generating-visual-diagrams: 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.

Scope

In scope:

PNG / SVG-style rendered image output
Visual ERDs and architecture diagrams
LWC or Experience Cloud mockups / wireframes
Image edits on previously generated visuals

Out of scope — delegate instead:

Mermaid or text-only diagrams → generating-mermaid-diagrams
Object / field metadata discovery for ERDs → generating-custom-object or generating-custom-field
LWC implementation after the mockup is approved → generating-lwc-components
Apex review / implementation → generating-apex

Hard Gate: Prerequisites First

Run the prerequisites check before using the skill:

bash

scripts/check-prerequisites.sh

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

references/gemini-cli-setup.md

Required Inputs

Ask for or infer before generating:

Input	Default if not provided
Image type	ERD
Subject scope and key entities / systems	Ask the user
Target quality	Draft (1K)
Preferred style	architect.salesforce.com aesthetic
Aspect ratio	Default (no override)
Quick mode or interview mode	Interview mode

Interview-First Workflow

Unless the user asks for quick / simple / just generate, ask clarifying questions first using the question bank in references/interview-questions.md.

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

Quick mode defaults (triggered by "quick", "simple", "just generate", "fast"):

professional style, 1K draft, legend included, one image first then iterate

Recommended Workflow

1. Run prerequisites check

Run

scripts/check-prerequisites.sh

and confirm all required tools pass before proceeding.

2. Gather inputs

object list / metadata (delegate to

generating-custom-object

generating-custom-field

if needed)

purpose: draft vs presentation vs documentation
desired aesthetic — read references/architect-aesthetic-guide.md for ERDs
aspect ratio / resolution

3. Run interview or use quick-mode defaults

Load references/interview-questions.md for the matching question set (ERD, LWC, architecture, code review).

4. Build a concrete prompt

Good prompts specify subject, composition, color treatment, labels/legends, and output quality goal.

5. Generate a fast draft at 1K

bash

gemini --yolo "/generate 'Your prompt here'"

Open the result and review layout before spending on higher resolution.

6. Iterate using edits

bash

gemini --yolo "/edit 'Specific change instruction'"

Use

/edit

for small adjustments — cheaper than regenerating. See references/iteration-workflow.md.

7. Generate final at 2K/4K using the Python script

Run

scripts/generate_image.py

when layout is confirmed:

bash

uv run scripts/generate_image.py -p "Refined prompt" -f "output.png" -r 4K

8. Error recovery

If
```
gemini --yolo
```
returns no image: re-run once; if it fails again, fall back to the Python script path.
If the Python script fails with
```
GEMINI_API_KEY not found
```
: verify the key is exported in your shell profile (
```
~/.zshrc
```
on macOS/zsh,
```
~/.bashrc
```
on Linux) and the terminal session is refreshed.
If the extension is missing: run
```
gemini extensions install nanobanana
```
and re-run the prerequisites check.

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

Full style specification: references/architect-aesthetic-guide.md

Common Patterns

Pattern	Default approach
visual ERD	get metadata if available, then render a draft first
LWC mockup	load assets/lwc/data-table.md, assets/lwc/record-form.md, or assets/lwc/dashboard-card.md for the matching template
architecture illustration	load assets/architecture/integration-flow.md; emphasize systems and flows
image refinement	use `/edit` for small changes before regenerating
final production asset	switch to script-driven 2K/4K generation via `scripts/generate_image.py`
Apex / LWC code review	load assets/review/apex-review.md or assets/review/lwc-review.md for the review prompt template

Output Expectations

Deliverables produced by this skill:

Draft image (
```
<name>.png
```
) — 1K resolution rendered via
```
gemini --yolo "/generate ..."
```
for layout review
Final image (
```
<name>.png
```
) — 2K or 4K resolution rendered via
```
scripts/generate_image.py
```
once composition is approved
Edit iteration (
```
<name>.png
```
) — incremental refinement via
```
gemini --yolo "/edit ..."
```
without full regeneration

After delivering each image:

Open the file in Preview or attach it in the session for multimodal review
Ask the user whether to iterate on layout, labeling, or color before finalizing
Only proceed to high-res output after draft composition is confirmed

Rules / Constraints

Rule	Rationale
Always run prerequisites check before any generation	Missing tools produce silent failures
Always draft at 1K before generating at 4K	Cost and time savings; composition changes at high res are wasteful
Use `/edit` for incremental changes, not full regeneration	Cheaper and faster for small adjustments
Never commit `GEMINI_API_KEY` to version control	Key is personal and tied to billing
Delegate text diagrams to `generating-mermaid-diagrams`	This skill owns rendered images only

Gotchas

Issue	Resolution
Edit not applying correctly	Be specific: reference existing elements by name; one change at a time
4K output looks different from 1K draft	Use exact same prompt text; minor variations are normal model behavior
`gemini --yolo` fails silently	Check that the Nano Banana extension is installed: `gemini extensions list`
Image dimensions wrong	Set `--aspect-ratio` explicitly in `scripts/generate_image.py` using `-a "16:9"`
RGBA image causes errors in Python script	Script auto-converts RGBA→RGB; ensure Pillow is installed via `uv`

Cross-Skill Integration

Need	Delegate to	Reason
Mermaid first draft or text diagram	generating-mermaid-diagrams	faster structural diagramming
Object / field discovery for ERD	generating-custom-object / generating-custom-field	accurate schema grounding
Turn mockup into real LWC component	generating-lwc-components	implementation after design
Apex review / implementation	generating-apex	code-quality follow-up

Reference File Index

File	When to read
references/gemini-cli-setup.md	Prerequisites fail — Gemini CLI / Nano Banana setup guidance
references/interview-questions.md	Step 3 — load question set matching the request type
references/iteration-workflow.md	Step 6 — draft-to-final iteration patterns and cost tips
references/architect-aesthetic-guide.md	Step 4 — ERD color palettes, box styles, prompt templates
references/examples-index.md	Step 4 — example prompts for ERD, LWC, architecture, code review
assets/erd/core-objects.md	Step 4 — prompt template for core CRM objects (Account, Contact, Opportunity, Case)
assets/erd/custom-objects.md	Step 4 — prompt template for custom object ERDs
assets/lwc/data-table.md	Step 4 — prompt template for lightning-datatable mockups
assets/lwc/record-form.md	Step 4 — prompt template for lightning-record-form mockups
assets/lwc/dashboard-card.md	Step 4 — prompt template for dashboard card / metric tile mockups
assets/architecture/integration-flow.md	Step 4 — prompt template for integration architecture diagrams
assets/review/apex-review.md	Step 4 — Gemini review prompt template for Apex code
assets/review/lwc-review.md	Step 4 — Gemini review prompt template for LWC components
scripts/check-prerequisites.sh	Step 1 — run to verify all required tools are installed
scripts/generate_image.py	Step 7 — run for 2K/4K resolution output and image editing with resolution control

generating-visual-diagrams

NPX Install

Tags

SKILL.md Content

generating-visual-diagrams: Salesforce Visual AI Skill

Scope

Hard Gate: Prerequisites First

Required Inputs

Interview-First Workflow

Recommended Workflow

1. Run prerequisites check

2. Gather inputs

3. Run interview or use quick-mode defaults

4. Build a concrete prompt

5. Generate a fast draft at 1K

6. Iterate using edits

7. Generate final at 2K/4K using the Python script

8. Error recovery

Default Style Guidance

Common Patterns

Output Expectations

Rules / Constraints

Gotchas

Cross-Skill Integration

Reference File Index