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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:
- Mermaid or text-only diagrams → sf-diagram-mermaid
- metadata discovery for ERDs → sf-metadata
- LWC implementation after the mockup → sf-lwc
- Apex review / implementation → sf-apex
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 Installs
242
Repository
jaganpro/sf-skills
GitHub Stars
219
First Seen
Jan 22, 2026
Security Audits
Gen Agent Trust HubPassSocketPassSnykPass
Installed on
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