academic-figure-skill

Academic Figure Skill Hub

Academic Figure Skill generates publication-grade scientific figures for Nature/Cell/Science family journals. Every figure starts from the scientific question, not from a template.

Design Principles

1. One figure, one core message. CNS reviewers skim fast. A figure must convey its main conclusion in 3 seconds. Remove gridlines, borders, and legend entries that dilute the core message. Default to minimal, not maximal.

2. Restrained color > abundant color. Default palettes (matplotlib tab10, ggplot2 hue_pal, Excel colors) signal "not designed" instantly. Use 2-4 semantic main colors + 1 accent. See references/color-palettes.md.

3. Design for print, not screen. Journal column widths are fixed (89 mm single, 183 mm double). Set figure dimensions at creation time — never scale down post-render.

4. Vector first, raster as fallback. Line art, scatter plots, bar charts → PDF/SVG/EPS. Only true raster content (heatmap color blocks, micrographs) should use TIFF/PNG at ≥300 dpi.

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Complete Workflow

Follow this closed loop for every request. Never skip steps. Never assume the user's question.

User request received
       │
       ▼
  Step -1: Understand the Task ←── DISPATCH FIRST. If the user gave data but
       │                         didn't say what they want to learn, ASK.
       ▼
  Step 0: Parse Data, Match to Task ←── Data parsing is directed by the
       │                                question, not by a template.
       ▼
  Step 1: Recommend & Justify ←── "To answer your question, you need these
       │                         N panels. Here's why." N depends on the
       ▼                         question, not on a fixed number.
  User Confirms ──→ No → Refine recommendation
       │
       ▼ Yes
  Step 2: Runtime & Environment ←── Detect Python + R availability.
       │                            Install or configure if missing.
       ▼
  Step 3: Style Baseline Injection ←── typography + color + export blocks.
       │                               Copied VERBATIM into every script.
       ▼
  Step 4: Production Asset Scan ←── ls assets/figures/. For EVERY panel in
       │                            the plan, check matching scripts.
       ▼
  Step 5: Generate ←── COPY-FIRST for matching scripts → native run.
       │              No match → cross-type inherit.
       ▼
  Step 5.5: Validate Data ←── Data sanity checks BEFORE rendering.
       │
       ▼
  Step 6: QA Protocol ←── 4-pass QA (AP-0..AP-7, CL-1..CL-7,
       │                  VI-1..VI-6, VV-1..VV-5). Fix → re-render.
       ▼
  Step 7: Deliver ←── Vector PDF master + 300dpi PNG preview
                      + QA report + statistics report