/publication-chart-skill

name: publication-chart-skill
description: This skill should be used when the user asks for a publication-quality scientific figure or table, wants help choosing the right chart for results, needs a paper-ready pubfig or pubtab workflow, wants a figure + companion table for a results section, wants an Excel sheet turned into publication-ready LaTeX, or wants an existing scientific figure/table reviewed and upgraded.
version: 0.2.0

Publication Chart Skill

Goal

Use this skill to turn research results into **publication-grade figures and tables** with an end-to-end workflow.

Primary production stack:

• **`pubfig`** for figures
• **`pubtab`** for publication tables

This skill covers the full delivery chain:

1. understand the scientific communication goal, 2. choose the right artifact type, 3. map the task to `pubfig`, `pubtab`, or both, 4. generate concrete runnable instructions, 5. export paper-ready assets, 6. run publication QA, 7. propose targeted revisions.

Use this skill when

Trigger this skill for requests like:

• “make a publication-quality figure”
• “choose the right chart for these results”
• “turn these results into a paper-ready figure”
• “make a benchmark / ablation / calibration / forest / heatmap / scatter / line / bar figure”
• “make a benchmark / appendix / ablation table from Excel”
• “convert this Excel table into publication-ready LaTeX”
• “prepare one summary figure plus one companion table for the results section”
• “review and improve this scientific figure/table”
• “I already have a weak chart / screenshot / draft plot — make it publication-ready”
• “export panels for a paper figure”

Do not use this skill for

Do **not** use this skill when the task is mainly:

• manuscript prose writing,
• statistical testing without artifact design,
• raw exploratory analysis with no publication deliverable,
• Figma-first layout work before the figure/table content is solid.

For simple composite assembly after the figure content is already strong, use the optional secondary workflow in `references/composite-assembly.md`.

Primary contract

Inputs

Expect some combination of:

• the scientific communication goal,
• available data shape,
• venue or style constraints,
• whether the artifact is a figure, table, or mixed deliverable,
• optional existing assets such as code, spreadsheets, `.tex`, screenshots, or draft plots,
• whether the user needs a first draft, a publication-ready artifact, or a review/revision pass.

Outputs

The minimum useful output is:

• the recommended figure/table form,
• the recommended `pubfig` / `pubtab` route,
• a minimal runnable code snippet or CLI command,
• explicit export filenames and formats,
• a publication QA summary,
• and, when needed, a revision plan.

Default workflow

0. Probe the environment and artifact state

Before generating anything, identify:

• whether `pubfig` or `pubtab` is actually available,
• whether the user already has code / spreadsheets / `.tex` / screenshots,
• whether the deliverable is a fresh build or a revision,
• whether the result needs exact values, fast visual perception, or both.

Prefer the smallest environment check that helps execution. When the bundled helper script is available, use it first:

• `python3 scripts/ensure_publication_tooling.py --require pubfig --json`
• `python3 scripts/ensure_publication_tooling.py --require pubtab --json`

Equivalent manual checks are still acceptable when needed:

• `python -c "import pubfig; print(pubfig.__version__)"`
• `python -c "import pubtab; print(pubtab.__version__)"`
• `pubtab --help`

Report the result clearly as **available** or **missing**.

If a dependency is missing and the task requires runnable execution:

• **auto-install it by default**,
• prefer the user’s active environment instead of guessing a random global interpreter,
• use `python3 scripts/ensure_publication_tooling.py --require ...` as the default bundled route when the script is present,
• let that helper choose `uv` vs `python -m pip` against the active interpreter,
• re-run the availability probe after installation,
• and only then continue with the artifact workflow.

Equivalent concrete commands include:

• `python3 scripts/ensure_publication_tooling.py --require pubfig`
• `python3 scripts/ensure_publication_tooling.py --require pubtab`
• `uv pip install pubfig`
• `uv pip install pubtab`
• `python -m pip install pubfig`
• `python -m pip install pubtab`

If auto-install fails, report the exact failure and then degrade gracefully.

Do not block on a full environment audit.

1. Classify the task

Classify the request along these axes:

• **artifact type**: figure / table / mixed deliverable
• **maturity**: exploratory draft / publication-ready generation / revision of an existing artifact
• **structure**: single panel / multi-panel / figure-plus-table package
• **evidence mode**: pattern perception / exact value lookup / both

Do not jump into plotting code before the communication target is clear.

Before plotting research results, lock the evidence contract:

• primary scientific claim,
• unit of analysis,
• primary metric and metric direction,
• whether repeated rows are independent,
• missing cells or incomplete comparison blocks,
• error-bar basis: subject, subject-task, fold, seed, run, or bootstrap sample,
• whether exact values need a companion table,
• whether the current evidence allows a winner/significance claim.

If these are unclear, ask or produce an audit recommendation instead of a polished figure. Do not create a paper-ready plot while the unit of analysis, missing-cell handling, or error-bar basis is unresolved.

2. Choose the representation

Choose the representation based on the scientific claim, not novelty or visual flair.

Common families:

• **comparison** — grouped scatter, bar, line comparison, benchmark summary, companion table
• **ablation** — grouped comparison, dumbbell, paired comparison, compact table
• **distribu