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sf-diagram-nanobananapro

by @jaganprov1.0.0
4.6(8)

使用Nano Banana Pro生成渲染级可视化内容,包括ERD、UI模型、架构图和演示就绪图像

visualizationdiagram-generationui-mockupserdsalesforceGitHub
安装方式
npx skills add jaganpro/sf-skills --skill sf-diagram-nanobananapro
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Before / After 效果对比

1
使用前

手动绘制架构图、调整布局、导出多版本、适配不同场景,一张图需要2-3小时

使用后

AI自动生成渲染级可视化图像,20分钟获得演示就绪的专业图表

description SKILL.md

sf-diagram-nanobananapro

sf-diagram-nanobananapro: Salesforce Visual AI Skill

Use this skill when the user needs rendered visuals, not text diagrams: ERDs, UI mockups, architecture illustrations, slide-ready images, or image edits using Nano Banana Pro.

Hard Gate: Prerequisites First

Always run the prerequisites check before using the skill:

~/.claude/skills/sf-diagram-nanobananapro/scripts/check-prerequisites.sh

If prerequisites fail, stop and route the user to setup guidance in:

When This Skill Owns the Task

Use sf-diagram-nanobananapro when the user wants:

  • PNG / SVG-style image output

  • rendered ERDs or architecture diagrams

  • LWC or Experience Cloud mockups / wireframes

  • visual polish beyond Mermaid

  • edits to a previously generated image

Delegate elsewhere when the user wants:

Required Context to Gather First

Ask for or infer:

  • image type: ERD, UI mockup, architecture illustration, or image edit

  • subject scope and key entities / systems

  • target quality: draft vs presentation vs production asset

  • preferred style and aspect ratio

  • whether the user wants quick mode or an interview-driven prompt build

Interview-First Workflow

Unless the user explicitly asks for quick/simple/just generate, ask clarifying questions first.

Minimum question set

Request type Ask about

ERD / schema objects, visual style, purpose, extras

UI mockup component type, object/context, device/layout, style

architecture image systems, boundaries, protocols, emphasis

image edit what to keep, what to change, output quality

Question bank: references/interview-questions.md

Quick mode defaults

If the user says “quick”, “simple”, or “just generate”, default to:

  • professional style

  • 1K draft output

  • legend included when helpful

  • one image first, then iterate

Recommended Workflow

1. Gather inputs

Decide which of these are needed:

  • object list / metadata

  • purpose: draft vs presentation vs documentation

  • desired aesthetic

  • aspect ratio / resolution

  • whether this is a fresh render or edit of an existing image

2. Build a concrete prompt

Good prompts specify:

  • subject and scope

  • composition / layout

  • color treatment

  • labels / legends / relationship lines

  • output quality goal

3. Generate a fast draft first

gemini --yolo "/generate 'Professional Salesforce ERD with Account, Contact, Opportunity; clean legend; white background; Salesforce-style colors'"

4. Iterate before final

Use natural-language edits:

gemini --yolo "/edit 'Move Account to center, thicken relationship lines, add legend in bottom right'"

5. Use the Python script for controlled final output

Use the script when you need higher resolution or explicit edit inputs:

uv run scripts/generate_image.py \
  -p "Final production-quality Salesforce ERD with legend and field highlights" \
  -f "crm-erd-final.png" \
  -r 4K

Full iteration guide: references/iteration-workflow.md

Default Style Guidance

For ERDs, default to the architect.salesforce.com aesthetic unless the user asks otherwise:

  • dark border + light fill cards

  • cloud-specific accent colors

  • clean labels and relationship lines

  • presentation-ready whitespace and hierarchy

Style guide: references/architect-aesthetic-guide.md

Common Patterns

Pattern Default approach

visual ERD get metadata if available, then render a draft first

LWC mockup use component template + user context + one draft iteration

architecture illustration emphasize systems and flows, reduce field-level detail

image refinement use /edit for small changes before regenerating

final production asset switch to script-driven 2K/4K generation

Examples: references/examples-index.md

Output / Review Guidance

After generating, do one of these:

  • open the file in Preview for visual inspection

  • attach/read the image in the coding session for multimodal review

  • ask the user whether to iterate on layout, labeling, or color before finalizing

Keep the first pass cheap; only spend on high-res output after the composition is right.

Cross-Skill Integration

Need Delegate to Reason

Mermaid first draft or text diagram sf-diagram-mermaid faster structural diagramming

object / field discovery for ERD sf-metadata accurate schema grounding

turn mockup into real component sf-lwc implementation after design

review Apex / trigger code in parallel sf-apex code-quality follow-up

Reference Map

Start here

Visual style / examples

Score Guide

Score Meaning

70+ strong image prompt / workflow choice

55–69 usable draft with iteration needed

40–54 partial alignment to request

< 40 poor fit; re-interview and rebuild prompt

Weekly Installs216Repositoryjaganpro/sf-skillsGitHub Stars205First SeenJan 22, 2026Security AuditsGen Agent Trust HubFailSocketPassSnykFailInstalled oncodex209opencode209gemini-cli207cursor207github-copilot204amp202

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统计数据

安装量355
评分4.6 / 5.0
版本1.0.0
更新日期2026年3月21日
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创建2026年3月21日
最后更新2026年3月21日