/aris-paper-illustration
Generate publication-quality AI illustrations for academic papers using Gemini image generation. Creates architecture diagrams, method illustrations with Claude-supervised iterative refinement loop. Use when user says \"生成图表\", \"画架构图\", \"AI绘图\", \"paper illustration\",
$ npx -y skills add OpenLAIR/dr-claw --skill aris-paper-illustration --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
- You can call itInvoke it directly when you want it.
- Slash command
/aris-paper-illustration
Context preview
The summary Claude sees to decide when to auto-load this skill.
Generate publication-quality AI illustrations for academic papers using Gemini image generation. Creates architecture diagrams, method illustrations with Claude-supervised iterative refinement loop. Use when user says \"生成图表\", \"画架构图\", \"AI绘图\", \"paper illustration\",
SKILL.md
aris-paper-illustration.SKILL.mdname: aris-paper-illustration
description: "Generate publication-quality AI illustrations for academic papers using Gemini image generation. Creates architecture diagrams, method illustrations with Claude-supervised iterative refinement loop. Use when user says \"生成图表\", \"画架构图\", \"AI绘图\", \"paper illustration\", \"generate diagram\", or needs visual figures for papers."
argument-hint: "[description-or-method-file]"
allowed-tools: Bash(*), Read, Write, Edit, Grep, Glob, Agent, mcp__codex__codex, mcp__codex__codex-reply, WebSearch
license: MIT
metadata:
author: wanshuiyin/ARIS
version: "1.0.0"
Paper Illustration: Multi-Stage Claude-Supervised Figure Generation
Generate publication-quality illustrations using a **multi-stage workflow** with **Claude as the STRICT supervisor/reviewer**.
Core Design Philosophy
┌──────────────────────────────────────────────────────────────────────────┐
│ MULTI-STAGE ITERATIVE WORKFLOW │
├──────────────────────────────────────────────────────────────────────────┤
│ │
│ User Request │
│ │ │
│ ▼ │
│ ┌─────────────┐ │
│ │ Claude │ ◄─── Step 1: Parse request, create initial prompt │
│ │ (Planner) │ │
│ └──────┬──────┘ │
│ │ │
│ ▼ │
│ ┌─────────────┐ │
│ │ Gemini │ ◄─── Step 2: Optimize layout description │
│ │ (gemini-3-pro)│ - Refine component positioning │
│ │ Layout │ - Optimize spacing and grouping │
│ └──────┬──────┘ │
│ │ │
│ ▼ │
│ ┌─────────────┐ │
│ │ Gemini │ ◄─── Step 3: CVPR/NeurIPS style verification │
│ │ (gemini-3-pro)│ - Check color palette compliance │
│ │ Style │ - Verify arrow and font standards │
│ └──────┬──────┘ │
│ │ │
│ ▼ │
│ ┌─────────────┐ │
│ │ Paperbanana │ ◄─── Step 4: Render final image │
│ │ (gemini-3- │ - High-quality image generation │
│ │ pro-image) │ - Internal codename: Nano Banana Pro │
│ └──────┬──────┘ │
│ │ │
│ ▼ │
│ ┌─────────────┐ │
│ │ Claude │ ◄─── Step 5: STRICT visual review + SCORE (1-10) │
│ │ (Reviewer) │ - Verify EVERY arrow direction │
│ │ STRICT! │ - Verify EVERY block content │
│ └──────┬──────┘ - Verify aesthetics & visual appeal │
│ │ │
│ ▼ │
│ Score ≥ 9? ──YES──► Accept & Output │
│ │ │
│ NO │
│ │ │
│ ▼ │
│ Generate SPECIFIC improvement feedback ──► Loop back to Step 2 │
│ │
└──────────────────────────────────────────────────────────────────────────┘
Constants
- **IMAGE_MODEL = `gemini-3-pro-image-preview`** — Paperbanana (Nano Banana Pro) for image rendering
- **REASONING_MODEL = `gemini-3-pro-preview`** — Gemini for layout optimization and style checking
- **MAX_ITERATIONS = 5** — Maximum refinement rounds
- **TARGET_SCORE = 9** — Minimum acceptable score (1-10) — RAISED FOR QUALITY
- **OUTPUT_DIR = `figures/ai_generated/`** — Output directory
- **API_KEY_ENV = `GEMINI_API_KEY`** — Environment variable
CVPR/ICLR/NeurIPS Top-Tier Conference Style Guide
**What "CVPR Style" Actually Means:**
Visual Standards
- **Clean white background** — No decorative patterns or gradients (unless subtle)
- **Sans-serif fonts** — Arial, Helvetica, or Computer Modern; minimum 14pt
- **Subtle color palette** — Not rainbow colors; use 3-5 coordinated colors
- **Print-friendly** — Must be readable in grayscale (many reviewers print papers)
- **Professional borders** — Thin (2-3px), solid colors, not flashy
Layout Standards
- **Horizontal flow** — Left-to-right is the standard for pipelines
- **Clear grouping** — Use subtle background boxes to group related modules
- **Consistent sizing** — Similar components should have similar sizes
- **Balanced whitespace** — Not cramped, not sparse
Arrow Standards (MOST CRITICAL)
- **Thick strokes** — 4-6px minimum (thin arrows disappear when printed)
- **Clear ar
Read more
name: aris-paper-illustration description: "Generate publication-quality AI illustrations for academic papers using Gemini image generation. Creates architecture diagrams, method illustrations with Claude-supervised iterative refinement loop. Use when user says \"生成图表\", \"画架构图\", \"AI绘图\", \"paper illustration\", \"generate diagram\", or needs visual figures for papers." argument-hint: "[description-or-method-file]" allowed-tools: Bash(*), Read, Write, Edit, Grep, Glob, Agent, mcp__codex__codex, mcp__codex__codex-reply, WebSearch license: MIT metadata: author: wanshuiyin/ARIS version: "1.0.0"
Paper Illustration: Multi-Stage Claude-Supervised Figure Generation
Generate publication-quality illustrations using a **multi-stage workflow** with **Claude as the STRICT supervisor/reviewer**.
Core Design Philosophy
┌──────────────────────────────────────────────────────────────────────────┐ │ MULTI-STAGE ITERATIVE WORKFLOW │ ├──────────────────────────────────────────────────────────────────────────┤ │ │ │ User Request │ │ │ │ │ ▼ │ │ ┌─────────────┐ │ │ │ Claude │ ◄─── Step 1: Parse request, create initial prompt │ │ │ (Planner) │ │ │ └──────┬──────┘ │ │ │ │ │ ▼ │ │ ┌─────────────┐ │ │ │ Gemini │ ◄─── Step 2: Optimize layout description │ │ │ (gemini-3-pro)│ - Refine component positioning │ │ │ Layout │ - Optimize spacing and grouping │ │ └──────┬──────┘ │ │ │ │ │ ▼ │ │ ┌─────────────┐ │ │ │ Gemini │ ◄─── Step 3: CVPR/NeurIPS style verification │ │ │ (gemini-3-pro)│ - Check color palette compliance │ │ │ Style │ - Verify arrow and font standards │ │ └──────┬──────┘ │ │ │ │ │ ▼ │ │ ┌─────────────┐ │ │ │ Paperbanana │ ◄─── Step 4: Render final image │ │ │ (gemini-3- │ - High-quality image generation │ │ │ pro-image) │ - Internal codename: Nano Banana Pro │ │ └──────┬──────┘ │ │ │ │ │ ▼ │ │ ┌─────────────┐ │ │ │ Claude │ ◄─── Step 5: STRICT visual review + SCORE (1-10) │ │ │ (Reviewer) │ - Verify EVERY arrow direction │ │ │ STRICT! │ - Verify EVERY block content │ │ └──────┬──────┘ - Verify aesthetics & visual appeal │ │ │ │ │ ▼ │ │ Score ≥ 9? ──YES──► Accept & Output │ │ │ │ │ NO │ │ │ │ │ ▼ │ │ Generate SPECIFIC improvement feedback ──► Loop back to Step 2 │ │ │ └──────────────────────────────────────────────────────────────────────────┘
Constants
- **IMAGE_MODEL = `gemini-3-pro-image-preview`** — Paperbanana (Nano Banana Pro) for image rendering
- **REASONING_MODEL = `gemini-3-pro-preview`** — Gemini for layout optimization and style checking
- **MAX_ITERATIONS = 5** — Maximum refinement rounds
- **TARGET_SCORE = 9** — Minimum acceptable score (1-10) — RAISED FOR QUALITY
- **OUTPUT_DIR = `figures/ai_generated/`** — Output directory
- **API_KEY_ENV = `GEMINI_API_KEY`** — Environment variable
CVPR/ICLR/NeurIPS Top-Tier Conference Style Guide
**What "CVPR Style" Actually Means:**
Visual Standards
- **Clean white background** — No decorative patterns or gradients (unless subtle)
- **Sans-serif fonts** — Arial, Helvetica, or Computer Modern; minimum 14pt
- **Subtle color palette** — Not rainbow colors; use 3-5 coordinated colors
- **Print-friendly** — Must be readable in grayscale (many reviewers print papers)
- **Professional borders** — Thin (2-3px), solid colors, not flashy
Layout Standards
- **Horizontal flow** — Left-to-right is the standard for pipelines
- **Clear grouping** — Use subtle background boxes to group related modules
- **Consistent sizing** — Similar components should have similar sizes
- **Balanced whitespace** — Not cramped, not sparse
Arrow Standards (MOST CRITICAL)
- **Thick strokes** — 4-6px minimum (thin arrows disappear when printed)
- **Clear ar
A Super AI Lab with massive AI Doctors as Assistants. Best IDE for Research via AI Power.
Repo: OpenLAIR/dr-claw
Other skills on dr-claw.
- /dr-claw
Dr. Claw skill for OpenClaw project discovery, idea intake, waiting-session triage, structured session control, event-driven notifications, and mobile reporting through the local drclaw CLI.
Open skill - /academic-researcher
Academic research assistant for literature reviews, paper analysis, and scholarly writing. Use when: reviewing academic papers, conducting literature reviews, writing research summaries, analyzing methodologies, formatting citations, or when user mentions academic research,
Open skill - /autogpt
Autonomous AI agent platform for building and deploying continuous agents. Use when creating visual workflow agents, deploying persistent autonomous agents, or building complex multi-step AI automation systems.
Open skill - /crewai
Multi-agent orchestration framework for autonomous AI collaboration. Use when building teams of specialized agents working together on complex tasks, when you need role-based agent collaboration with memory, or for production workflows requiring sequential/hierarchical
Open skill - /langchain
Framework for building LLM-powered applications with agents, chains, and RAG. Supports multiple providers (OpenAI, Anthropic, Google), 500+ integrations, ReAct agents, tool calling, memory management, and vector store retrieval. Use for building chatbots, question-answering
Open skill - /llamaindex
Data framework for building LLM applications with RAG. Specializes in document ingestion (300+ connectors), indexing, and querying. Features vector indices, query engines, agents, and multi-modal support. Use for document Q&A, chatbots, knowledge retrieval, or building RAG
Open skill

