AI-illustration renderer of the display family: generate publication-quality academic concept figures (architecture/method/pipeline/taxonomy) through a local Codex app-server bridge that uses Codex native image generation. Use when user says "画架构图", "method illustration", "concept figure", "AI 配图", or needs an AI-rendered concept figure.
Standardmäßig ist der Prompt ausgewählt, der zuerst die Quelle prüft. Sie können zu einem direkten Befehl wechseln oder eine lokale Kopie herunterladen.
Quelldateien prüfen
Lesen Sie SKILL.md und alle von SkillsMP angezeigten Begleitdateien, bevor Sie sich für eine Installation entscheiden.
Mit Codex oder Claude installieren Kopieren Sie diesen Prompt, fügen Sie ihn in Codex, Claude oder einen anderen Assistant ein und lassen Sie die Skill-Seite prüfen und installieren.
Ein direkter Befehl überspringt den Prüf-Prompt. Prüfen Sie die Quelle, bevor Sie ihn ausführen.
AI-illustration renderer of the display family: generate publication-quality academic concept figures (architecture/method/pipeline/taxonomy) through a local Codex app-server bridge that uses Codex native image generation. Use when user says "画架构图", "method illustration", "concept figure", "AI 配图", or needs an AI-rendered concept figure.
{"version":"0.2.2","last_updated":"2026-08-05","summary":"AI-illustration renderer that uses a Display Intake for approved narrative context and facts."}
Paper Illustration Image2
Generate publication-quality paper figures using Claude as the planner/reviewer
and a local Codex app-server MCP bridge as the raster renderer.
Output: write into a display unit
Output goes into a displays/displayNN-<slug>/ unit per the shared contract:
../../ref/display-unit-output-contract.md.
THIS renderer's row: asset -> assets/figure.png; rebuild spec ->
(final prompt + bridge job + score) + ; finalize with
(Step 7).
recipe/prompt.md
recipe/review_log.json
--display-unit <unit-dir>
For a new unit, read intake/manifest.yaml before planning the prompt.
The manifest supplies approved narrative context and any facts the illustration may state.
This renderer takes no values source for a purely conceptual image.
If the image includes a real N, percentage, coefficient, or other estimate, that fact MUST be a
declared role: values intake source; never let image generation invent it.
Legacy source/ units remain valid only through the compatibility path in the shared contract.
Fit & Readiness (haipipe)
Use this for conceptual figures only — architecture diagrams, method/pipeline
schematics, taxonomy trees.
It produces an AI raster image.
Do NOT use it for data displays. Tables and result figures (descriptives,
dose-response, subgroup, etc.) must be rendered from real data by a task
(the Z01-style parse-then-render path) so they are reproducible and exact.
An
AI raster of a data figure is unverifiable and unfit for a data-driven venue.
For deterministic vector schematics (e.g. a study-flow / CONSORT diagram), prefer
haipipe-display-diagram (JSON -> SVG, no external service) or a
task-rendered matplotlib schematic; reach for image2 only when you want a richer
conceptual illustration than a vector spec can express, typically for a
conference/ML venue.
Runtime dependency: needs the codex-image2 MCP bridge (toolkit
mcp-servers/codex-image2/, install per its README) plus the Codex desktop app
signed in and the codex CLI on PATH.
If mcp__codex-image2__* tools are not
present, the bridge is not registered in this session — report that honestly
rather than falling back to a shell/Python bitmap.
Constants
RENDERER = codex-image2 — Native image generation bridge exposed through local Codex app-server
OPTIONAL_TEXT_CRITIC = mcp__codex__codex — Optional text-only second opinion for layout/style checks
OUTPUT_DIR — for a paper: the display unit displays/displayNN-slug/ (asset -> assets/figure.png, iterations + receipts -> recipe/).
Only with no paper: the flat fallback figures/ai_generated/.
TEXT_LANGUAGE = English — Default figure text language unless the user requests otherwise
Clean white background — No decorative patterns or gradients unless extremely subtle
Sans-serif fonts — Arial, Helvetica, or similarly clean paper-friendly typography
Subtle color palette — Use 3-5 coordinated colors, not rainbow colors
Print-friendly — Must remain understandable in grayscale
Professional borders — Thin to medium, clean, and consistent
Layout Standards
Horizontal flow — Left-to-right is the default for pipelines
Clear grouping — Use spacing or subtle grouping boxes for related modules
Consistent sizing — Similar components should have similar sizes
Balanced whitespace — Avoid both cramped and overly sparse layouts
Arrow Standards (MOST CRITICAL)
Thick strokes — Arrows must remain visible after paper scaling
Clear arrowheads — Large, unmistakable arrowheads
Dark colors — Prefer black or dark gray arrows
Labeled — Important arrows should show what flows through them
No crossings — Reorganize the figure to avoid crossings where possible
CORRECT DIRECTION — Arrows must point to the right target
Visual Appeal (Academic Professional Style)
Aim for the balance point: neither overly conservative nor flashy.
✅ Should have
Subtle gradients — Gentle same-family gradients are acceptable
Rounded corners — Modern but restrained rounded blocks
Clear hierarchy — Main modules larger, secondary modules smaller
Consistent color coding — Stable mapping between module types and colors
Professional typography — Clean labels with readable size hierarchy
❌ Avoid
❌ Rainbow gradients
❌ Heavy drop shadows
❌ 3D perspective effects
❌ Glowing effects
❌ Decorative clip-art icons
❌ Slide-deck styling that feels flashy rather than paper-ready
✓ Ideal effect
Looks intentional, professional, and immediately readable
Has moderate visual appeal without becoming decorative
Feels appropriate for a top-tier conference paper figure
Survives PDF scaling and grayscale printing
What to AVOID (CRITICAL)
❌ Thin, hairline arrows
❌ Unlabeled or ambiguous connections
❌ Tiny unreadable text
❌ Flat, boring box soup with no hierarchy
❌ Over-decorated figures with shadows/glows/icons
❌ Wrong arrow directions
Scope
Figure Type
Quality
Examples
Architecture diagrams
Excellent
Model architecture, pipeline, encoder-decoder
Method illustrations
Excellent
Conceptual diagrams, algorithm flowcharts
Conceptual figures
Good
Comparison diagrams, taxonomy trees
Not for: photo-realistic scenes, or any display better served by a sibling renderer — see the sibling-routing table in ../../ref/display-unit-output-contract.md.
Workflow: MUST EXECUTE ALL STEPS
Step 0: Pre-flight Check
Render this checklist explicitly before starting:
📋 paper-illustration-image2 integration checklist:
[ ] 0. Resolve/scaffold the display unit (see the contract): displays/displayNN-slug/
[ ] 1. Read intake/manifest.yaml and confirm all facts in the prompt are declared there
[ ] 2. preflight --workspace <paper-root> --json-out displays/displayNN-slug/recipe/preflight.json
[ ] 3. Confirm preflight JSON says ok=true before rendering
[ ] 4. Render via mcp__codex-image2__generate_start + generate_status
[ ] 5. Finalize into the unit: finalize --workspace <paper-root> --display-unit displays/displayNN-slug --best-image <best_png> (Step 7)
[ ] 6. Verify: verify --workspace <paper-root> --display-unit displays/displayNN-slug
Resolve the target display unit (displays/displayNN-slug/); scaffold it via
Skill("haipipe-paper", "display scaffold ...") if it does not exist.
Only when
there is no paper, fall back to creating figures/ai_generated/.
Confirm the request is suitable for a raster illustration:
architecture diagram
conceptual method figure
workflow illustration
Prefer English figure text unless the user asked otherwise.
Confirm the Intake context is complete, then run preflight (receipt into the unit's recipe/):
If preflight is not ok=true, stop and say so clearly.
Step 1: Claude Plans the Figure
Turn the user request into a fully specified image prompt.
Include:
figure type
exact modules / stages
flow direction
labels to show
data-flow arrows
style constraints
what to avoid
When the input is a method note or a paper section, summarize it first into a
clean figure brief before writing the final image prompt.
Step 2: Layout Optimization
This step is required.
Before rendering, refine the prompt into a concrete
layout plan:
exact module order
spacing and grouping
relative module prominence
arrow routing and likely collision points
If mcp__codex__codex is available, you may ask it for a short second-opinion
layout critique here, but Claude should still complete this step even without
Codex.
Use Codex layout critique for:
missing components
confusing layout
weak flow hierarchy
likely arrow-direction ambiguity or clutter
Step 3: Style Verification
This step is also required.
Check the prompt against the intended paper style
before rendering:
palette is restrained and academic
arrows are thick, dark, and readable
labels are concise and in English unless requested otherwise
the figure will read clearly in grayscale / print
no glow, rainbow gradient, or slide-deck decoration slips in
If mcp__codex__codex is available, you may ask it for a short text-only
style audit, but do not block on it.
Step 4: Generate Through the Bridge
Call mcp__codex-image2__generate_start with:
prompt: the final image prompt
cwd: the paper workspace (paper root)
outputPath: figures/ai_generated/figure_vN.png. NOTE: the bridge HARD-LOCKS output under figures/ai_generated/; it rejects any path outside it (so you cannot render straight into the unit).
Iterations render here as scratch; finalize --display-unit then promotes the accepted one to displays/displayNN-slug/assets/figure.png and writes review provenance to recipe/.
system: a short instruction like Academic paper figure. Prefer crisp English labels.
timeoutSeconds: a bounded render timeout such as 180
Then call mcp__codex-image2__generate_status with bounded waits until:
done=true and status=completed, or
done=true and status=failed
If generation fails, report the bridge error directly instead of hiding it.
Step 5: Review the Output
Review the generated image with a strict checklist:
are all major components present?
is the logical flow obvious?
are labels readable?
do arrows point the right way?
does the figure look paper-ready rather than like a slide?
Score it from 1-10.
Step 6: Refine if Needed
If score < 9, write a targeted refinement prompt:
say exactly what was wrong
say what to preserve
regenerate to figure_v2.png, figure_v3.png, etc.
Keep refinement feedback concrete:
Increase spacing between genome scan and scoring modules
Make the off-target branch thinner and secondary
Use cleaner English labels: "Candidate sgRNA library", not "sgRNA library 23 bp"
Step 7: Finalize And Verify
When accepted, finalize INTO THE DISPLAY UNIT (the contract path; see the "Output:
write into a display unit" section above and
../../ref/display-unit-output-contract.md). Pass
--display-unit <displays/displayNN-slug> so the helper writes
assets/figure.png + float.tex (only from the caller-approved caption + label + placement, never
invented or changed) + recipe/review_log.json,
then compile preview.pdf from the paper root.
# Paper target — write into the display unit (DEFAULT for a paper):
python3 "${CLAUDE_SKILL_DIR:-.}/scripts/paper_illustration_image2.py" finalize \
--workspace <paper-root> \
--display-unit <paper-root>/displays/displayNN-slug \
--best-image <paper-root>/figures/ai_generated/figure_vN.png \
--caption "Paper-ready caption." --label "fig:slug" --placement "t" \
--score 9 --review-summary "Accepted after strict review."# also drop the rebuild spec the helper does not author:# displays/displayNN-slug/recipe/prompt.md (final prompt + bridge job + score)# compile the unit preview from the paper ROOT so displays/ paths resolve:
pdflatex -interaction=nonstopmode -output-directory displays/displayNN-slug \
displays/displayNN-slug/preview.tex
python3 "${CLAUDE_SKILL_DIR:-.}/scripts/paper_illustration_image2.py" verify \
--workspace <paper-root> --display-unit <paper-root>/displays/displayNN-slug \
--json-out <paper-root>/displays/displayNN-slug/recipe/verify.json
Fallback (NO paper / scratch only): omit --display-unit; the helper writes the
flat figures/ai_generated/{figure_final.png,latex_include.tex,review_log.json}.
The unit's float.tex is \input by 0-lifecycle/4-display/4-display.tex, so a
correctly filed unit appears in the combined gallery automatically.
Repair Path
If rendering succeeded but final artifacts were skipped, repair the integration
explicitly.
For a paper, pass --display-unit so repair lands in the unit (an
existing hand-edited float.tex is preserved, not clobbered):
