Multimodal Ui Flow Analyzer

Multimodal UI Flow Analyzer

This skill enables you to analyze static web app UI flows described via markdown + annotated screenshots and output agent-consumable knowledge for downstream AI code agents.

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When to Use This Skill

Activate this skill when:

• Processing UI documentation that includes annotated screenshots
• Converting visual UI flows into structured automation specs
• Generating test automation guidance from UI walkthroughs
• Creating machine-readable UI interaction sequences

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Core Workflow (7 Steps)

Step 1: Normalize Input Markdown

Before analysis, ensure each UI step follows this structure:

## Step N: 

**Intent:**
What the user is trying to accomplish.

**User Action (Text):**
Plain-language description of the interaction.

**Visual Reference:**

**Visual Annotations:**
- Box / arrow / highlight descriptions

If the input doesn't follow this format, restructure it first.

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Step 2: Apply Vision-Aware Analysis Rules

When analyzing screenshots:

1. Identify interactive UI elements (buttons, inputs, menus, links)
2. Map visual annotations (boxes, arrows, highlights) to UI elements
3. Infer user intent from both text and visual cues
4. Ignore decorative elements that are non-interactive
5. Assume static UI (no animations or runtime state changes)

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Step 3: Process Each Step Atomically

Analyze one step at a time, never the entire document at once.

For each step, extract:

• The UI element being interacted with
• Its visual characteristics and location
• Its technical role in the web application
• Preconditions and resulting state

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Step 4: Treat Annotations as Ground Truth

Annotation Priority Rules:

• Highlighted areas are authoritative targets
• Prefer annotated elements over textual ambiguity
• If text and image conflict, image evidence wins

Map annotations explicitly:

{
  "annotation_mapping": {
    "red_box": "Primary action button",
    "arrow": "Cursor movement direction",
    "highlight": "Target input field"
  }
}

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Step 5: Generate Structured Output

Produce output in the canonical format (see templates in assets/templates/).

Per-Step JSON Format:

{
  "step_id": "step-N",
  "intent": "Description of user goal",
  "action": "click|type|select|scroll|hover",
  "ui_element": {
    "type": "button|input|link|menu|dropdown",
    "label": "Visible text or aria-label",
    "visual_location": "Position description",
    "identification_strategy": [
      "visible text equals 'X'",
      "role=button",
      "data-testid='element-id'"
    ]
  },
  "precondition": "Required state before action",
  "resulting_state": "Expected state after action"
}

Flow Markdown Format:

# UI_FLOW: 

## Metadata
- App: 
- Flow Type: Static UI Interaction
- Source: Annotated screenshots + human-authored text

---

## Step 1
**Intent:** 

**Action:**
- type: 
- target:
  - role: 
  - text: ""
  - location: 

**Preconditions:**
- 

**Postconditions:**
- 

**Automation Notes:**
- 

---

Step 6: Add Automation Hints

For each step, include:

1. Stable DOM selectors (prefer semantic)

• role=button + visible text
• data-testid attributes
• aria-label values

1. Brittle selectors to avoid

• Pixel-based positions
• Absolute CSS selectors
• Dynamic class names

1. Wait conditions

• Elements to wait for before action
• Loading states to handle

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Step 7: Validate the Output

Before finalizing, verify:

• [ ] All steps have clear preconditions
• [ ] Step ordering is logical and complete
• [ ] No ambiguous UI references remain
• [ ] Each action has a defined resulting state
• [ ] Selectors are stable and semantic where possible

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Output Templates

Use templates from assets/templates/:

| Template | Purpose | |----------|---------| | step-output.json | Single step structured output | | flow-output.md | Complete flow specification | | automation-hints.md | Test automation guidance |

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Example Interaction

Input: User provides markdown with annotated screenshot showing a "Create Project" button highlighted with a red box.

Analysis Process:

1. Parse step structure from markdown
2. Identify red box annotation → maps to button element
3. Extract button text: "Create Project"
4. Determine location: "top-right of main content area"
5. Infer action type: click
6. Define precondition: "User is on Projects dashboard"
7. Define postcondition: "Project creation modal opens"

Output:

{
  "step_id": "step-2",
  "intent": "Create a new project",
  "action": "click",
  "ui_element": {
    "type": "button",
    "label": "Create Project",
    "visual_location": "top-right of main content area",
    "identification_strategy": [
      "visible text equals 'Create Project'",
      "role=button"
    ]
  },
  "precondition": "User is on Projects dashboard",
  "resulting_state": "Project creation modal opens"
}

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Edge Cases

Ambiguous Annotations

If multiple elements are highlighted, process them in visual reading order (top-to-bottom, left-to-right).

Missing Screenshots

If a step lacks a visual reference, flag it and proceed with text-only analysis. Note reduced confidence in output.

Complex Multi-Element Interactions

For drag-and-drop or multi-select, describe both source and target elements with separate identification strategies.

Dynamic Content

If the UI shows dynamic content (lists, tables), describe the interaction pattern rather than specific instances.

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Constraints

• DO NOT assume backend logic or API behavior
• DO NOT infer state beyond what's visible
• DO NOT generate pixel coordinates as primary selectors
• ALWAYS prefer semantic selectors over structural ones
• ALWAYS document uncertainty when present

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: boweneos
• Source: boweneos/ui-flow-agent-skills
• License: MIT

Install and usage instructions live in the source repository linked above.