nature-figure — Claude skill: manuscript figures in Python or R

---
name: nature-figure
description: >-
  Create, revise, audit, and export manuscript scientific figures in Python or R.
  Use for 论文配图、科研绘图、多面板图 and submission-ready plots, or explicitly
  requested AI-generated graphical abstracts and mechanism schematics. Not for
  interactive dashboards, data cleaning, or statistics-only analysis.
---

# Nature Figure Making — Router

## Routing protocol

For a new task, load the core and matching resources below. Reuse already loaded guidance on follow-ups; load more only when the task needs it.

### 0. Check for graphical-abstract and AI-schematic routes

For every graphical-abstract planning, generation, revision, or audit task that
uses AI, read
[references/ai-graphical-abstract-workflow.md](references/ai-graphical-abstract-workflow.md)
first. It owns the message/audience brief, composition and palette workflow,
policy gate, human scientific review, disclosure boundary, and provenance
requirements. A Nature Careers article is practitioner advice, not submission
clearance; verify the current official policy for the exact target journal.

If the request is planning or auditing only, do not ask for Python or R unless
the user also asks to render or revise a data-driven figure.

If the user explicitly asks to generate a manuscript schematic, graphical abstract, mechanism diagram, concept illustration, or paper schematic with OpenRouter, GPT Image 2, an image-generation API, or similar wording, do **not** ask "Python or R?". This is a non-plotting AI-schematic route.

For this route:

1. Read [manifest.yaml](manifest.yaml) and the `always_load` files.
2. Read [references/ai-graphical-abstract-workflow.md](references/ai-graphical-abstract-workflow.md).
3. Read [references/openrouter-image-generation.md](references/openrouter-image-generation.md).
4. Use [scripts/generate_openrouter_schematic.py](scripts/generate_openrouter_schematic.py) when the user wants a real API call or a reproducible payload.
5. Treat output as a draft schematic / graphical abstract, not as a quantitative data panel. Do not invent experimental values, author logos, institutional marks, or unsupported mechanisms. Keep internal usefulness separate from submission eligibility.

If the user wants a hand-built, editable vector schematic (architecture, pipeline, agent or model block diagram, or mechanism flow) rather than an AI-generated image, do **not** ask "Python or R?". Read [references/vector-schematic-workflow.md](references/vector-schematic-workflow.md) and [references/vector-schematic-style-rules.md](references/vector-schematic-style-rules.md), confirm the brief with the user before drawing, and run `scripts/audit_svg_schematic.py` on the SVG before delivery. This route never plots data.

Only continue to the Python/R backend gate for plotting, charting, data visualization, or manuscript figure assembly tasks that are not explicit OpenRouter AI image-generation requests.

### 1. Load the manifest and the core layer

Read [manifest.yaml](manifest.yaml). It declares the `backend` axis, the allowed values, and the file paths each value maps to.

Also read every file listed under `always_load` (`static/core/contract.md` and `static/core/stance.md`). These hold the figure contract, the backend gate, the missing-runtime rule, the privacy rule, and the default operating stance that apply to every figure job.

### 2. Resolve the plotting backend

Backend selection applies only to rendering or editing plotting code. Reuse a choice already established in the same task and its follow-ups; do not ask again merely because a new message omits the language. Read-only figure review and backend-independent data inspection may proceed without this choice. If the backend remains unresolved, retain the one-time Python/R question and pause only dependent plotting steps. Explicit approval requirements and backend exclusivity remain in force.

Resolve the plotting backend from the current task before consulting the saved default. Decide the `backend` value in this order:

1. If the current request explicitly chooses Python or R, use that backend and save it with `scripts/nature_figure_backend.py set python` or `scripts/nature_figure_backend.py set r`.
2. If the request provides a clearly language-specific input file/workflow, use that backend and save it.
3. Otherwise reuse a Python/R choice already established in this task. If none exists, run `scripts/nature_figure_backend.py get` and use a returned `python` or `r` preference.
4. If neither a task choice nor a saved preference exists, ask exactly one concise question — **Python or R? I will remember this as your default.** — and pause only dependent plotting steps. After the user answers, save the answer before proceeding.

- `python` — matplotlib / seaborn.
- `r` — ggplot2 / patchwork / ComplexHeatmap.

Do not guess or choose a backend by aesthetics alone. Only recommend a backend when the user explicitly asks you to choose; then use `references/backend-selection.md`, state the reason, save the selected backend, and proceed. Once selected, the backend is **exclusive** for all drawing, previewing, exporting, and visual QA (see `core/contract.md`). This gate does not apply to the explicit OpenRouter AI-schematic route above.

### 3. Load the matching backend fragment

After the backend is resolved, Read the mapped fragment (`static/fragments/backend/python.md` or `static/fragments/backend/r.md`). It carries the backend-only execution rule and the publication quick-start (rcParams/theme and export helper). Do **not** load the other backend's fragment.

### 4. Build the figure using the loaded material

Apply the loaded material in this order:

1. Figure contract (`core/contract.md`) — write the core conclusion, map the evidence chain, classify the archetype, set the journal/export contract, before any code.
2. Multi-panel evidence architecture — when planning, restructuring, or auditing a labelled multi-panel figure, load `references/multipanel-evidence-architecture.md`. Make the figure answer one Results-level scientific question; assign panels different inferential roles, not merely different metrics. When figure order must follow the manuscript argument, also load `../nature-shared/core/nature-results-discussion.md`.
3. Default stance (`core/stance.md`) — archetype-first composition, hero panel, restrained palette, statistics/integrity as part of the figure.
4. Backend fragment — the exclusive Python or R quick-start and execution rule.
5. Template adaptation — when reusing built-in original examples, licensed external material, or user-provided plotting code, load `references/asset-adaptation.md` before mapping data or changing the script.
6. Rendered QA and delivery preflight — load `references/qa-contract.md`, run the render-time panel-alignment gate for every multi-panel figure, `scripts/validate_figure.py` on the plotting source, `scripts/audit_pdf_text.py` on the exported PDF, and `scripts/audit_figure_collisions.py` on the same final PDF. Then inspect every panel and the complete figure at final physical size. Automated checks do not replace the panel-by-panel uncertainty, salience, spacing, and ambiguity audit.

For every figure containing two or more comparable panels, measure the **final
rendered plot-area rectangles** before export and preserve the alignment JSON.
Python figures must call `require_matplotlib_panel_alignment()` from
`scripts/audit_panel_alignment.py` after the final layout draw. R/patchwork
figures must source `scripts/panel_alignment.R`, write the patchwork layout
manifest at the final export dimensions, and run the same backend-neutral JSON
auditor. Use a default physical tolerance of `1.5 pt` for shared edges, widths,
heights, panel-label anchors and repeated gutters. `FIX BEFORE DELIVERY` or exit
code `1` blocks export; `NOT AUDITABLE` or exit code `2` blocks any claim that
alignment passed. A horizontal row of three or four equal-grid-span panels must
have equal final plot-area widths as well as equal heights and gutters; an
intentional unequal-width design requires a recorded `panel-width` exemption.
Structured unequal-span grids—including two stacked panels
beside one panel spanning both rows, in either column—must be inferred from
shared grid start/stop boundaries and checked automatically. Nested grids,
free-positioned hero panels, insets and colorbars may be excluded only through
explicit comparable groups or a recorded exemption with a reason. Do not
weaken the global tolerance to hide one intentional exception.

After every generated or revised Python/R scientific figure, export the final
PDF and run the collision audit again; this is mandatory after any change to
data geometry, text, fonts, legends, annotations, axes, error bars, panel size
or layout, not only at final submission. Use:

```bash
python skills/nature-figure/scripts/audit_figure_collisions.py figure.pdf \
  --json-out figure.collision-audit.json \
  --overlay-pdf figure.collision-audit.pdf
```

- `FIX BEFORE DELIVERY` or exit code `1`: repair the figure, re-export with the
  selected plotting backend, and rerun all rendered QA.
- `REVIEW REQUIRED`: inspect every WARN at final physical size; record why an
  intentional overlay is acceptable. Use `--strict` when WARN must block.
- `NOT AUDITABLE` or exit code `2`: report the dependency/PDF blocker and do not
  claim collision validation. Install `requirements.txt` when PyMuPDF is absent.

The collision audit reads PDF geometry for both Python and R output. It does not redraw
the scientific figure or authorize cross-backend plotting. Its optional marked
PDF is a QA-only diagnostic artifact and must never replace the selected
backend's source or submission files.

When the target is the flagship journal Nature, also load
`references/nature-article-requirements.md`. It separates initial-review files
from accepted-in-principle main and Extended Data production contracts and owns
the flagship legend limit.

When the target is Nature Machine Intelligence, instead load
`../nature-shared/journal-formats/nature-machine-intelligence.md`. Apply its
combined six-item main display budget, ten-item Extended Data maximum,
initial-versus-production boundary, 300-dpi/180-mm production checks and source-
data contract. NMI's current live pages do not assign a standalone per-legend
number, but its official 2018 brief guide set a historical advisory ceiling of
fewer than 300 English words per complete figure legend. Count the whole legend,
not each panel; aim for 150–250 words and keep it below 300 unless the live
submission system or editor gives a newer instruction. Do not import flagship
Nature's limit.

The chart serves the scientific logic; aesthetic polish is subordinate to making the core conclusion clear, defensible, and reviewable.

### 5. Reach for references only when needed

The files under `references/` are deep references, not defaults. Open them on demand per the `references.on_demand` table in the manifest — for example `references/figure-contract.md` to build the contract, `references/multipanel-evidence-architecture.md` to turn one Results-level question into complementary panel roles and a claim-escalating figure sequence, `references/asset-adaptation.md` to reuse a plotting template safely, `references/template-catalog.md` for validated Python CSV templates, `references/api.md` for the Python palette and numerical/layout safety helpers, `references/r-workflow.md` for R, `references/design-theory.md` for color/typography/export rationale, `references/common-patterns.md` and `references/chart-types.md` for layout/chart recipes, `references/nature-2026-observations.md` for real Nature page archetypes, `references/qa-contract.md` before final delivery, `references/nature-article-requirements.md` for exact flagship Nature stage and upload rules, `../nature-shared/journal-formats/nature-machine-intelligence.md` for exact NMI figure rules, `references/ai-graphical-abstract-workflow.md` for AI-assisted graphical-abstract planning, policy gating, human verification, and provenance, and `references/tutorials.md` / `references/demos.md` for worked examples.

Do not infer flagship Nature or NMI requirements from a Nature Communications
corpus or from the visual-style examples in this skill.