paper-illustration-image2

name	paper-illustration-image2
description	Generate publication-quality academic illustrations through a local Codex app-server bridge that uses Codex native image generation. This is a separate experimental alternative to `paper-illustration`, intended for Claude Code users who want a GPT-image-style renderer without modifying the original skill.
argument-hint	[description-or-method-file]
allowed-tools	Bash(*), Read, Write, Edit, Grep, Glob, WebSearch, mcp__codex-image2__generate, mcp__codex-image2__generate_start, mcp__codex-image2__generate_status, mcp__codex__codex, mcp__codex__codex-reply

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.

Core Design Philosophy

┌──────────────────────────────────────────────────────────────────────────┐
│                    MULTI-STAGE ITERATIVE WORKFLOW                        │
├──────────────────────────────────────────────────────────────────────────┤
│                                                                          │
│   User Request                                                           │
│       │                                                                  │
│       ▼                                                                  │
│   ┌─────────────┐                                                        │
│   │   Claude    │ ◄─── Step 1: Parse request, create initial prompt     │
│   │  (Planner)  │      - Extract components, labels, and data flow       │
│   │             │      - Write a paper-ready figure brief                │
│   └──────┬──────┘                                                        │
│          │                                                               │
│          ▼                                                               │
│   ┌─────────────┐                                                        │
│   │Claude/Codex │ ◄─── Step 2: Optimize layout description               │
│   │   Layout    │      - Refine component positioning                    │
│   │   Review    │      - Optimize spacing and grouping                   │
│   └──────┬──────┘                                                        │
│          │                                                               │
│          ▼                                                               │
│   ┌─────────────┐                                                        │
│   │Claude/Codex │ ◄─── Step 3: CVPR/NeurIPS style verification           │
│   │   Style     │      - Check palette, arrows, and label standards      │
│   │   Check     │      - Tighten the prompt before rendering             │
│   └──────┬──────┘                                                        │
│          │                                                               │
│          ▼                                                               │
│   ┌─────────────┐                                                        │
│   │ codex-image2│ ◄─── Step 4: Native image generation via bridge        │
│   │ MCP bridge  │      - Call generate_start / generate_status           │
│   │ + app-server│      - Accept only native imageGeneration output       │
│   └──────┬──────┘                                                        │
│          │                                                               │
│          ▼                                                               │
│   ┌─────────────┐                                                        │
│   │   Claude    │ ◄─── Step 5: STRICT visual review + SCORE (1-10)      │
│   │  (Reviewer) │      - Verify logic, labels, arrows, and aesthetics    │
│   │   STRICT!   │      - Reject unclear or non-paper-ready figures       │
│   └──────┬──────┘                                                        │
│          │                                                               │
│          ▼                                                               │
│   Score ≥ 9? ──YES──► Accept & Output                                    │
│          │                                                               │
│          NO                                                              │
│          │                                                               │
│          ▼                                                               │
│   Generate SPECIFIC improvement feedback ──► Loop back to Step 2        │
│                                                                          │
└──────────────────────────────────────────────────────────────────────────┘

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

• MAX_ITERATIONS = 5 — Maximum refinement rounds

• TARGET_SCORE = 9 — Minimum acceptable score (1-10)

• OUTPUT_DIR = figures/ai_generated/ — Output directory

• TEXT_LANGUAGE = English — Default figure text language unless the user requests otherwise

• NATIVE_IMAGE_REQUIREMENT = strict — Accept only native imageGeneration output; reject shell/Python fallbacks

• IMAGE2_HELPER — canonical name paper_illustration_image2.py, resolved per shared-references/integration-contract.md §2 (Policy A — skill-local gate). Phase 3.2 (Arch C) moved the canonical implementation into skills/paper-illustration-image2/scripts/; tools/paper_illustration_image2.py remains as an os.execv shim so legacy resolver layers keep working without a re-install. Resolve via:

  # Layer 0: self-contained (CC 1.0+ exposes $CLAUDE_SKILL_DIR).
  IMAGE2_HELPER=""
  if [ -n "${CLAUDE_SKILL_DIR:-}" ] && [ -f "$CLAUDE_SKILL_DIR/scripts/paper_illustration_image2.py" ]; then
    IMAGE2_HELPER="$CLAUDE_SKILL_DIR/scripts/paper_illustration_image2.py"
  fi
  # Layers 1-4: shared-runtime chain via shim at tools/paper_illustration_image2.py.
  if [ -z "$IMAGE2_HELPER" ]; then
    cd "" ||  1
     [ -z  ] && [ -f .aris/installed-skills.txt ]; 
        ARIS_REPO=$(awk -F  .aris/installed-skills.txt 2>/dev/null) || 
    
     [ -z  ] && [ -f  ]; 
        ARIS_REPO=$(  2>/dev/null) || 
    
    IMAGE2_HELPER=
    [ -f  ] || IMAGE2_HELPER=
    [ -f  ] || { [ -n  ] && IMAGE2_HELPER=; }
    [ -f  ] || IMAGE2_HELPER=
  
  [ -z  ] && {
      >&2
      >&2
     1
  }
  

CVPR/ICLR/NeurIPS Top-Tier Conference Style Guide

What "CVPR Style" Actually Means:

Visual Standards

• 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)

目标：既不保守也不花哨，找到平衡点

✅ 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: Statistical plots (use /paper-figure), deterministic vector topology figures (prefer /figure-spec), photo-realistic scenes

Workflow: MUST EXECUTE ALL STEPS

Step 0: Pre-flight Check

Render this checklist explicitly before starting:

📋 paper-illustration-image2 integration checklist:
   [ ] 1. python3 "$IMAGE2_HELPER" preflight --workspace <cwd> --json-out figures/ai_generated/preflight.json
   [ ] 2. Confirm preflight JSON says ok=true before rendering
   [ ] 3. Render via mcp__codex-image2__generate_start + generate_status
   [ ] 4. Finalize via python3 "$IMAGE2_HELPER" finalize --workspace <cwd> --best-image <best_png>
   [ ] 5. Verify artifacts via python3 "$IMAGE2_HELPER" verify --workspace <cwd> --json-out figures/ai_generated/verify.json

1. Create figures/ai_generated/ if it does not exist.
2. Confirm the request is suitable for a raster illustration:
   • architecture diagram
   • conceptual method figure
   • workflow illustration
3. Prefer English figure text unless the user asked otherwise.
4. Run:

python3 "$IMAGE2_HELPER" preflight \
  --workspace <cwd> \
  --json-out figures/ai_generated/preflight.json

5. 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: current project root or paper workspace
• outputPath: figures/ai_generated/figure_v1.png
• 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:

• run the canonical helper to promote the best image to figure_final.png
• let the helper write latex_include.tex
• let the helper write review_log.json
• run helper verification before claiming success

python3 "$IMAGE2_HELPER" finalize \
  --workspace <cwd> \
  --best-image figures/ai_generated/figure_vN.png \
  --score 9 \
  --review-summary "Accepted after strict review; labels and arrows are paper-ready."

python3 "$IMAGE2_HELPER" verify \
  --workspace <cwd> \
  --json-out figures/ai_generated/verify.json

Suggested LaTeX:

\begin{figure*}[t]
    \centering
    \includegraphics[width=0.95\textwidth]{figures/ai_generated/figure_final.png}
    \caption{[Replace with a paper-ready caption].}
    \label{fig:[replace-me]}
\end{figure*}

Key Rules

1. Never skip Step 2 or Step 3; layout and style checks are required.
2. Never skip the final visual review.
3. Never accept a figure that is logically wrong just because it looks attractive.
4. Use the codex-image2 bridge only for native image generation.
5. If the bridge says native image generation is unavailable, surface that honestly.
6. Reject any shell/Python/manual bitmap fallback masquerading as image generation.
7. Keep figure text in English unless the user requested another language.
8. Prefer 1-3 strong refinement rounds over many shallow ones.
9. Use specific, actionable refinement feedback instead of vague comments.
10. Review arrow direction, label clarity, and visual hierarchy every round.
11. Accept only figures that look paper-ready, not slide-ready.
12. Always use tools/paper_illustration_image2.py finalize to emit the final artifacts.
13. Always use tools/paper_illustration_image2.py verify before claiming success.

Repair Path

If rendering succeeded but final artifacts were skipped, repair the integration explicitly:

python3 "$IMAGE2_HELPER" finalize \
  --workspace <cwd> \
  --best-image figures/ai_generated/figure_vN.png

python3 "$IMAGE2_HELPER" verify \
  --workspace <cwd> \
  --json-out figures/ai_generated/verify.json

Output Structure

figures/ai_generated/
├── preflight.json         # Helper preflight receipt
├── figure_v1.png          # Iteration 1
├── figure_v2.png          # Iteration 2
├── figure_v3.png          # Iteration 3
├── figure_final.png       # Accepted version (copy of best, score ≥ 9)
├── latex_include.tex      # LaTeX snippet
├── review_log.json        # Review notes and refinement history
└── verify.json            # Helper verification diagnostic

Model Summary

Stage	Agent / Tool	Purpose
Step 0	python3 "$IMAGE2_HELPER" preflight	Observable activation predicate and preflight receipt
Step 1	Claude	Parse request and create the initial figure prompt
Step 2	Claude (+ optional Codex critique)	Refine layout, grouping, spacing, and arrow routing
Step 3	Claude (+ optional Codex critique)	Verify academic visual style before rendering
Step 4	mcp__codex-image2__generate_start + generate_status	Native raster image generation through Codex app-server
Step 5	Claude	Strict visual review and scoring
Step 7	python3 "$IMAGE2_HELPER" finalize + verify	Emit canonical artifacts and external verification receipt