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diagram-drawingdiagram drawing 搜索

Agent Skill

diagram-drawing 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

3,960

周安装

165

GitHub Stars

公开资料未说明

下载量

1,320
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:diagram-drawing(diagram drawing 搜索)
来源仓库:https://github.com/0xcjl/diagram-drawing
安装命令:
openclaw skills install diagram-drawing
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install diagram-drawing

简介

从自然语言生成高质量的 SVG 和 PNG 技术图表,包括架构、流程图、序列、UML、思维导图和 AI 代理系统。

SKILL.md

name
diagram-drawing
description
Generate production-quality SVG+PNG technical diagrams from natural language. Use when user wants to create any technical diagram — architecture, data flow, flowchart, sequence, agent/memory, UML, mind map, or concept map — and export as SVG+PNG. Triggers (EN): "generate diagram", "draw diagram", "create chart", "visualize", "architecture diagram", "flowchart", "sequence diagram", "data flow", "tech graph". Triggers (中文): "画图", "帮我画", "生成图", "做个图", "架构图", "流程图", "可视化", "出图", "技术图". Also triggers on any system/flow description the user wants illustrated, especially for AI/Agent systems (RAG, Agentic Search, Mem0, Multi-Agent, Tool Call).

Diagram Drawing

Generate production-quality SVG+PNG technical diagrams from natural language descriptions.

Quick Start

  1. Classify diagram type from user request
  2. Extract layers, nodes, edges, flows
  3. Plan layout (top→bottom for most, left→right for wide flows)
  4. Generate SVG with semantic shapes and arrows
  5. Export PNG at 1920px width

Diagram Types & Layout

Architecture

Nodes = services/components. Group into horizontal layers (top→bottom or left→right).

  • Typical: Client → Gateway → Services → Data/Storage
  • Dashed <rect> containers group related services
  • ViewBox: 0 0 960 600 or 0 0 960 800

Data Flow

Label every arrow with data type ("embeddings", "query", "context"). Wider arrows for primary paths.

Flowchart / Process

Top-to-bottom preferred. Diamond = decision, rounded rect = process, parallelogram = I/O.

Agent Architecture

5 layers: Input → Agent Core (LLM) → Memory → Tools → Output

  • Show iterative reasoning with loop arcs (curved arrows)
  • Separate memory read/write paths visually

Memory Architecture (Mem0-style)

  • Show memory write path and read path separately
  • Tiers: Working → Short-term → Long-term → External Store
  • Operations: store(), retrieve(), forget(), consolidate()

Sequence Diagram

  • Vertical lifelines (dashed lines)
  • Horizontal arrows = messages, top-to-bottom time order
  • Activation boxes (thin filled rects on lifeline)
  • ViewBox height = 80 + (num_messages × 50)

Comparison Matrix

  • Column headers = systems, row headers = attributes
  • Checked: #dcfce7 fill + ✓; unsupported: #f9fafb fill

Mind Map

  • Central node at cx=480, cy=280
  • Curved bezier branches, not straight lines
  • First-level: 360/N degrees apart

UML Diagrams

  • Class: 3-compartment rect (name / attributes / methods)
  • Use Case: Ellipse + stick figure actors
  • State Machine: Rounded rects + transitions with guards
  • ER: Rect entities + diamond relationships + cardinality

Shape Vocabulary

ConceptShape
User / HumanCircle head + body path
LLM / ModelRounded rect, double border, ⚡
AgentHexagon or double-border rect
Memory (short-term)Dashed-border rounded rect
Memory (long-term)Solid cylinder
Vector StoreCylinder with grid lines
Graph DB3 overlapping circles
Tool / FunctionRect with ⚙ icon
API GatewayHexagon
Queue / StreamHorizontal pipe/tube
DocumentFolded-corner rect
Browser / UIRect with 3-dot titlebar
DecisionDiamond

Arrow Semantics

Flow TypeColorStrokeDash
Primary data#2563eb blue2px solidnone
Control/trigger#ea580c orange1.5px solidnone
Memory read#059669 green1.5px solidnone
Memory write#059669 green1.5px5,3
Async/event#6b7280 gray1.5px4,2
Embedding#7c3aed purple1px solidnone
Feedback/loop#7c3aed purple1.5px curvednone

Always include a legend when 2+ arrow types are used.

Workflow (Always Follow)

  1. Classify diagram type
  2. Extract structure — layers, nodes, edges, flows
  3. Plan layout
  4. Load style reference from references/styles.md for exact colors
  5. Map nodes to shapes
  6. Check references/icons.md for known products
  7. Generate SVG
  8. Validate: run python3 scripts/svg2png.py <file.svg> or rsvg-convert
  9. Export PNG at 1920px width
  10. Report output paths

SVG Generation Strategy

Estimate complexity:

  • Count nodes: N, arrows: A
  • Estimated SVG lines: L = 50 (header) + N×15 + A×3 + 20 (legend)

Generation method:

  • L < 150: Write tool directly (single call, most reliable)
  • L ≥ 150: Python script via exec tool (avoids heredoc issues)

Python method (recommended for complex SVGs):

python3 << 'EOF'
svg_content = '''<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 960 600">
...complete SVG here...
</svg>'''

with open('/path/to/output.svg', 'w') as f:
    f.write(svg_content)
print("Generated output.svg")
EOF

SVG Technical Rules

  • ViewBox: 0 0 960 600 default; 0 0 960 800 tall; 0 0 1200 600 wide
  • Fonts: <style>font-family: system-ui, Helvetica, sans-serif</style>no external @import
  • <defs>: arrow markers, gradients, filters, clip paths
  • Text: minimum 12px, prefer 13-14px labels
  • All arrows: <marker> with markerEnd, sized markerWidth="10" markerHeight="7"
  • Drop shadows: <feDropShadow> sparingly
  • Curved paths: M x1,y1 C cx1,cy1 cx2,cy2 x2,y2 cubic bezier

Built-in Patterns

RAG Pipeline: Query → Embed → VectorSearch → Retrieve → LLM → Response Agentic RAG: Query → Embed → VectorSearch → Agent loop (Tool use) → LLM → Response Agentic Search: Query → Planner → [Search / Calculator / Code] → Synthesizer → Response Mem0 Memory: Input → Memory Manager → [Write: VectorDB + GraphDB] → [Read: Retrieve+Rank] → Context Multi-Agent: Orchestrator → [SubAgent A / B / C] → Aggregator → Output Tool Call Flow: LLM → Tool Selector → Tool Execution → Parser → LLM (loop)

Error Recovery

ProblemFix
PNG blank/blackRemove @import url() — use system fonts only
rsvg-convert not foundUse python3 scripts/svg2png.py fallback
SVG cut off at bottomIncrease ViewBox height
Text overflowingtext-anchor="middle" + shorten label
Marker undefinedEnsure <marker id="..."> in <defs>
Arrow crossing nodesUse orthogonal routing or bezier detour

Output

  • Default: ./[derived-name].svg and ./[derived-name].png
  • Custom: user specifies --output /path/

Styles

#NameBackgroundBest For
1Flat Icon (default)WhiteBlogs, docs, slides
2Dark Terminal#0f0f1aGitHub, dev articles
3Blueprint#0a1628Architecture docs
4Notion CleanWhiteNotion, wikis
5Glassmorphism#0d1117 gradientProduct sites, keynotes
6Claude Official#f8f6f3 warm creamAnthropic-style diagrams
7OpenAI OfficialPure whiteOpenAI-style diagrams

Load references/styles.md for exact color tokens and patterns.

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

83.78%
按下载量换算1,106

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

通过

权限和风险

操作浏览器

该 Skill 可能涉及浏览器控制能力,使用时可能读取或操作网页内容,需要在受控环境中确认权限边界。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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