Publication Chart SkillSAFE

Semi-automated research assistant for academic research and software development. Supports Claude Code, Codex CLI, Kimi Code CLI, and OpenCode across ideation, coding, experiments, writing, and publication.

The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.

---
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

SAFEgrade B · trust 89/100 Nothing in the source contradicts what it says it does. Grade A is reserved for packages that have also passed the behavioural sandbox.

What the source does

Filesystem

none-observed

Network

none-observed

Shell

declared (1 observation(s))

Dependencies

pinned

Secrets in source

none-found

Findings (1)

module = importlib.import_module(package)

Gates applied: no_behavioural_pass.