_axis_end_tick_caps(ax) so fitted spine ends read as
bounded. Leave padding alone on categorical axes, bars, and distributions.dlab_seq
(the default) for magnitudes, cmap="dlab_div" for signed data such as
correlations or deltas. Never use jet, rainbow, or viridis.Audit your plotting scripts programmatically — do not eyeball:
grep -nE "set_theme|set_style|plt\.style\.use|frameon=True|bbox=dict|color=[\"']#|color=[\"'](red|blue|green|orange|purple|tab:)" *.pyAny hit is a violation: replace with palette names or the default cycle.
© pymc-labs, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 3 other files in dlab/data/figure_style of pymc-labs/decision-lab.
Open the folder on GitHubat commit a68f132
Dlab Figure Style 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 |
|---|---|---|---|---|---|---|
| Dlab Figure Style this skillpymc-labs/decision-lab | 199 | — | ~1.1k | Automated safety check: Pass | Apache-2.0 | |
| MatplotlibzLanqing/codex-claude-academic-skills | 4.7k | 17 repos | ~2.9k | Automated safety check: Pass | MIT | |
| Scientific Visualizationmims-harvard/OptimusKG | 147 | 19 repos | ~6.3k | Automated safety check: Pass | MIT | |
| SeabornzLanqing/codex-claude-academic-skills | 4.7k | 15 repos | ~4.9k | Automated safety check: Pass | BSD-3-Clause | |
| Plot From DataTrae1ounG/paper-plot-skills | 872 | 1 repos | ~583 | Automated safety check: Pass | None | |
| Scientific Figure MakingChenLiu-1996/figures4papers | 8.3k | — | ~557 | Automated safety check: Pass | Custom licence |
zLanqing/codex-claude-academic-skills
Low-level plotting library for full customization. An agent skill from zLanqing/codex-claude-academic-skills.
mims-harvard/OptimusKG
Create publication figures with matplotlib/seaborn/plotly. An agent skill from mims-harvard/OptimusKG.
zLanqing/codex-claude-academic-skills
Statistical visualization with pandas integration. An agent skill from zLanqing/codex-claude-academic-skills.
Trae1ounG/paper-plot-skills
Generate publication-quality matplotlib figures by selecting a pre-built paper style and substituting user data.
ChenLiu-1996/figures4papers
Covers publication-ready matplotlib figures for academic papers, slides, and reports—bars, trends, scatter, heatmaps, and multi-panel layouts—with this…
Trae1ounG/paper-plot-skills
Reproduce any academic paper figure from an uploaded image using accumulated style experience.
pymc-labs/decision-lab
Complete reference for decision-lab (dlab). An agent skill from pymc-labs/decision-lab.
pymc-labs/decision-lab
Methodology for probabilistic forecasting of when and whether a future event will occur.
Works with
Categories
decision-lab house figure style for matplotlib. An agent skill from pymc-labs/decision-lab. Dlab Figure Style is an agent skill from pymc-labs/decision-lab. decision-lab house figure style for matplotlib.
Dlab Figure Style fits situations like: saving any matplotlib figure; tasks that involve Data visualization.
Run `npx skills add pymc-labs/decision-lab --skill dlab-figure-style -a claude-code`. Or copy the skill folder (dlab/data/figure_style in pymc-labs/decision-lab) into .claude/skills/dlab-figure-style in your project. Claude Code loads it when a task matches its description.
Run `npx skills add pymc-labs/decision-lab --skill dlab-figure-style -a codex`. Or copy the skill folder (dlab/data/figure_style in pymc-labs/decision-lab) into .agents/skills/dlab-figure-style in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add pymc-labs/decision-lab --skill dlab-figure-style -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/dlab-figure-style, .gemini/skills/dlab-figure-style, .github/skills/dlab-figure-style and .opencode/skills/dlab-figure-style in your project.
Going by SKILL.md and its folder, Dlab Figure Style needs Python for the scripts in its folder. Our summary lists: Python 3.
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.
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.
Dlab Figure Style is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 1.1k tokens (SKILL.md is roughly 4.2k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Dlab Figure Style: Matplotlib (zLanqing/codex-claude-academic-skills, 4.7k stars), Scientific Visualization (mims-harvard/OptimusKG, 147 stars), Seaborn (zLanqing/codex-claude-academic-skills, 4.7k stars) and Plot From Data (Trae1ounG/paper-plot-skills, 872 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
pymc-labs (a GitHub organization) maintains it in pymc-labs/decision-lab, which has 199 GitHub stars. The repository holds 3 skills in this directory. The repository was last updated on September 25, 2026.
Source: pymc-labs/decision-lab on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.