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    draw architecture

    1

    Generates publication-quality AI and software architecture diagrams in TikZ, SVG, and JSON.

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    draw architecture

    draw architecture

    Example session with this skill installed

    Generate a professional diagram for a RAG system with a vector database, an LLM, and a retrieval reranker. Export as SVG and TikZ.

    • Read your context and instructions
    • Compiled the draw architecture

    I have generated the RAG system architecture diagram. I extracted the components (User, Query, Vector DB, Reranker, and LLM) and established a sequential data flow with a feedback loop for the reranking stage.

    RAG System Architecture

    ComponentFunctionInterface
    User QueryEntry PointString Input
    Vector DBSemantic RetrievalTop-K Embeddings
    RerankerPrecision FilteringScored Documents
    LLMGenerationFinal Response
    {
  "nodes": [
    {"id": "user", "label": "User", "type": "actor", "x": 0, "y": 0},
    {"id": "vdb", "label": "Vector DB", "type": "storage", "x": 2, "y": 0},
    {"id": "rerank", "label": "Reranker", "type": "process", "x": 4, "y": 0},
    {"id": "llm", "label": "LLM", "type": "model", "x": 6, "y": 0}
  ],
  "edges": [
    {"from": "user", "to": "vdb", "label": "query"},
    {"from": "vdb", "to": "rerank", "label": "candidates"},
    {"from": "rerank", "to": "llm", "label": "context"},
    {"from": "llm", "to": "user", "label": "response", "kind": "dashed"}
  ]
}

    Figure: A standard RAG pipeline showing retrieval flow from Vector DB through a Reranker to the LLM, with a dashed response path to the user.
    Source: output/rag_arch.svg, output/rag_arch.tex, output/rag_arch.json.
    Verified: Semantic checks passed; no overlapping routes; label positions optimized.
    Assumptions: Used standard cosine similarity for VDB; assumed synchronous processing.

    Next steps

    • Run python $SKILL_ROOT/scripts/archdiagram.py render rag_arch.json --theme paper to update typography.
    • Open rag_arch.tex in Overleaf for final LaTeX paper integration.
    • Add via waypoints to the JSON if you need to route the response line around the Reranker node.

    Connects securely to your tools. The creator never sees your data.

    About this skill

    Architecture diagrams in technical docs and papers are often imprecise, messy, or impossible to edit once rendered. This skill generates exact, publication-quality schematics for AI systems, neural networks, and software services from code or descriptions.

    What it does

    • Generate schematics for RAG, multi-agent systems, and service topologies using a custom SVG/TikZ renderer.
    • Visualize neural architectures including Transformers, CNNs, and GNNs with precise tensor shapes and data flow.
    • Extract evidence from PyTorch code, papers, or configs to build diagrams grounded in actual implementation.
    • Search a figure atlas containing over 100 TikZ master templates for vision, audio, robotics, and LLM components.
    • Export editable formats including raw TikZ for LaTeX, structured JSON scenes, and production-ready SVG/PDF.

    How it works

    1. Analyze evidence by inspecting provided source code or text to identify components, interfaces, and tensor dimensions.
    2. Select a route between the JSON-to-SVG renderer for systems or native TikZ templates for mathematical neural layers.
    3. Build the source using archdiagram.py to validate layout, handle edge routing, and manage group boundaries.
    4. Run semantic review to verify arrow directions, resolve label collisions, and confirm residual connection origins.

    Frameworks & tools

    Built on a Python 3.10+ stdlib renderer. Supports TikZ/LaTeX for math-heavy figures and integrates with Inkscape for PDF/PNG exports.

    Why this beats prompting it yourself

    Standard LLMs struggle with spatial reasoning, leading to overlapping nodes and "hallucinated" connections in Mermaid or Graphviz. This skill uses a specialized geometric renderer and a vetted atlas of 100+ professional templates to ensure mathematical and architectural accuracy.

    Use cases

    • Research paper figures that require monochrome LaTeX typography and TikZ source.
    • System documentation for multi-agent workflows and RAG pipelines.
    • Code-to-diagram conversion to visualize existing PyTorch or backend service structures.
    • Technical presentations needing editable, high-resolution vector schematics.

    Known limitations

    Does not train models or infer missing code logic. High-fidelity PDF/PNG export requires a local Inkscape installation. TikZ compilation requires a TeX engine.

    How to use

    Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.

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    Requires

    Python
    Git

    Listed1 day ago

    What's inside

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