Imported from xxx1766/lucky-research (.claude/skills/figure-tool/SKILL.md). Install upstream with npx skills add xxx1766/lucky-research --skill figure-tool. Copyright stays with the author.

figure-tool

Source spec: docs/superpowers/specs/2026-05-13-figure-tool-design.md. Read it before the first invocation in any session.

Required style prelude: also read .claude/skills/paper-architect/references/latex-conventions.md sections hard-rules (no ;, no ---/--) and tables-and-figures (vector PDF, grayscale-resilient, ≤6 colors, arrow flow, font size between body and caption, caption answers "what is this"). The rules apply to figure captions, axis labels, legend text, and any text label inside a structural SVG.

Mental model

A figure is one of two kinds (structural / data) under one of two scopes (paper / experiment).

                       /figure new <slug>
                              │
              ┌───────────────┴───────────────┐
        scope=paper                    scope=experiment
              │                                │
   outputs/papers/<v>/<d>/         outputs/experiments/<exp>/repo/
   figures/<slug>.{svg,pdf,png}    figures/<vN.M>/<slug>.{svg,pdf,png}   (data)
                                   figures/_arch/<slug>.{svg,pdf,png}    (structural)

Reference figures (style inspiration) are global: inputs/figure-refs/<slug>/{image, note.md} + AgentDB project/figure-refs/<slug>.

Evidence-first & QA (always applies)

Same anti-fabrication spine as /paper write / /paper verify, applied to figures. Adapted from the academic-figure skill of joshua-zyy/academic-paper-writer.

Never fabricate. A figure serves a claim — it is never ornament.

• Data plots: plot only user-provided experiment data or already-verified numbers. Never synthesise plausible-looking points to fill a curve. If the data isn't on hand, stop and tell the user which file/metric is missing rather than inventing it (mirrors the [DATA_NEEDED] discipline in prose).
• Architecture / structural diagrams: draw only modules, connections, and data flows that the actual code (at the bound repo SHA) or the paper supports. Don't invent a block or an arrow to make the diagram look complete. Mark any element you can't confirm with a literal [VERIFY_ARCH: <what's unconfirmed>] text node in the SVG and list it in the note's body — the user resolves it before the figure ships, exactly like a prose placeholder.

Data-plot QA checklist (verify before declaring a data figure done):

1. No rainbow / jet colormap — use the resolved academic palette only.
2. A truncated axis (non-zero origin) is explicitly marked / annotated.
3. Error bars / bands state their type in the caption (std / SEM / 95% CI).
4. Series are distinguishable in grayscale — never hue alone (pair with dash / marker), per the existing latex-conventions.md rule.
5. Panel labels (a/b/c) and the caption serve the figure's claim.

Surface this checklist's result in the /figure new data-path output so the user sees it was applied.

Cursor reads (always do this first)

At every /figure ... invocation, read both cursors:

• mcp__claude-flow__memory_retrieve namespace=project, key=paper-context.current → expect {"venue": "...", "direction": "..."} or absent
• mcp__claude-flow__memory_retrieve namespace=project, key=experiment-context.current → expect {"slug": "..."} or absent

Call research_assistant.figures.paths.resolve_scope(paper_ctx=..., experiment_ctx=..., cli_scope=...) (all three args are keyword-only):

• NoScopeError → tell the user to run /paper venue or /experiment init.
• AmbiguousScopeError → ask "paper or experiment?" (step 0).

Subcommand router

/figure recommend flow

A read-only decision-support subcommand. Does not write any file; prints a recommendation and surfaces the natural next call (/figure new <slug>).

1. Resolve scope (cursor reads above) — needed only for the palette hint in the recommendation. Do not error on NoScopeError; the recommender is useful even before a paper or experiment exists.
2. Collect inputs (plain text, no AskUserQuestion per the feedback_decision_ui memory):
   • Data snippet — paste an Excel/CSV table, or a 2–3 line description of variables, axes, sample count, distribution shape.
   • Core conclusion — one sentence on what the figure must demonstrate.
3. Read references/chart-recommender-prompt.md as the prompt prelude (it embeds the 19-chart academic library verbatim).
4. Apply the prompt with the stitched # Input block.
5. Print the model's four-section response (推荐方案 · 核心理由 · 视觉设计 规范) and the hand-off line: Next: /figure new <slug> (kind=data, chart=<推荐>).

/figure new 6-step flow

Collision guard (before Step 1). After scope is resolved, check whether the target figures dir already holds <slug>.note.md (or <slug>.svg / plot_<slug>.py). If so and the user did not pass --force, refuse with figure '<slug>' already exists in <scope> — pass --force to overwrite and stop (matches the Error-policy row). With --force, proceed and overwrite. Without this guard the render steps silently overwrite an existing figure.

Step 0 — scope (only if AmbiguousScopeError) Ask in plain text (per memory rule on multi-option research picks): "Paper or experiment?" — accept paper / experiment. Persist only to this invocation.

Step 1 — intent Ask: "这张图要让读者一秒看懂什么？" — one sentence. Store as the future intent: frontmatter.

Step 2 — kind Ask: "structural 还是 data?" — accept structural / data. If data, also ask which experiment version (defaults to the latest if scope=experiment).

Step 3 — references

1. Build a query from {intent} {kind} + intent-extracted keywords.
2. Call mcp__claude-flow__memory_search namespace=project/figure-refs, query, topK=5.
3. Show up to 3 candidates as: [N] <slug> tags: ... palette: <ANSI swatches> why: <first line of why_i_like_it>.
4. If the library is empty, skip silently. In any case, also accept inline paths/URLs (+ <path> or + <url>).
5. Record the selected slugs for the future refs: field.

Step 4 — size Read venue's _venue.md (if it exists at outputs/papers/<v>/_venue.md) for column widths. Offer 4 options:

[1] single-column         (3.3 in × auto)
[2] double-column-half    (6.5 in × 3.0 in)
[3] double-column-full    (7.0 in × auto)
[4] custom                (you type W H in inches)

Step 5 — palette

1. Resolve via palette.load_palette_for_direction(figures_dir, fallback=<venue-recommended>).
2. If the direction has _palette.yml, default = that palette; also offer 2 alternatives from shipped (paper-trio, paper-extended).
3. Else, offer 3 shipped palettes filtered by kind and size (heuristics: single-column → mono/trio first; multi-line ablation → extended; poster/slides → dark-on-light).
4. Render each option with palette.ansi_swatch(spec).

Step 6 — render

For kind=structural:

1. Construct the prompt for SVG generation, including:
   • Inkscape conventions from spec section 5.1 (viewBox in 96 DPI inches, namespace, labelled layers, <defs>-centralised styles).
   • Selected refs as multimodal image inputs.
   • The palette's hex colors and slot names.
   • The intent sentence.
   • Project conventions (from latex-conventions.md section tables-and-figures): vector-only (no raster embeds), grayscale-resilient (test by removing color — lines stay distinguishable by dash pattern, marker shape, or position), ≤6 colors total consistent within functional modules (same color means the same role across all figures in the paper), arrows flow in one general direction (left-to-right or top-to-bottom), prefer visual encodings over text labels for repeated information, figure text size between body and caption (typically 7–8 pt in two-column).
   • Architecture-diagram style (from references/architecture-diagram-prompt.md): flat-vector aesthetic in the DeepMind / OpenAI mold — pure-white background, soft palette, simple icons, English-only text labels, no long sentences inside boxes, no 3D shadows / sketch lines / photo-realism. This prelude carries the visual-style guidance adapted from the awesome-ai-research-writing "论文架构图" entry; reproduce the Visual Constraints block verbatim when constructing the prompt.
   • Hard rules for any text inside the SVG (labels, legends, annotations): no ; as punctuation, no --- or -- as prose punctuation. Restructure with commas or split labels.
   • Evidence-first (see "Evidence-first & QA" above): draw only code/paper-supported modules and flows; mark anything unconfirmed as a [VERIFY_ARCH: …] text node and record it in the note body — never invent structure to look complete.
2. Decide D2 scaffold opt-in: if structure is genuinely auto-layout (boxes + arrows), generate a .d2 source first, call d2.scaffold_to_svg(...). If it returns None, fall back to raw SVG generation. If success, set backend: d2-scaffolded; otherwise backend: raw-svg.
3. Write <slug>.svg to the resolved figures dir.
4. Call figures.export.export(svg_path). On ExportError, leave the SVG and tell the user to inspect.
5. Write <slug>.note.md via figures.note.write_note(note_path, FigureNote(...), body="").
6. If scope == "experiment", call research_assistant.experiments.append_figures_to_version(slug=<exp-slug>, version=<vN.M>, figure_stems=["repo/figures/_arch/<slug>"]). Structural figures are non-versioned — they live under repo/figures/_arch/ (see experiment_figures_dir(slug, version=None)), so the link-back stem must use _arch/, not <vN.M>/ (that form is only correct for the data branch).
7. Print the LaTeX include snippet (see "Insert snippet" below).

For kind=data: 0. Trajectory shortcut. If the intent contains "trajectory", "training curve", "metric over runs", or similar, and either (a) the user said "send to chat / paste into HTML" or (b) matplotlib is unavailable, reach for references/trajectory-svg-recipe.md instead of matplotlib. That recipe is a zero-dependency pure-Python SVG (~80 lines, adapted from Orchestra-Research/AI-Research-SKILLs MIT 0-autoresearch-skill). Write plot_<slug>.svg.py (driver) + <slug>.svg next to it; skip the cairosvg / mplstyle path; set backend: trajectory-svg in the note. For all other "metric over time" plots, fall through to the matplotlib path below.

1. Write plot_<slug>.py to the scripts dir for this scope:
   • paper: outputs/papers/<v>/<d>/figures/_scripts/plot_<slug>.py
   • experiment: outputs/experiments/<exp>/repo/scripts/plot_<slug>.py
2. Script header includes a docstring with data source (path / columns / metric) confirmed in Step 1+3.
3. Script imports from research_assistant.figures.save import save_all and uses matplotlib.style.use(<absolute-path-to-mplstyle>) for the chosen palette. save_all writes .pdf + .png (no .svg — for data figures the script itself is the canonical source). 3a. Project conventions (from latex-conventions.md section tables-and-figures):
   • Axis labels, legend entries, and tick labels obey hard rules: no ;, no ---/--.
   • Math inside labels uses built-in operators (\arg\max, \log) and \textrm{} for multi-letter names (\textrm{softmax}).
   • Use the resolved palette's colors and the palette mplstyle; do not hardcode hex.
   • Limit visible series to ≤6 and design for grayscale: pair each color with a distinct marker or dash pattern so the figure survives black-and- white printing.
4. Jupyter-friendly: if the user prefers to iterate in a notebook, develop in Jupyter and export to .py via jupytext --to py <name>.ipynb or File → Save As → .py. The committed plot_<slug>.py stays the source of truth; the figure-tool does not generate .ipynb files.
5. Execute the script (Bash: python <path-to-plot-script>). Pipe output; if non-zero exit, surface stderr. Evidence-first: the script must read real data (a CSV/TSV path or values the user confirmed in Step 1+3). Never hardcode invented numbers to produce a nice-looking curve — if the data isn't available, stop and name the missing file/metric.
6. Run the data-plot QA checklist ("Evidence-first & QA" above): palette (no rainbow/jet), axis-truncation marked, error-bar type stated, grayscale- distinguishable, labels serve the claim. Note any item that fails.
7. Write <slug>.note.md via write_note(...) with backend: matplotlib.
8. Experiment scope: same append_figures_to_version link-back as structural.
9. Print the LaTeX include snippet, followed by a one-line QA summary (QA: palette ✓ · axis ✓ · error-bars n/a · grayscale ✓).

Insert snippet

After every successful /figure new and /figure render, print exactly:

✓ Files: <abs-path>.svg / .pdf / .png
✓ Note:  <abs-path>.note.md
✓ LaTeX include (copy into your .tex):

    \begin{figure}[t]
      \centering
      \includegraphics[width={WIDTH}]{figures/<slug>.pdf}
      \caption{<intent>}
      \label{fig:<slug>}
    \end{figure}

{WIDTH} from size.preset:

• single-column → \columnwidth
• double-column-half → 0.48\textwidth
• double-column-full → \textwidth
• custom → \columnwidth (let user adjust)

For experiment scope, the include path is repo/figures/<vN.M>/<slug>.pdf instead.

Caption rules (apply when the user pastes the snippet into their .tex, and again when /paper write drafts the surrounding section):

• The caption answers what is this. Let the body text answer what does it mean.
• Hard rules: no ;, no --- or --. Restructure with commas or split into two sentences.
• Quantify any claim that the figure shows a good or better result. State the metric and the magnitude.
• Define every acronym in the caption on first use within the caption.
• Format rules (from paper-architect/references/latex-conventions.md section tables-and-figures → "Caption format"): decide case by syntax — noun phrase = Title Case, no period; complete sentence = Sentence case, end with period. Don't open with The figure shows ... / This diagram illustrates .... Don't use showcase / depict. No Figure 1: prefix in the source.
• Chinese intent → English caption. Step 1 collects intent: as one Chinese sentence. When emitting the LaTeX snippet, polish the intent to an English caption per the rules above — do not paste the raw Chinese. Persist both: intent: in note.md stays Chinese (for future recall); the \caption{...} argument is the polished English.

Reference intake — /figure ref add

1. Resolve the input image: positional <file>, --url <u> (curl into inputs/figure-refs/staging/), or scan inputs/figure-refs/staging/.
2. Ask: slug (default = kebab of filename).
3. Ask: source citation.
4. Ask: kind (structural / data / mixed).
5. Auto-suggest tags + why_i_like_it: read the image multimodally, propose 1 paragraph for why_i_like_it and a short list of tags. Show the suggestion and ask the user to confirm / edit / replace.
6. Auto-extract palette: palette.extract_palette_from_image(image_path, count=6). Show; user may override.
7. Build a FigureRef, call refs.add_ref(image_path, ref).
8. Call mcp__claude-flow__memory_store with refs.to_agentdb_payload(ref).

Error policy

When NOT to use this skill

• Wrappers / packaging logic for camera-ready submissions — that belongs to paper-architect / ref-manager.
• Editing already-existing SVGs by hand — print the path with /figure edit; the user opens in Inkscape themselves.