7 skills published by Azhi-ss across 1 repository. Together they weigh 51 018 tokens — that is what loading all of them at once would cost you in context.
7 skills 51 018 tokens total
Extract and analyze architecture diagrams from academic PDFs or existing figure images. Use this skill whenever the user provides a PDF or image containing an architecture diagram and wants to extract, analyze, or redraw it — including "从PDF提取架构图", "架构图分析", "extract figures from pdf", "architecture diagram extraction", "analyze this diagram". Runs a local PDF extraction script and produces structured redraw parameters for downstream prompt skills.
Make palette decisions for academic figures — choose between classic and pastel style families, then recommend a colorblind-safe scheme with exact hex values based on venue, domain, figure type, and module count. Use this skill whenever the user asks about colors, palettes, style for a figure, including "用什么配色", "推荐配色", "what palette for NeurIPS", "Nature Blue", "classic vs pastel", "色盲友好配色", or any color-related question about academic diagrams.
Generate publication-ready figure prompts for image models (GPT-Image-2, Gemini NanoBanana, etc.) in the classic academic style. Use this skill whenever the user wants a box-border architecture diagram, framework/network/module figure, or JSON figure spec — including "生成框架图", "画架构图", "JSON配图规范", "academic figure prompt", "框架图JSON". Produces a structured JSON spec AND a 200-400 word English image prompt with icons, dimension labels, and panel grouping. For pastel/airy ICLR-style figures, route to academic-figure-prompt-pastel instead.
Generate publication-ready figure prompts in the modern pastel/airy ICLR/NeurIPS style with soft panels, tokens, pills, and rounded type. Use this skill whenever the user wants a pastel, airy, soft, or modern ML figure — including "pastel风格", "ICLR那种", "现代柔彩", "airy figure prompt", "token flow diagram", "2024-2025 conference style". Produces a layered English image prompt with P1/P2/P3 color schemes. For classic box-border CVPR/Nature diagrams, route to academic-figure-prompt instead.
Entry-point assistant for academic figure generation. Use this skill whenever the user wants to generate academic figures, paper diagrams, or architecture visualizations — including "帮我画图", "从仓库到配图走一遍", "完整论文配图工作流", "帮我分析这个仓库然后出图", or any end-to-end request from code/paper to figure. The assistant analyzes the input, presents a Figure Plan for user confirmation, then generates colors, prompts, and (when an image model is available) the final image. Routes repo-first (code) or paper-first (document/PDF) inputs.
Plan figures for an academic paper or document — map sections to figure types, count, priority, and aspect ratios. Use this skill whenever the user provides paper text, a PDF, an outline, section drafts, or asks what figures their paper needs, including "论文需要哪些图", "论文配图规划", "paper figure planning", "which figures should my paper have", "帮我规划配图". Produces a Figure Plan with must/strong/nice priorities and per-figure visual elements.
Quick-understanding doc for ML/DL, AI4Science, and research codebases — task type, stack, architecture, and figure-worthy innovations. Use when the user wants repo analysis, 仓库分析, or to understand a codebase before figure planning.