Agent skill

Dlab Figure Style

by pymc-labs in pymc-labs/decision-lab

decision-lab house figure style for matplotlib. An agent skill from pymc-labs/decision-lab.

Apache-2.0Auto-check passedData & Analytics

Install Dlab Figure Style

skills CLI
$ npx skills add pymc-labs/decision-lab --skill dlab-figure-style -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install pymc-labs/decision-lab dlab-figure-style --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ 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-src

Use ~/.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/

Facts

Skill name
dlab-figure-style
GitHub stars
199
Token cost
~1.1k tokens
SKILL.md length
512 words
Files
4
Skills in repo
3
Repo updated
First seen
Licence
Apache-2.0

At a glance

decision-lab house figure style for matplotlib. An agent skill from pymc-labs/decision-lab.

  • Works in 9 steps: Scatter edge trap. ax.scatter(...,… → Band/line layering. Draw each series'… → Grid OR reference lines — never both. A… → …
  • Saving any matplotlib figure
  • SKILL.md covers Never override the house style, Use the palette by name, The rules the rc cannot enforce and Before finishing
  • Runs Python scripts from its folder

What it does

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.

When your agent uses it

  • Saving any matplotlib figure
  • Tasks that involve Data visualization

Example prompts

  • “/dlab-figure-style”

Requirements

  • Python 3

Workflow steps

9 steps, taken from the first numbered list in SKILL.md.

  1. Scatter edge trap. ax.scatter(..., color=X) sets face AND edge to X,
  2. Band/line layering. Draw each series' fill_between one z-layer below
  3. Grid OR reference lines — never both. A panel gets either a subtle
  4. Legends are frameless and text is boxless. Never pass frameon=True
  5. Axis limits. On continuous scatter/line panels, fit limits to the data
  6. One legend per exhibit. In multi-panel figures, use one shared legend
  7. Low-contrast series need labels. Coral, sky, and sand sit below 3:1
  8. Scatter with many categories. Only the first three cycle colors are
  9. Heatmaps and images. Use the registered house colormaps: dlab_seq

What it can do on your machine

Read from SKILL.md and the folder at commit a68f132. 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 script files (Python), which the agent can run.

    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

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.

Always · name and description, kept in context so the agent knows when to use it
~62
When it runs · the whole SKILL.md, loaded when a task matches
~1.1k

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 pymc-labs/decision-lab at commit a68f132, republished under its Apache-2.0 licence (© pymc-labs). 512 words, ~1,061 tokens.

Download SKILL.mdSave it as .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.
name
dlab-figure-style
description
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.

decision-lab figure style

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.

Never override the house style

  • NEVER call 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.
  • Do NOT set fonts, spine visibility, furniture colors, grid style, or rcParams in plotting scripts. The environment already did.
  • Do NOT hard-code hex colors or CSS color names ("red", "tab:blue"). Colors come from the palette (below) or from the default cycle.

Use the palette by name

python
import dlab_plotstyle  # activates clean fill_between edges; always import it
from dlab_plotstyle import PALETTE, PALETTE_LIGHT, PALETTE_DARK
  • For order-free multi-series plots, pass no colors at all — the default cycle assigns well-separated, colorblind-safe colors in a validated order.
  • Set a color explicitly only when a series has fixed meaning across figures (the same channel/category in every exhibit), and take it from PALETTE.
  • Use PALETTE_LIGHT for filled bands and backgrounds, PALETTE_DARK for emphasis and outlines.

The rules the rc cannot enforce

  1. Scatter edge trap. 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.
  2. Band/line layering. Draw each series' 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.
  3. Grid OR reference lines — never both. A panel gets either a subtle grid (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.
  4. Legends are frameless and text is boxless. Never pass 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.
  5. Axis limits. On continuous scatter/line panels, fit limits to the data (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.
  6. One legend per exhibit. In multi-panel figures, use one shared legend for the whole figure, not a repeated per-panel legend. Never label every data point — direct-label selectively.
  7. Low-contrast series need labels. Coral, sky, and sand sit below 3:1 contrast on white — when one carries a key series, add a direct label or annotation so identity never rides on color alone.
  8. Scatter with many categories. Only the first three cycle colors are mutually distinguishable in unordered point clouds. For scatter plots with more than 3 categories, add marker shapes or facet into small multiples.
  9. Heatmaps and images. Use the registered house colormaps: dlab_seq (the default) for magnitudes, cmap="dlab_div" for signed data such as correlations or deltas. Never use jet, rainbow, or viridis.
Show full SKILL.md (23 more words)Show less

Before finishing

Audit your plotting scripts programmatically — do not eyeball:

bash
grep -nE "set_theme|set_style|plt\.style\.use|frameon=True|bbox=dict|color=[\"']#|color=[\"'](red|blue|green|orange|purple|tab:)" *.py

Any 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

Files

SKILL.md and 3 other files in dlab/data/figure_style of pymc-labs/decision-lab.

  • SKILL.md
  • __init__.py
  • dlab_plotstyle.py
  • matplotlibrc

Open the folder on GitHubat commit a68f132

Compare with similar skills

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.

Dlab Figure Style compared with similar skills
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SeabornzLanqing/codex-claude-academic-skills4.7k15 repos~4.9kAutomated safety check: PassBSD-3-Clause
Plot From DataTrae1ounG/paper-plot-skills8721 repos~583Automated safety check: PassNone
Scientific Figure MakingChenLiu-1996/figures4papers8.3k—~557Automated safety check: PassCustom licence

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Works with

Questions about Dlab Figure Style

What does Dlab Figure Style do?

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.

When should I use Dlab Figure Style?

Dlab Figure Style fits situations like: saving any matplotlib figure; tasks that involve Data visualization.

How do I install Dlab Figure Style in Claude Code?

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.

How do I install Dlab Figure Style in Codex?

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.

Can I use Dlab Figure Style 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 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.

What does Dlab Figure Style need to run?

Going by SKILL.md and its folder, Dlab Figure Style needs Python for the scripts in its folder. Our summary lists: Python 3.

Does Dlab Figure Style 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 Dlab Figure Style 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 Dlab Figure Style use?

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.

How many tokens does Dlab Figure Style use?

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.

What are the alternatives to Dlab Figure Style?

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.

Who maintains Dlab Figure Style?

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.