ablation-planner
Use when main results pass result-to-claim (claim_supported=yes or partial) and ablation studies are needed for paper submission.
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
Claude Code
$ npx -y skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill paper-illustration-image2 --agent claude-code
Codex
$ npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill paper-illustration-image2 -a codex -y
Cursor
$ npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill paper-illustration-image2 -a cursor -y
Gemini CLI
$ npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill paper-illustration-image2 -a gemini-cli -y
GitHub Copilot
$ npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill paper-illustration-image2 -a github-copilot -y
OpenCode
$ npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill paper-illustration-image2 -a opencode -y
Windsurf
$ npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill paper-illustration-image2 -a windsurf -y
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, mcp__antigravity__antigravity, mcp__antigravity__antigravity-reply, mcp__grok__grok, mcp__grok__grok-reply, mcp__claude-review__review, mcp__claude-review__review_reply, mcp__claude-review__review_start, mcp__claude-review__review_reply_start, mcp__claude-review__review_status
> **Before each reviewer slot**, follow [Selectable Reviewers](../shared-references/reviewer-backends.md): > resolve invocation → `.aris/reviewer.json` → Claude Code default (Codex MCP) > with `reviewer_route.py` through the canonical helper resolver, preserving this > slot's tier, fresh contexts, source/image inputs and deterministic gates. > Codex examples/pins below apply only to `backend=codex`; other routes use their > own first-call/reply contract. Save the route and actual returned identity; > unavailable selections are `REVIEW_UNAVAILABLE`. Classify acceptance from actual > families/completion/scope. Forward invocation reviewer/model/effort overrides > to reviewer-bearing children. Generation workers are unchanged.
Generate publication-quality paper figures using **Claude as the planner/reviewer** and a **local Codex app-server MCP bridge** as the raster renderer.
┌──────────────────────────────────────────────────────────────────────────┐ │ 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 │ │ │ └──────────────────────────────────────────────────────────────────────────┘
· · · · · · · · 💬 Join Community · 🌱 ARIS is a methodology, not a platform. What matters is the research workflow — take it wherever you go.
Use when main results pass result-to-claim (claim_supported=yes or partial) and ablation studies are needed for paper submission.
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