figure-candidate-gallery

name

figure-candidate-gallery

description

Generate a batch of diverse, polished, on-palette candidate small figures for a near-final document, render them all to one contact sheet, and present an honest per-figure assessment so the author can cherry-pick the strongest few. Use with /workflows enabled when the prose is mostly frozen and a section would read better with small, meaningful illustrations or experiment plots (not the hero or overview figure). Fans out one render agent per candidate (matplotlib for data plots, skia-canvas for modern schematics), enforces one shared visual system, and forbids fabricated data. For outside-model prompt packs use figure-prompt-builder, which this pack ships; for HTML system mockups use ci-mockup-figure where it is installed.

Figure Candidate Gallery

Overview

Starting from a near-final document, this skill generates a deliberately broad batch of diverse, on-palette candidate small figures, renders them to one contact sheet, and presents an honest assessment so the author can select a few and discard the rest. Each candidate is a self-contained render script, a vector PDF, and a PNG preview; the contact-sheet helper adapts its grid to the batch size. The quality floor is the shipped composition contracts in references/candidate-archetypes.md and the legibility and encoding gates in references/visual-system.md, both of which a reviewer can check against a rendered PNG.

The engine is /workflows. The skill fans out one render agent per candidate so a dozen figures render in parallel, then collects their metadata and builds the contact sheet. Running the candidates serially by hand is slow and loses the breadth that makes cherry-picking work; the workflow fan-out is the point.

This skill is for small figures that raise readability and visual quality: a decision ladder, a martingale alarm crossing a threshold, a paired distribution test, a posterior bar chart, a small interaction graph, a feasibility dumbbell built from real preliminary numbers. It is not for the hero or overview figure. Those carry the project's whole logic and should go to a human designer (for example, a student working in Photoshop), or to ci-mockup-figure (if installed) for an HTML system mockup, or to figure-prompt-builder for an outside-model prompt pack.

Invoke it late, when the prose is mostly frozen. The skill reads the fixed text and mines it for concrete, citable hooks: the exact quantity a sentence names, the decision rule a sub-aim defines, the preliminary result already cited. Because the text is settled, candidates can be specific to what each section actually claims rather than to a guess about what it might say.

When To Use

• The document draft is near-final and a few sections read as dense prose that a small figure would lighten.
• The author wants to see many options at once and choose, rather than commit to one figure up front.
• The figures are column-width illustrations or small experiment plots, not the main architecture diagram.
• An existing table reads as a wall of cells, and a graphic would carry the same content in less space with fewer counting steps for the reader.
• A shared color and type system already exists (or can be defined once) so the batch looks like one family.

When Not To Use

• The hero, overview, or full-system figure is the target. Route to a human designer, ci-mockup-figure if installed, or figure-prompt-builder.
• The prose is still moving. Wait until the text is settled, or the candidates will illustrate claims that later change.
• A single, already-specified figure needs careful hand-construction. This skill optimizes for breadth and selection, not for one polished final asset.
• /workflows is unavailable. The method still works run by hand, but it loses the parallel breadth it is built around; say so and proceed only if the user accepts the slower path.

The Hard Rules

1. Never Fabricate Data

This is the non-negotiable rule. A fabricated number in a federal proposal is a fatal error, not a style slip. Every candidate carries an explicit evidence class, and the render agent must declare it:

• conceptual: a schematic or diagram. Boxes, arrows, exemplar shapes, axis labels, and named stages are fine. No numeric value may be presented as a measured result.
• illustrative: shows a plausible shape (for example, a test statistic crossing an alarm line) with values that are clearly exemplar. The caption must say "illustrative" or "schematic," and the figure must never be read as a real result. Use round or obviously synthetic numbers, never precise decimals that look measured.
• real-data: every plotted number is copied verbatim from a source the author owns, with the source file path and the BibTeX cite key recorded. Never recompute, never round silently, never invent. A shipped exemplar such as feasibility-dumbbell supplies the visual shape only. Every value in a real-data figure comes from the brief, which must name the source file and the cite key, and each plotted number must match that source character for character.
• transcribed: every value is copied verbatim from the document this figure illustrates, which means the source is the document's own prose or a table the figure replaces. No external cite key exists here, and inventing one is a sourcing error. Record the source .tex file and the exact sentence or row each value came from. This class fails later than the others: a text edit moves a threshold, the figure keeps the old number, and nothing catches the mismatch. Re-check every transcribed value against its source sentence after any edit to that section, and again before the document ships.

When the evidence class is unclear, downgrade to illustrative or drop the candidate. Rather omit a figure than invent its data. One case must never take that downgrade: a transcribed figure whose numbers are commitments the document makes, such as a stated threshold or a promised deliverable. Captioning those as illustrative would misdescribe what the document promises. Fix the transcription or drop the figure. Surface the evidence class for every candidate in the final assessment, so the author knows which figures still need a real source before they ship.

2. Small And Meaningful, Not Padding

Each figure must earn its space by compressing logic the prose states less clearly. Target column width (roughly 0.45 to 0.5\textwidth), legible down to about 7 pt. A figure that only restates an adjacent sentence is cut, not kept.

Design the candidate at its final insertion width so the PDF is placed at 1:1. A graphic drawn at 6.5 in and inserted at 3.38 in halves every font, which is how a design that passed its own 7 pt gate ships at 3.5 pt. Crop with pdfcrop --margins 0 when the design already carries its own padding, since added margin widens the file past the insertion width and reintroduces a downscale.

3. One Visual System

The whole batch shares a color-to-role map, one type family, and a small set of motifs (for example, a hazard treatment for failure or leakage). Consistency is what makes a dozen separate scripts look like one designed set. The helpers are scripts/figure_base.py and scripts/skia_base.mjs. Both are copied into the run directory before fan-out, and both read the run's palette and font from one palette.json written beside them, so a batch that mixes the two tools cannot go half off-palette. See references/visual-system.md.

4. Division Of Labor

Small figures render here. Core figures go to a human designer. State this split when handing back results, and never let an auto-rendered schematic stand in for the hero figure the proposal actually needs.

5. Internal Until Chosen

Candidates render into figure-src/candidates/<run-id>/, which is gitignored, so the batch stays out of version control. Only the figures the author selects are copied into figure/ and wired into the document.

Workflow

Phase 1: Read The Frozen Sections And List Opportunities

Read the near-final source for the target sections (for a proposal, the aim files and any overview). For each section, note the concrete, visualizable hooks: a decision rule, a defined statistic, a named threshold, a stated quantity, a cited preliminary result. Map each hook to the section it supports. Do not read the whole repository; read the sections the figures will serve.

When the target is an artifact the document already carries, measure its defect before designing the replacement. Count words per column or per region, count repeated tokens per region, and compare each count against the width that region receives. Report both metrics: word share finds the starved region, and repeated-token share finds the region that has no business being one. Exact counts shift with tokenization, so name the rule you used and keep the script. Record the counts in the brief so the render agent designs against a measured imbalance rather than a general impression.

A rebuild of one existing slot runs a smaller batch, with a floor of two candidates rather than the dozen Phase 2 calls for. Give each candidate a named strategy rather than a free hand, or they converge on one shape. Phase 5 already requires a one-line reason per candidate, and with only two candidates that reason carries the whole recommendation.

Include one candidate that keeps the current size regime and only compresses it, so the narrow variants are compared against a measured current-size endpoint. Compare candidates using measurements taken from the current document, and retain each endpoint's source PDF or build log if the numbers are to be reused as evidence. Do not carry endpoints over from another document.

Phase 2: Write The Candidate Brief List

Turn the hooks into a brief list. Aim for breadth: cover each section, and vary the figure archetype so the batch is diverse rather than six versions of one shape. Make each brief specific enough that a render agent cannot collapse it to a generic shape. For each candidate record:

• id in the form cand-NN, a bare descriptive slug such as aim2-paired-ks, and an outputStem equal to <id>-<slug>. Use that exact stem for the script, the PDF, the PNG, and the returned metadata. The contact sheet collects cand-*.png, so a bare slug renders a file it will not find.
• target section
• archetype, plus the named reference exemplar when one exists (see references/candidate-archetypes.md)
• compositionSpec: the required panels or regions, the flow direction, the required formulas and thresholds, and any leader lines or callouts. Copy the archetype's composition contract; do not pass the shape name alone.
• densityTarget: sparse, balanced, or dense, with a text budget such as "one formula plus one short note per pillar"
• mustInclude: the visual elements that are not optional
• avoid: the named anti-patterns to reject (for example chip-only-pillars)
• the one-sentence message the figure must land
• render tool: matplotlib for data plots, skia-canvas for modern schematics
• evidence class; for real-data the source path and cite key; for transcribed the source .tex file plus the sentence or row each value came from

Over-generate on purpose. The author expects to keep some and drop others, so a dozen candidates with a few weak ones is the right shape, not a tight set of three.

Phase 3: Prepare The Work Tree, Then Fan Out With /workflows

Do both of these before launching any agent. A dozen agents that fail on a missing directory or a missing package cost a fan-out to discover what one check finds in a second.

Prepare the tree. Resolve the repository root and <skill-dir>, the directory holding this SKILL.md. For every invocation choose a new run identifier and create a fresh <candDir> at figure-src/candidates/<run-id>/. Never reuse a nonempty run directory. The contact sheet collects every cand-*.png it finds, so a later two-candidate rebuild in a shared directory silently presents ten stale figures beside the two new ones, and a stale transcribed figure may no longer match the sentence it came from. In a git repository, ensure the root .gitignore carries /figure-src/candidates/ and confirm with git check-ignore rather than assuming it. If any brief renders with skia, also copy <skill-dir>/scripts/skia_base.mjs into <candDir>; the helper's bare skia-canvas import still resolves upward to figure-src/node_modules. Pass this exact <candDir> to every render agent and to the contact-sheet command. Create figure/ only when the author promotes a candidate.

Set the palette once, in palette.json. Copy <skill-dir>/scripts/figure_base.py into <candDir> as well, then write <candDir>/palette.json beside the two helpers:

{
  "font": "serif",
  "palette": {
    "data": {"accent": "#B26B00", "fill": "#FBEFD8"},
    "bad":  {"accent": "#B23A48", "fill": "#F9E0E4"}
  }
}

Both helpers read that file at import and merge it over their defaults, so one edit covers a batch that mixes matplotlib and skia. Put the document's own colors in palette, add a role whose name matches the document's vocabulary, and set font to serif for a mathpazo body or sans for crisp small plots. The role map in references/visual-system.md is a starting point rather than any particular document's palette. Writing the run file is what keeps the batch on-palette without touching the installed skill, which a pack owns and which every later run would inherit.

Both helpers now sit beside the candidates, so a matplotlib candidate imports figure_base from <candDir> and a skia candidate imports ./skia_base.mjs.

Preflight the toolchain. Verify <python> -c "import matplotlib", which is required even for an all-skia batch because the contact sheet imports it. If any brief uses skia, verify <node> <candDir>/skia_base.mjs, which also proves figure-src/node_modules/skia-canvas resolves. When an import fails, stop before fan-out, name the missing package and the interpreter you used, and ask before installing anything. If only skia is missing, offer a matplotlib-only batch and say plainly that the schematic candidates are lost.

Confirm both helpers resolve the same visual system before fan-out, since a mixed batch goes off-palette silently otherwise:

<python> <skill-dir>/scripts/check_run_config.py --dir <candDir>

It compares the complete palette and the font, so a drift in one role is caught as well as a wholesale mismatch, and it exits nonzero with what differs. A malformed palette.json makes both helpers raise rather than fall back to the shipped colors, so this check fails there too instead of agreeing on defaults.

Run one render agent per candidate. Each agent writes a self-contained script into <candDir>, runs it with the configured interpreter, and returns structured metadata. A compact script:

export const meta = {
  name: 'figure-candidate-gallery',
  description: 'Render a batch of small proposal figure candidates in parallel',
  phases: [{ title: 'Render' }],
}

// BRIEFS is the Phase 2 list, passed in whole: id, slug, outputStem, section,
// archetype, exemplar, message, compositionSpec, densityTarget, mustInclude,
// avoid, tool, evidenceClass, dataSource. The prompt below reads every one of
// them, and compositionSpec, densityTarget, mustInclude, and avoid are the
// fields that keep a render agent from collapsing a brief to a generic shape.
// Every field Phase 2 marks as required must be present before fan-out.
const BRIEFS = args.briefs
const CAND_DIR = args.candDir     // the run directory prepared above
const SKILL_DIR = args.skillDir   // the resolved absolute <skill-dir>

const SCHEMA = {
  type: 'object',
  // dataSource is unconditionally required: without it the later recheck has no
  // source to check against, and the result still validates. Do NOT express this
  // as a conditional. A top-level oneOf/allOf/anyOf is not portable across tool
  // APIs, and where it is rejected the whole fan-out fails before any agent
  // starts work. Require the field and let 'conceptual' and
  // 'illustrative' candidates carry the literal string "n/a". selfAssessment is
  // required for the same reason: Phase 5 presents an honest call per candidate,
  
  : [, , , , , , , , ],
  : {
    : { :  },
    : { :  },
    : { :  },                 
    : { : , : [, ] },
    : { : , : [, , , ] },
    : { :  },           
    : { : , : [, ] },
    : { :  },
    : { : , : [, , ] },
  },
}

 results =  (.(  (
  [
    ,
    ,
    ,
    ,
    ,
    ,
    b. === 
      ? 
      : b. === 
      ? 
      : ,
    ,
    ,
    ,
    ,
    ,
    ,
  ].(),
  { : b., : , :  }
)))

 results.()

Pass skillDir the resolved absolute <skill-dir>, never the literal placeholder. A child agent reads only its own prompt, so a path this workflow does not interpolate is a path the child cannot find.

Each render agent uses the shared toolkit so the batch stays consistent. The real-data branch of the prompt restates the no-fabrication rule directly to the agent, because that is the rule most likely to be violated under generation.

Before building the contact sheet, reconcile the returned results against every Phase 2 brief, matching on id. Each brief needs a result with status: rendered, and both <candDir>/<outputStem>.pdf and <candDir>/<outputStem>.png must exist on disk. Retry the failures, or present the exact missing and failed list as a partial run. Never describe a partial run as a completed batch.

Phase 4: Build The Contact Sheet

After the workflow returns, run the contact-sheet script over the candidates directory. Resolve <skill-dir> as the directory holding the SKILL.md you are reading. A consumer receives this skill at .claude/skills/, so a top-level skills/ checkout is not there to be found:

<python> <skill-dir>/scripts/make_contact_sheet.py --dir <candDir>

It tiles every cand-*.png into one labeled grid and writes _contact-sheet.png and _contact-sheet.pdf. Read the contact sheet to verify the batch rendered on-palette and nothing broke.

Phase 5: Present For Cherry-Picking

Give the author the contact-sheet path and a per-candidate table. For each candidate show the title, the message, the evidence class, and an honest call: strong, ok, or weak, with a one-line reason. Flag any real-data or transcribed candidate whose source still needs verification, and flag any candidate that overlaps the hero figure (a human designer owns that). The author selects; do not wire anything in or commit until they choose.

A workable table shape:

#	Figure (section)	Evidence	Call
05	Paired equivalence test (Aim 2)	illustrative	strong: the decisive test as two distributions
11	Graph predicts failure (Aim 3)	real-data	strong: author's verified numbers
02	Detector triad (Aim 1)	conceptual	weak: busy, overlaps the pipeline figure

Phase 6: Promote The Chosen Figures

Once the author names the keepers, copy each chosen .pdf from <candDir> into figure/, add a wrapfigure or small figure with a caption and a \ref callout in the prose, and recompile. For any real-data figure, confirm the cite key resolves and the source is current before it ships. For any transcribed figure, re-read every value against the sentence or row recorded in the brief. Leave the rest in the gitignored candidates directory.

Toolchain

• matplotlib for data plots and any figure with axes. Copy <skill-dir>/scripts/figure_base.py into the run directory, set this run's palette and font in <candDir>/palette.json beside it, and have every matplotlib candidate import that copy for the shared rcParams, palette, and helpers. Export PDF (with pdf.fonttype = 42) and PNG.
• skia-canvas (Node) for modern schematics: rounded cards, chips, arrows, node-link graphs, loops. This is the path that gives the clean, current look, in place of dated TikZ box farms. Copy <skill-dir>/scripts/skia_base.mjs into the run directory and have each skia candidate import it as ./skia_base.mjs. Node's ESM loader resolves skia-canvas by walking up from the file, so the run directory must sit under figure-src/, where skia-canvas is installed at figure-src/node_modules (npm i skia-canvas, run once under figure-src/). NODE_PATH does not work for ESM bare imports.
• run config check via <skill-dir>/scripts/check_run_config.py, which compares the palette and font both run-local helpers resolve.
• contact sheet via <skill-dir>/scripts/make_contact_sheet.py. It imports matplotlib, so matplotlib is required even for a batch rendered entirely with skia.

Interpreter and tool paths are machine-local. Use the workspace's configured Python and Node rather than hardcoding one machine's paths; if the project records an interpreter (for example in AGENTS.local.md), use that.

Output Layout

<work-dir>/figure-src/node_modules/       # skia-canvas, installed once
<work-dir>/figure-src/candidates/         # gitignored, internal
  <run-id>/                               # one directory per invocation
    palette.json                          # this run's palette and font; both helpers read it
    figure_base.py                        # copied here, reads palette.json
    skia_base.mjs                         # copied here when a brief uses skia, reads palette.json
    cand-01-<slug>.py | .mjs              # one render script per candidate
    cand-01-<slug>.pdf                    # vector output
    cand-01-<slug>.png                    # raster preview
    ...
    _contact-sheet.png | .pdf             # the grid for review
<work-dir>/figure/                        # only the chosen figures land here

References

• references/candidate-archetypes.md: the catalog of small-figure shapes that work, grouped by what each one shows, with the evidence class each tends to carry.
• references/visual-system.md: the palette-to-role map, type, motifs, sizing, and the shared toolkit modules.