name: nature-figure
version: 2.8.0
description: >
  Declarative manifest for the static/dynamic split. SKILL.md uses this to
  decide which fragments to load for a given figure request. The main axis is
  the plotting backend (Python or R). The first plotting use should establish
  a saved user preference; later tasks reuse that preference unless the user
  explicitly changes it. The large body of design, API, pattern, and QA
  material stays in on-demand references.

always_load:
  # Skill-local core (figure does not use the prose-oriented nature-shared layer)
  - static/core/contract.md
  - static/core/stance.md

routes:
  graphical_abstract_workflow:
    detect: |
      Use this route whenever AI assists the planning, generation, revision, or
      audit of a graphical abstract. Planning and audit-only tasks do not need
      the Python/R backend gate. The reference owns the policy check,
      message/audience brief, visual design workflow, human verification,
      disclosure boundary, and provenance record.
    reference: references/ai-graphical-abstract-workflow.md
  openrouter_image_generation:
    detect: |
      Use this route only when the user explicitly asks for OpenRouter, GPT
      Image 2, image-generation API output, AI-generated paper schematics,
      graphical abstracts, concept illustrations, or mechanism diagrams. This
      route skips the Python/R backend gate because it generates a raster/vector
      image draft through an external image API rather than plotting data.
    reference: references/openrouter-image-generation.md
    script: scripts/generate_openrouter_schematic.py

  vector_schematic_workflow:
    detect: |
      Use this route when the user wants a hand-built, editable SVG schematic:
      system or method architecture, pipeline overview, agent or model block
      diagram, or mechanism or process flow. It skips the Python/R backend gate
      because it draws structure rather than plotting data. Confirm a written
      brief before drawing and run the SVG audit script before delivery.
    reference: references/vector-schematic-workflow.md
    script: scripts/audit_svg_schematic.py

preference:
  backend_script: scripts/nature_figure_backend.py
  config_env: NATURE_FIGURE_CONFIG
  default_config: ~/.config/nature-skills/nature-figure.json
  behavior: |
    For plotting tasks, first honor an explicit Python/R choice, then a clearly
    language-specific input workflow, then a choice established in this task,
    then the saved preference returned by the backend script. If none exists,
    ask the user once and save the answer. Pause only dependent plotting work.

axes:
  backend:
    detect: |
      BLOCKING GATE WITH PERSISTED PREFERENCE. Determine the plotting backend
      from an explicit user choice, a clearly language-specific input
      file/workflow, a choice established in this task, or the saved preference
      returned by scripts/nature_figure_backend.py get. If none resolves it, ask
      exactly one concise question — "Python or R? I will remember this as your
      default." — and pause dependent plotting steps while continuing independent
      inspection. Read-only review does not require a backend choice.
      Save the answer with the backend preference script
      before proceeding. Only recommend a backend when the user explicitly asks
      you to choose; in that case use references/backend-selection.md, state the
      reason, save the selected backend, then proceed. Once selected, the
      backend is exclusive for all drawing, previewing, exporting, and visual QA.
    values:
      python:  static/fragments/backend/python.md
      r:       static/fragments/backend/r.md
    multi: false

references:
  on_demand:
    - condition: convert a user request into core conclusion, evidence hierarchy, panel map, and review-risk checks
      path: references/figure-contract.md
    - condition: plan, restructure, or audit a multi-panel manuscript figure; decide panel roles or main-versus-Extended-Data placement; or align figure order with a Results evidence chain
      path: references/multipanel-evidence-architecture.md
    - condition: user has not chosen Python/R, asks for a recommendation, or a mixed Python/R workflow is possible
      path: references/backend-selection.md
    - condition: user chooses R or provides R scripts/templates/data
      path: references/r-workflow.md
    - condition: adapt a user-provided or private R template collection without exposing source paths
      path: references/r-template-index.md
    - condition: reuse or adapt a bundled example, plotting script, preview image, or user-provided template against new data
      path: references/asset-adaptation.md
    - condition: every generated or revised Python/R scientific figure, multi-panel alignment gate, rendered PDF collision audit, final delivery, revision package, rendered PDF text audit, panel-by-panel visual QA, microscopy/blot figure, or journal-specific audit
      path: references/qa-contract.md
    - condition: target is the flagship journal Nature, or exact initial-versus-production main/Extended Data figure and legend requirements are needed
      path: references/nature-article-requirements.md
    - condition: target is Nature Machine Intelligence, or exact NMI main-display, Extended Data, legend, source-data, initial-review, or production-figure requirements are needed
      path: ../nature-shared/journal-formats/nature-machine-intelligence.md
    - condition: typography, color theory, layout rationale, export policy
      path: references/design-theory.md
    - condition: Python PALETTE, helper function signatures, Matplotlib panel-alignment integration, validation rules
      path: references/api.md
    - condition: "Python layout patterns: hero panels, legend-only axes, dark image plates, asymmetric layouts"
      path: references/common-patterns.md
    - condition: "real Nature page archetypes: schematic-led composites, dark image plates, clinical triptychs, asymmetric hero layouts"
      path: references/nature-2026-observations.md
    - condition: "OpenRouter/GPT Image 2 route: AI-generated graphical abstracts, mechanism diagrams, paper schematics, or concept illustrations"
      path: references/openrouter-image-generation.md
    - condition: AI-assisted graphical abstract planning, generation, revision, audit, journal-policy check, scientific verification, disclosure, or provenance
      path: references/ai-graphical-abstract-workflow.md
    - condition: "vector schematic route: hand-built editable SVG architecture, pipeline, agent, or mechanism diagram, brief-then-draw workflow, and final-size SVG audit"
      path: references/vector-schematic-workflow.md
    - condition: "colour, line, arrowhead, container, legend, and common-failure conventions for SVG architecture or mechanism schematics"
      path: references/vector-schematic-style-rules.md
    - condition: writing or auditing figure/table legend text — Fig. N | title, panel style, stats in legend, Source Data boilerplate, attribution; load the flagship Nature requirements too when that is the target journal
      path: references/figure-legend-conventions.md
    - condition: "end-to-end walkthroughs: bars, trends, heatmaps"
      path: references/tutorials.md
    - condition: "validated Python CSV templates: volcano, ROC, marker dot plot, marginal density, or paired plot"
      path: references/template-catalog.md
    - condition: "radar, 3D sphere, fill_between, scatter, ablation-line, probability/manifold concept patterns"
      path: references/chart-types.md
    - condition: third-party figures4papers demo lookup, copyright-aware reference use, or original pattern reimplementation
      path: references/demos.md