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fireworks-tech-graph

Stop drawing diagrams by hand. Describe your system in English or Chinese — get publication-ready SVG + PNG technical diagrams in seconds.

Overview

fireworks-tech-graph turns natural language descriptions into polished SVG diagrams, then exports them as high-resolution PNG via rsvg-convert. It ships with 7 visual styles and deep knowledge of AI/Agent domain patterns (RAG, Agentic Search, Mem0, Multi-Agent, Tool Call flows), plus full support for all 14 UML diagram types.

User: "Generate a Mem0 memory architecture diagram, dark style"
  → Skill classifies: Memory Architecture Diagram, Style 2
  → Generates SVG with swim lanes, cylinders, semantic arrows
  → Exports 1920px PNG
  → Reports: mem0-architecture.svg / mem0-architecture.png

Showcase

All samples exported at 1920px width (2× retina) via rsvg-convert. PNG is lossless and the right choice for technical diagrams — sharp edges, no JPEG compression artifacts on text/lines.

Style 1 — Flat Icon (default)

Mem0 Memory Architecture — white background, semantic arrows, layered memory system

Style 2 — Dark Terminal

Tool Call Flow — dark background, neon accents, monospace font

Style 3 — Blueprint

Microservices Architecture — deep blue background, grid lines, cyan strokes

Style 4 — Notion Clean

Agent Memory Types — minimal white, single accent color

Style 5 — Glassmorphism

Multi-Agent Collaboration — dark gradient background, frosted glass cards

Style 6 — Claude Official

System Architecture — warm cream background (#f8f6f3), Anthropic brand colors, clean professional aesthetic

Style 7 — OpenAI Official

API Integration Flow — pure white background, OpenAI brand palette, modern minimalist design

Stable Prompt Recipes

Use prompts like these when you want the model to stay close to the repo's strongest regression-tested outputs:

Style 1 — Flat Icon

Draw a Mem0 memory architecture diagram in style 1 (Flat Icon).
Use four horizontal sections: Input Layer, Memory Manager, Storage Layer, Output / Retrieval.
Include User, AI App / Agent, LLM, mem0 Client, Memory Manager, Vector Store, Graph DB, Key-Value Store, History Store, Context Builder, Ranked Results, Personalized Response.
Use semantic arrows for read, write, control, and data flow. Keep the layout clean and product-doc friendly.

Style 2 — Dark Terminal

Draw a tool call flow diagram in style 2 (Dark Terminal).
Show User query, Retrieve chunks, Generate answer, Knowledge base, Agent, Terminal, Source documents, and Grounded answer.
Use terminal chrome, neon accents, monospace typography, and semantic arrows for retrieval, synthesis, and embedding update.

Style 3 — Blueprint

Draw a microservices architecture diagram in style 3 (Blueprint).
Create numbered engineering sections like 01 // EDGE, 02 // APPLICATION SERVICES, 03 // DATA + EVENT INFRA, 04 // OBSERVABILITY.
Include Client Apps, API Gateway, Auth / Policy, three services, Event Router, Postgres, Redis Cache, Warehouse, and Metrics / Traces.
Use blueprint grid, cyan strokes, and a bottom-right title block.

Style 4 — Notion Clean

Draw an agent memory types diagram in style 4 (Notion Clean).
Compare Sensory Memory, Working Memory, Episodic Memory, Semantic Memory, and Procedural Memory around a central Agent core.
Use a minimal white layout, neutral borders, one accent color for arrows, and short storage tags for each memory type.

Style 5 — Glassmorphism

Draw a multi-agent collaboration diagram in style 5 (Glassmorphism).
Use three sections: Mission Control, Specialist Agents, and Synthesis.
Include User brief, Coordinator Agent, Research Agent, Coding Agent, Review Agent, Shared Memory, Synthesis Engine, and Final response.
Use frosted cards, soft glow, and semantic arrows for delegation, shared memory writes, and synthesis output.

Style 6 — Claude Official

Draw a system architecture diagram in style 6 (Claude Official).
Use left-side layer labels: Interface Layer, Core Layer, Foundation Layer.
Include Client Surface, Gateway, Task Planner, Model Runtime, Policy Guardrails, Memory Store, Tool Runtime, Observability, and Registry.
Use warm cream background, restrained brand-like palette, generous whitespace, and a bottom-right legend.

Style 7 — OpenAI Official

Draw an API integration flow diagram in style 7 (OpenAI Official).
Use three sections: Entry, Model + Tools, and Delivery.
Include Application, OpenAI SDK Layer, Prompt Builder, Model Runtime, Tool Calls, Response Formatter, Observability, and Release Control.
Keep the look minimal, white, precise, and modern with clean green-accented arrows.

Features

• 7 visual styles — from clean white docs to dark neon to frosted glass to official brand styles
• Executable style system — style guides are encoded into the generator, not only documented in markdown
• 14 diagram types — Full UML support (Class, Component, Deployment, Package, Composite Structure, Object, Use Case, Activity, State Machine, Sequence, Communication, Timing, Interaction Overview, ER Diagram) plus AI/Agent domain diagrams
• AI/Agent domain patterns — RAG, Agentic Search, Mem0, Multi-Agent, Tool Call, and more built-in
• Semantic shape vocabulary — LLM = double-border rect, Agent = hexagon, Vector Store = ringed cylinder
• Semantic arrow system — color + dash pattern encode meaning (write vs read vs async vs loop)
• Product icons — 40+ products with brand colors: OpenAI, Anthropic, Pinecone, Weaviate, Kafka, PostgreSQL…
• Swim lane grouping — automatic layer labeling for complex architectures
• SVG + PNG output — SVG for editing, 1920px PNG for embedding
• rsvg-convert compatible — no external font fetching, pure inline SVG

Installation

npx skills add yizhiyanhua-ai/fireworks-tech-graph

This skill is installed from the GitHub repository. The npm package page is the public package/distribution page:

https://www.npmjs.com/package/@yizhiyanhua-ai/fireworks-tech-graph

Do not use the npm package name with skills add, because the CLI resolves install sources as GitHub/local paths.

Update

npx skills add yizhiyanhua-ai/fireworks-tech-graph --force -g -y

Re-run add --force to pull the latest version of this skill.

Or clone directly:

git clone https://github.com/yizhiyanhua-ai/fireworks-tech-graph.git ~/.claude/skills/fireworks-tech-graph

Requirements

# macOS
brew install librsvg

# Ubuntu/Debian
sudo apt install librsvg2-bin

# Verify
rsvg-convert --version

Why Not Mermaid or draw.io?

Mermaid is great for quick inline diagrams in markdown. draw.io is great for manual polishing. fireworks-tech-graph is optimized for describing a system and getting a polished diagram immediately, without writing DSL syntax or clicking around a GUI.

Usage

Trigger phrases

The skill auto-triggers on:

generate diagram / draw diagram / create chart / visualize
architecture diagram / flowchart / sequence diagram / data flow

Basic usage

Draw a RAG pipeline flowchart

Generate an Agentic Search architecture diagram

Specify style

Draw a microservices architecture diagram, style 2 (dark terminal)

Draw a multi-agent collaboration diagram --style glassmorphism

Specify output path

Generate a Mem0 architecture diagram, output to ~/Desktop/

Create a tool call flow diagram --output /tmp/diagrams/

Example Prompts by Scenario

AI/Agent Systems

Compare Agentic RAG vs standard RAG in a feature matrix, Notion clean style

→ Comparison matrix: RAG vs Agentic RAG, covering retrieval strategy, agent loop, tool use

Generate a Mem0 memory architecture diagram with vector store, graph DB, KV store, and memory manager

→ Memory Architecture with swim lanes: Input → Memory Manager → Storage tiers → Retrieval

Draw a Multi-Agent diagram: Orchestrator dispatches 3 SubAgents (search / compute / code execution), results aggregated

→ Agent Architecture with hexagons, tool layers, and result aggregation

Visualize the Tool Call execution flow: LLM → Tool Selector → Execution → Parser → back to LLM

→ Flowchart with decision loop showing tool invocation cycle

Draw the 5 agent memory types: Sensory, Working, Episodic, Semantic, Procedural

→ Mind map or layered architecture showing memory tiers from sensory to procedural

Infrastructure & Cloud

Draw a microservices architecture: Client → API Gateway → [User Service / Order Service / Payment Service] → PostgreSQL + Redis

→ Architecture diagram with horizontal layers, swim lanes per service cluster

Generate a data pipeline diagram: Kafka → Spark processing → write to S3 → Athena query

→ Data flow diagram with labeled arrows (stream / batch / query)

Draw a Kubernetes deployment: Ingress → Service → [Pod × 3] → ConfigMap + PersistentVolume

→ Architecture with dashed containers per namespace, solid arrows for traffic flow

API & Sequence Flows

Draw an OAuth2 authorization code flow sequence diagram: User → Client → Auth Server → Resource Server

→ Sequence diagram with vertical lifelines and activation boxes

Draw the ChatGPT Plugin call sequence diagram

→ Sequence: User → ChatGPT → Plugin Manifest → API → Response chain

Decision & Process Flows

Draw a pre-launch QA flowchart for an AI app: Code Review → Security Scan → Performance Test → Manual Approval → Deploy

→ Flowchart with diamond decision nodes and parallel branches

Generate a feature comparison matrix: RAG vs Fine-tuning vs Prompt Engineering

→ Comparison matrix with checked/unchecked cells across cost, latency, accuracy, flexibility

Concept Maps

Visualize the LLM application tech stack: from foundation model to SDK to app framework to deployment

→ Layered architecture or mind map from model layer to product layer

Draw an AI Agent capability map: Perception / Memory / Reasoning / Action / Learning

→ Mind map with central "AI Agent" node and 5 radial branches

Styles

Each style has a dedicated reference file in references/ with exact color tokens, SVG patterns, and templates. The generator also consumes style-aware structure fields such as containers, semantic nodes[].kind, arrows[].flow, and explicit port anchors so sample-grade layouts can be reproduced more consistently.

Useful high-leverage fields for style-specific polish:

• style_overrides to nudge title alignment or palette tokens without forking a full style
• containers[].header_prefix / containers[].header_text for blueprint-style numbered section headers such as 01 // EDGE
• containers[].side_label for Claude-style left layer labels
• window_controls, meta_left, meta_center, meta_right for terminal / document chrome
• blueprint_title_block for engineering title boxes in style 3

Style Selection Guide

For UML Diagrams:

• Class/Component/Package: Style 1 (Flat Icon) or Style 4 (Notion Clean) — clear structure, easy to read
• Sequence/Timing: Style 2 (Dark Terminal) — monospace fonts help with alignment
• State Machine/Activity: Style 3 (Blueprint) — engineering aesthetic fits process flows
• Use Case/Interview: Style 1 (Flat Icon) — colorful, accessible

For AI/Agent Diagrams:

• RAG/Agentic Search: Style 2 (Dark Terminal) or Style 5 (Glassmorphism) — tech-forward aesthetic
• Memory Architecture: Style 3 (Blueprint) — emphasizes layered storage tiers
• Multi-Agent: Style 5 (Glassmorphism) — frosted cards distinguish agent boundaries

For Documentation:

• Internal docs: Style 4 (Notion Clean) — minimal, wiki-friendly
• Blog posts: Style 1 (Flat Icon) — colorful, engaging
• GitHub README: Style 2 (Dark Terminal) — matches dark theme
• Presentations: Style 5 (Glassmorphism) or Style 6 (Claude Official) — polished

Brand-Specific:

• Anthropic/Claude projects: Style 6 (Claude Official) — warm cream background, brand colors
• OpenAI projects: Style 7 (OpenAI Official) — clean white, OpenAI palette

Diagram Types

UML Diagram Support (14 Types)

AI/Agent Domain Patterns

Built-in pattern knowledge:

RAG Pipeline         → Query → Embed → VectorSearch → Retrieve → LLM → Response
Agentic RAG          → adds Agent loop + Tool use
Agentic Search       → Query → Planner → [Search/Calc/Code] → Synthesizer
Mem0 Memory Layer    → Input → Memory Manager → [VectorDB + GraphDB] → Context
Agent Memory Types   → Sensory → Working → Episodic → Semantic → Procedural
Multi-Agent          → Orchestrator → [SubAgent×N] → Aggregator → Output
Tool Call Flow       → LLM → Tool Selector → Execution → Parser → LLM (loop)

Shape Vocabulary

Shapes encode semantic meaning consistently across all styles:

Arrow Semantics

File Structure

fireworks-tech-graph/
├── SKILL.md                      # Main skill — diagram types, layout rules, shape vocab
├── README.md                     # This file (English)
├── README.zh.md                  # Chinese version
├── references/
│   ├── style-1-flat-icon.md      # White background, colored accents
│   ├── style-2-dark-terminal.md  # Dark bg, neon accents, monospace
│   ├── style-3-blueprint.md      # Blueprint grid, cyan lines
│   ├── style-4-notion-clean.md   # Minimal, white, single arrow color
│   ├── style-5-glassmorphism.md  # Dark gradient, frosted glass cards
│   ├── style-6-claude-official.md # Warm cream background, Anthropic brand
│   ├── style-7-openai.md      # Clean white, OpenAI brand palette
│   └── icons.md                  # 40+ product icons + semantic shapes
├── agents/
│   └── openai.yaml              # Agent metadata for compatible runtimes
├── fixtures/
│   ├── mem0-style1.json         # Style 1 regression fixture
│   ├── tool-call-style2.json    # Style 2 regression fixture
│   └── ...                      # Additional sample-grade fixtures per style
├── scripts/
│   ├── generate-diagram.sh       # Validate SVG + export PNG
│   ├── generate-from-template.py # Create starter SVGs from templates
│   ├── validate-svg.sh           # Validate SVG syntax
│   └── test-all-styles.sh        # Batch test all styles
├── assets/
│   └── samples/                  # Showcase diagram PNGs
├── templates/
│   ├── architecture.svg         # Architecture starter template
│   ├── data-flow.svg            # Data-flow starter template
│   └── ...                      # Additional diagram templates
└── agentloop-core.svg           # Included sample SVG

Product Icon Coverage

AI/ML: OpenAI, Anthropic/Claude, Google Gemini, Meta LLaMA, Mistral, Cohere, Groq, Hugging Face

AI Frameworks: Mem0, LangChain, LlamaIndex, LangGraph, CrewAI, AutoGen, DSPy, Haystack

Vector DBs: Pinecone, Weaviate, Qdrant, Chroma, Milvus, pgvector, Faiss

Databases: PostgreSQL, MySQL, MongoDB, Redis, Elasticsearch, Neo4j, Cassandra

Messaging: Kafka, RabbitMQ, NATS, Pulsar

Cloud: AWS, GCP, Azure, Cloudflare, Vercel, Docker, Kubernetes

Observability: Grafana, Prometheus, Datadog, LangSmith, Langfuse, Arize

Troubleshooting

License

MIT © 2025 fireworks-tech-graph contributors