Academic Figure Workflow Orchestrator
Academic Figure Workflow Orchestrator
Use this skill as the pack entrypoint. Route the task to the minimum set of sibling skills needed to get the user from raw inputs to a usable academic figure prompt.
Do not dump every sub-skill at once. Identify the user's current stage, load only the relevant sibling skill files, and move the workflow forward with the fewest necessary steps.
Sibling Skill Routing
Read the sibling skill files only when their stage is needed:
../academic-repo-analyzer/SKILL.md: Use when the user provides a repository, asks what a codebase does, or needs a quick understanding document before figure planning.../academic-figure-paper-analyzer/SKILL.md: Use when the user provides a paper, section draft, abstract, or method description and needs figure planning.../academic-figure-architecture-extractor/SKILL.md: Use when the user provides a PDF, wants to extract architecture diagrams, analyze diagram structure, or get color scheme recommendations for existing diagrams.../academic-figure-color-expert/SKILL.md: Use when the user asks for palette selection, venue-specific style advice, or accessibility-safe colors.../academic-figure-prompt/SKILL.md: Use when the user wants a classic academic figure prompt.../academic-figure-prompt-pastel/SKILL.md: Use when the user explicitly wants a modern ML / ICLR / NeurIPS 2024-2025 airy pastel style.
Stage Detection
Classify the request into one of these entry states:
- Repo-first The user has a repository or codebase and wants to understand it before planning figures.
- Paper-first The user has a paper, PDF, outline, or section text and wants figure planning directly.
- Architecture-extraction-first The user has a PDF and wants to extract architecture diagrams, analyze diagram structure, or get color recommendations for existing diagrams.
- Prompt-first The user already knows the target figure and wants a prompt now.
- Color-first The user mainly wants palette selection or venue-specific visual style guidance.
If the user is ambiguous, infer the most likely entry state from the artifacts they provided. Ask only for missing information that blocks the next step.
Default Workflow
Use the lightest valid path:
- Repo-first: repo analyzer -> paper analyzer if figure planning is requested -> color expert if palette is unspecified -> prompt skill
- Paper-first: paper analyzer -> color expert if palette is unspecified -> prompt skill
- Architecture-extraction-first: architecture extractor -> paper analyzer if figure planning is requested -> color expert if palette is unspecified -> prompt skill
- Prompt-first: choose prompt style -> collect only the minimum missing figure details -> generate prompt
- Color-first: color expert -> optionally continue into prompt generation
Do not force the full chain when the user wants only one stage.
Minimal Input Checklist
Before generating a final prompt, make sure you know:
- figure type
- subject or method being visualized
- target venue or style preference, if any
- palette choice, or a safe default
- any mandatory labels, modules, equations, or comparisons
If one or two details are missing, proceed with explicit assumptions. If core content is missing, ask targeted questions instead of hallucinating the figure structure.
Default Decisions
- If no palette is specified, prefer a safe default and say so explicitly. Default to
Okabe-Itounless the user's venue or requested style strongly suggests otherwise. - If the user mentions ICLR / NeurIPS / ICML 2024-2025 airy pastel aesthetics, route to
academic-figure-prompt-pastel. - Otherwise route to
academic-figure-prompt. - If the user has both a repo and a paper, prioritize the paper for figure planning and use the repo only to fill technical gaps.
Handoff Artifacts
When moving between stages, carry forward compact structured artifacts instead of re-explaining everything.
Quick Understanding Doc
Include:
- task type
- model family or method category
- input/output summary
- core modules
- training or inference flow
- likely figure-worthy innovations
Figure Plan
Include:
- recommended figure count
- figure types
- section-to-figure mapping
- priority ranking
- notes on what must appear visually
Palette Decision
Include:
- selected palette
- 2-3 core hex colors
- why it fits the venue or task
- accessibility notes if relevant
Prompt Package
Include:
- final English image prompt
- short Chinese explanation of what the prompt is optimizing for
- any explicit assumptions made due to missing input
Output Contract
When acting as the orchestrator, respond in this order:
- Current stage
- Next action
- Result or required clarification
- Handoff artifact or final prompt
Keep the orchestration visible but concise. The user should be able to see where they are in the pipeline without reading an essay.
Stop Conditions
Stop when one of these is true:
- the user received the requested deliverable for the current stage
- the next step requires missing source material the user has not provided
- the user explicitly wants evaluation instead of generation
Do not continue into downstream stages unless the user asked for them or the request clearly implies an end-to-end workflow.
More from azhi-ss/academic-figure-skills
academic paper analyzer & figure planner
Use this skill whenever the user wants to analyze an academic paper, identify figure-worthy content, plan which figures to generate, suggest figure types and count per section, or says "分析论文配图需求", "论文需要哪些图", "论文配图规划", "paper figure planning", "analyze paper for figures", or "which figures does my paper need".
12academic figure prompt
Use this skill whenever the user wants detailed English prompts for AI image tools to produce top-conference-quality academic figures, needs prompts for framework diagrams, architecture diagrams, pipeline flowcharts, module detail diagrams, comparison figures, or data-pattern grids, or says "论文配图提示词", "生成论文配图", "学术论文生图", "架构图提示词", "框架图提示词", "顶会风格配图", "CVPR 风格图", "NeurIPS 风格图", "paper figure prompt", or "academic diagram prompt".
11academic repo analyzer
Use this skill whenever the user wants to analyze a deep learning or machine learning code repository, understand what it does, identify its architecture and tech stack, generate a quick understanding document for downstream figure planning, or says "分析代码仓库", "仓库分析", "repo analyzer", "analyze this repo", "理解这个代码库", "what does this repo do", or "code repository analysis".
11academic figure color expert
Use this skill whenever the user wants help choosing an academic figure color palette, needs venue-specific or colorblind-safe design advice, wants a paper color scheme recommendation, wants to match a color scheme for extracted architecture diagrams, or says "学术配图配色", "论文配色方案", "色盲友好配色", "学术配色", "架构图配色", "academic color palette", "colorblind safe figure", "paper color scheme", "architecture diagram color matching".
11academic figure prompt — modern ml airy style
Use this skill whenever the user wants modern ML or RL paper-style figure prompts matching recent ICLR, NeurIPS, or ICML 2024-2025 aesthetics, needs a soft pastel academic diagram style, or says "pastel风格论文配图", "现代ML论文配图", "modern ML figure prompt", "pastel academic figure", "ICLR 2024 风格图", or "NeurIPS 2025 风格图".
11academic figure architecture extractor & analyzer
Use this skill whenever the user wants to extract architecture diagrams from academic papers, filter out invalid images, analyze the structure and components of diagrams, automatically match suitable color schemes, or says "提取论文架构图", "架构图分析", "从PDF中提取图表", "自动分析架构图", "architecture diagram extraction", "extract figures from pdf", "analyze architecture diagram".
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