Post Resolution search visualizations

[ngoiyaeric]

I understand you're asking about making the resolution search visualizations more contextual to user needs and queries. Currently, the resolution search captures map images and displays them in a carousel, but the response doesn't clearly indicate what the analysis is focusing on or how it relates to the user's specific context. 1 2

Current State

The resolution search system captures map images (Mapbox and/or Google) and analyzes them with the resolutionSearch agent, which includes context like:

• User-drawn features on the map 3
• Geographic coordinates and location name 4
• Recent news for the location 5
• Temporal context (local time) 6

However, the visualizations displayed in the ResolutionCarousel use generic labels like "Mapbox Capture" or "Google Capture" without explicitly referencing what the analysis is focusing on. 7

Making Visualizations More Contextual

To address this, you could enhance the system prompt in resolutionSearch to make the summary more explicitly contextual about what it's analyzing:

// In lib/agents/resolution-search.tsx, enhance the system prompt to include:
${drawnFeatures && drawnFeatures.length > 0 ? `**User-Drawn Features:**
The user has drawn the following features on the map for your reference:
${drawnFeatures.map(f => `- ${f.type} (${f.measurement}): ${JSON.stringify(f.geometry)}`).join('\n')}
Use these user-drawn areas/lines as primary areas of interest for your analysis.
IMPORTANT: In your summary, explicitly state what features you are analyzing and why.` : ''}

Additionally, you could modify the ResolutionCarousel to accept context-aware labels based on the analysis results, rather than static labels. 1

The response schema already supports contextual information through the summary field which should include land feature classification, points of interest, and relevant context. 8

Notes

The current implementation does include contextual information in the analysis (drawn features, location, news, time), but the visual presentation in the carousel doesn't explicitly surface this context to users. The enhancement would focus on making the summary text more explicit about what is being analyzed and potentially updating the carousel labels to reflect the analysis focus.

Wiki pages you might want to explore:

• AI Agent Orchestration (QueueLab/QCX)
• Resolution Search and Image Analysis (QueueLab/QCX)
• Submit Action and Tool Orchestration (QueueLab/QCX)

Citations

File: components/resolution-carousel.tsx (L110-151)

 return (
 <div className="w-full max-w-lg mx-auto my-4 px-2">
 <Carousel className="w-full">
 <CarouselContent>
 {slides.map((slide, index) => (
 <CarouselItem key={index}>
 <div className="flex flex-col items-center p-1">
 {slide.type === 'compare' ? (
 <CompareSlider leftImage={slide.left} rightImage={slide.right} className="w-full" />
 ) : (
 <>
 <ResolutionImage src={slide.src} className="mb-0 mt-0 w-full" />
 {slide.showAnalysis && (
 <Button
 variant="default"
 size="sm"
 className="mt-2 w-full font-bold py-4 h-auto text-xs uppercase tracking-widest"
 onClick={handleQCXAnalysis}
 disabled={isAnalyzing}
 >
 {isAnalyzing ? 'ANALYZING...' : 'QCX-TERRA ANALYSIS'}
 </Button>
 )}
 <div className="mt-1 text-[10px] text-muted-foreground uppercase tracking-widest">
 {slide.label}
 </div>
 </>
 )}
 </div>
 </CarouselItem>
 ))}
 </CarouselContent>
 {slides.length > 1 && (
 <div className="flex justify-center gap-4 mt-2">
 <CarouselPrevious className="static translate-y-0" />
 <CarouselNext className="static translate-y-0" />
 </div>
 )}
 </Carousel>
 </div>
 )
}

File: lib/agents/resolution-search.tsx (L101-135)

 const systemPrompt = `
As a geospatial analyst, your task is to analyze the provided satellite image of a geographic location.

**Temporal Context:**
The current local time at this location is ${localTime} (timezone: ${timezone}).
This temporal information is important for understanding the current state and any time-sensitive features visible in the image.

${location ? `**Geographic Coordinates:**
The coordinates provided for this image are: Latitude ${location.lat}, Longitude ${location.lng}.
Location: ${locationName}` : ''}

${newsContext ? `**Recent Context:**
${newsContext}

Please incorporate this recent news context into your analysis where relevant.` : ''}

${drawnFeatures && drawnFeatures.length > 0 ? `**User-Drawn Features:**
The user has drawn the following features on the map for your reference:
${drawnFeatures.map(f => `- ${f.type} (${f.measurement}): ${JSON.stringify(f.geometry)}`).join('\n')}
Use these user-drawn areas/lines as primary areas of interest for your analysis.` : ''}

**Analysis Requirements:**

1. **Land Feature Classification:** Identify and describe the different types of land cover visible in the image (e.g., urban areas, forests, water bodies, agricultural fields).
2. **Points of Interest (POI):** Detect and name any significant landmarks, infrastructure (e.g., bridges, major roads), or notable buildings.
3. **Temporal Analysis:** Consider how the time of day and season might affect what's visible in the image.
4. **Coordinate Extraction:** If possible, confirm or refine the geocoordinates (latitude/longitude) of the center of the image.
5. **COG Applicability:** Determine if this location would benefit from Cloud Optimized GeoTIFF (COG) analysis for high-precision temporal or spectral data.
6. **News Integration:** Reference any recent news or events that may be relevant to the current state of the location.
7. **Structured Output:** Return your findings in a structured JSON format including summary, geoJson (if any), news context, and any extracted coordinates or COG information. Use the provided schema.

Your analysis should be based on the visual information in the image, the temporal context provided, and your general knowledge. Do not attempt to access external websites or perform web searches beyond what has been provided.

Analyze the user's prompt and the image to provide a holistic understanding of the location with full temporal and contextual awareness.
`;

File: lib/schema/resolution-search.ts (L8-10)

export const resolutionSearchSchema = z.object({
 summary: z.string().describe('A detailed text summary of the analysis, including land feature classification, points of interest, relevant current news, and temporal context.'),

To implement contextual visualizations for resolution search, you need to modify the data flow from the AI agent through to the UI components. Here's a step-by-step analysis:

Implementation Steps

Step 1: Update the Schema to Include Contextual Labels

Add fields to resolutionSearchSchema in lib/schema/resolution-search.ts to store contextual labels for each image type:

export const resolutionSearchSchema = z.object({
 summary: z.string().describe('A detailed text summary of the analysis, including land feature classification, points of interest, relevant current news, and temporal context.'),
 // Add these new fields:
 mapboxImageLabel: z.string().optional().describe('A contextual label describing what the Mapbox image shows, based on the analysis focus.'),
 googleImageLabel: z.string().optional().describe('A contextual label describing what the Google Satellite image shows, based on the analysis focus.'),
 analysisFocus: z.string().optional().describe('A brief phrase describing the primary focus of the analysis (e.g., "Urban infrastructure analysis", "Forest coverage assessment").'),
 // ... existing fields
})

Step 2: Enhance the System Prompt

Modify the system prompt in lib/agents/resolution-search.tsx to instruct the AI to generate contextual labels. Add this to the Analysis Requirements section:

**Analysis Requirements:**

// ... existing requirements 1-7

8. **Contextual Labeling:** Generate descriptive labels for the map images that reflect the analysis focus. If user-drawn features are present, reference them in the labels. For example:
 - If analyzing a drawn polygon: "Analysis of drawn area: [feature type]"
 - If analyzing a specific location: "[Location name] satellite view"
 - If general analysis: "[Primary feature type] analysis"
 Provide these in the mapboxImageLabel, googleImageLabel, and analysisFocus fields.

Step 3: Pass Labels Through AI State

In app/actions.tsx, the analysis result is stored in the AI state at lines 191-199. The new schema fields will automatically be included in the analysisResult object, so no changes are needed here—the data will flow through automatically.

Step 4: Update ResolutionCarousel Props

Modify the ResolutionCarouselProps interface in components/resolution-carousel.tsx to accept the contextual labels:

interface ResolutionCarouselProps {
 mapboxImage?: string | null
 googleImage?: string | null
 initialImage?: string | null
 // Add these new props:
 mapboxImageLabel?: string
 googleImageLabel?: string
 analysisFocus?: string
}

Step 5: Update Carousel Label Logic

Replace the static label generation in components/resolution-carousel.tsx (lines 78-84) with dynamic labels:

// Individual slides
if (mapboxImage) slides.push({ 
 type: 'image', 
 src: mapboxImage, 
 showAnalysis: false, 
 label: mapboxImageLabel || '

Wiki pages you might want to explore:
- [Resolution Search and Image Analysis (QueueLab/QCX)](/wiki/QueueLab/QCX#2.6)
- [Glossary (QueueLab/QCX)](/wiki/QueueLab/QCX#7)

[ngoiyaeric]

@coderabbitai plan

[coderabbitai]

Coding Plan

Summary

• Extend the AI schema with contextual label fields for carousel images and enhanced semantic fields for GeoJSON features
• Update the system prompt to guide the AI in creating location-specific labels and semantically meaningful map annotations tied to analysis findings
• Update UI components to accept label props with fallbacks to current hardcoded values
• Enhance summary generation to explicitly reference map annotations, creating clear connection between text and visuals
• Leverage detected features to generate more contextual related query suggestions

Design Choices

Design Choice 1: Should labels be dynamically updated during live streaming or only on history replay?

Options Considered:

1. Make labels streamable values (like summaryStream) that update in real-time
2. Accept fallback labels during live streaming, show contextual labels on history replay

Chosen Option: 2

Rationale: Use fallback labels during live streaming and contextual labels on replay—this is simpler, matches existing patterns, and provides acceptable UX since label changes during streaming would be minimally noticeable.

Design Choice 2: Should GeoJSON annotations appear during streaming or only after analysis completes?

Options Considered:

1. Stream GeoJSON features incrementally as they're generated
2. Keep current pattern: append GeoJsonLayer after analysis completes

Chosen Option: 2

Rationale: Keep the current pattern—annotations appear once analysis completes. This is consistent with existing behavior and avoids complexity of streaming partial GeoJSON.

💡 User Tips

Regenerate the plan with different choices with @coderabbitai <feedback>.

Implementation Steps

Phase 1: Schema and Agent Enhancement for Carousel Labels

Extend the resolution search schema to include AI-generated contextual labels and update the system prompt to instruct the model to produce them.

Task 1: Add Contextual Label Fields to Schema

Add optional string fields to the resolution search schema that the AI will populate with contextual descriptions of what each image shows.

• Update lib/schema/resolution-search.ts to add three new optional fields: mapboxImageLabel, googleImageLabel, and analysisFocus
• Each field should have a .describe() annotation guiding the AI on expected content (e.g., location-specific labels, feature descriptions)
• Keep fields optional to maintain backward compatibility with existing data

Task 2: Update System Prompt to Generate Labels

Modify the agent's system prompt to instruct the AI to generate meaningful contextual labels based on the analysis.

• Update lib/agents/resolution-search.tsx system prompt to add a new requirement (item 8) for contextual labeling
• Instruct the AI to reference user-drawn features in labels when present
• Provide examples: "Analysis of drawn area: [feature type]", "[Location name] satellite view"
• Specify that labels should be concise, uppercase-friendly, and descriptive of the analysis focus

🤖 Prompt for AI agents

Extend the resolution search schema and agent system prompt to support
AI-generated contextual labels. The goal is to have the AI model produce
location- and feature-aware labels as part of its structured output.

**`lib/schema/resolution-search.ts`**
- Add three new optional string fields to `resolutionSearchSchema`:
- `mapboxImageLabel`: annotated with `.describe()` to guide the AI to produce a
location-specific label for the Mapbox image
- `googleImageLabel`: annotated with `.describe()` to guide the AI to produce a
location-specific label for the Google Satellite image
- `analysisFocus`: annotated with `.describe()` to guide the AI to describe the
overall analysis focus or drawn feature
- Keep all three fields optional to maintain backward compatibility with
existing stored data

**`lib/agents/resolution-search.tsx`**
- Add a new requirement (item 8) to the system prompt for contextual labeling
- Instruct the AI to reference user-drawn features in labels when present
- Provide examples in the prompt: "Analysis of drawn area: [feature type]",
"[Location name] satellite view"
- Specify that labels should be concise, uppercase-friendly, and descriptive of
the analysis focus

Phase 2: Data Flow and Component Updates for Carousel Labels

Pass the AI-generated labels through the action layer to the UI components and update components to use dynamic labels with appropriate fallbacks.

Task 1: Update ResolutionCarousel Props and Label Logic

Modify the carousel component to accept optional label props and use them in slide generation with fallbacks to current hardcoded values.

• Extend ResolutionCarouselProps interface in components/resolution-carousel.tsx to include mapboxImageLabel, googleImageLabel, and analysisFocus as optional strings
• Update slide generation logic (around lines 78-84) to use the prop values when available, falling back to 'MAPBOX', 'GOOGLE SATELLITE', and 'MAP CAPTURE' respectively
• The fallback ensures the component works correctly during live streaming when labels aren't yet available

Task 2: Update CompareSlider to Accept Label Props

Replace hardcoded labels in the compare slider with configurable props.

• Add leftLabel and rightLabel optional props to CompareSlider in components/compare-slider.tsx
• Update the absolute-positioned label divs (lines 83-88) to use these props with fallbacks to 'MAPBOX' and 'GOOGLE SATELLITE'
• Update the call site in ResolutionCarousel to pass the appropriate label props to CompareSlider

Task 3: Pass Labels Through Actions Layer

Update the action layer to include labels in stored AI state and pass them to the carousel on history replay.

• In app/actions.tsx, update the resolution_search_result JSON storage (around lines 191-199) to include the new label fields from analysisResult
• Update getUIStateFromAIState case for resolution_search_result (lines 819-839) to extract the label fields from parsed JSON and pass them as props to ResolutionCarousel
• For the live streaming path (lines 225-234), pass undefined for label props (fallbacks will be used since labels aren't available yet)

🤖 Prompt for AI agents

Wire the AI-generated labels from the action layer through to the UI components,
updating components to accept dynamic label props with appropriate fallbacks.
The goal is for contextual labels to appear on history replay while live
streaming continues to use static fallback labels.

**`components/resolution-carousel.tsx`**
- Extend the `ResolutionCarouselProps` interface to include three new optional
string fields: `mapboxImageLabel`, `googleImageLabel`, and `analysisFocus`
- In the slide generation logic (around lines 78-84), use the prop values when
available, falling back to `'MAPBOX'`, `'GOOGLE SATELLITE'`, and `'MAP CAPTURE'`
respectively
- Pass the appropriate label props down to `CompareSlider`

**`components/compare-slider.tsx`**
- Add `leftLabel` and `rightLabel` as optional string props
- Update the absolute-positioned label divs (lines 83-88) to render these props
with fallbacks to `'MAPBOX'` and `'GOOGLE SATELLITE'`

**`app/actions.tsx`**
- In the `resolution_search_result` JSON storage block (around lines 191-199),
include `mapboxImageLabel`, `googleImageLabel`, and `analysisFocus` from
`analysisResult`
- In `getUIStateFromAIState` for the `resolution_search_result` case (lines
819-839), extract the three label fields from parsed JSON and pass them as props
to `ResolutionCarousel`
- In the live streaming path (lines 225-234), pass `undefined` for all three
label props so that the component fallbacks are used

Phase 3: Map Annotation Contextualization

Make bounding boxes and red dot markers semantically tied to analysis findings (POIs, land features, drawn areas) by enhancing the GeoJSON schema and updating the AI prompt to generate meaningful, referenced annotations.

Task 1: Enhance GeoJSON Feature Schema Structure

Extend the geoJson features within the schema to include richer semantic fields that categorize and label each annotation.

• Update lib/schema/resolution-search.ts to add new optional fields to each feature object in the geoJson.features array: featureCategory (enum: 'poi', 'land_feature', 'infrastructure', 'drawn_area', 'other') and displayLabel (short string for map tooltip/display)
• Keep existing name and description fields for backward compatibility
• Add .describe() annotations to guide the AI on when to use each category

Task 2: Update System Prompt for Semantic Map Annotations

Modify the agent's system prompt to instruct it to generate GeoJSON features that correspond to identified analysis findings.

• Update lib/agents/resolution-search.tsx system prompt to add a new requirement (item 9) for semantic annotations
• Instruct the AI to create Point features for POIs and landmarks, Polygon features for land areas and regions of interest
• Specify that each feature must have a name, featureCategory, and displayLabel that matches findings described in the summary
• Provide examples: "Create a Point for identified bridges with name 'Main Street Bridge', category 'infrastructure'"

Task 3: Update Summary to Reference Map Annotations

Modify the system prompt to instruct the AI to explicitly reference generated annotations in the summary text, creating semantic connection between text and map visuals.

• Update lib/agents/resolution-search.tsx system prompt to add a sub-requirement under the summary section
• Instruct the AI to reference annotated features by name in the summary (e.g., "I've marked the identified commercial district as a polygon" or "The red marker indicates the location of...")
• This helps users understand what each visual annotation represents without needing tooltips

Task 4: Generate Feature-Based Related Query Suggestions

Enhance the related query generation to consider detected annotated features, providing contextual follow-up suggestions.

• In app/actions.tsx, extract feature names and categories from the reconstructed geoJson after analysis completes
• Pass a summary of detected features (e.g., "Detected: Main Street Bridge (infrastructure), Central Park (land_feature)") to querySuggestor as additional context
• Update lib/agents/query-suggestor.tsx to accept optional detectedFeatures context and incorporate it into the prompt for generating more relevant follow-up queries like "Tell me more about Main Street Bridge" or "What is the history of Central Park?"

🤖 Prompt for AI agents

Enhance GeoJSON map annotations to be semantically meaningful and tied to
analysis findings, and leverage detected features to improve related query
suggestions. The goal is to make map annotations contextually informative and
create a coherent connection between text summaries and visual map elements.

**`lib/schema/resolution-search.ts`**
- Add two new optional fields to each feature object in the `geoJson.features`
array:
- `featureCategory`: an enum field with values `'poi'`, `'land_feature'`,
`'infrastructure'`, `'drawn_area'`, `'other'`, with `.describe()` annotations
guiding the AI on when to use each category
- `displayLabel`: a short optional string for map tooltip/display, with a
`.describe()` annotation
- Keep existing `name` and `description` fields unchanged for backward
compatibility

**`lib/agents/resolution-search.tsx`**
- Add a new requirement (item 9) to the system prompt for semantic annotations:
- Instruct the AI to create Point features for POIs and landmarks, and Polygon
features for land areas and regions of interest
- Specify that each feature must have a `name`, `featureCategory`, and
`displayLabel` matching findings in the summary
- Provide an example: "Create a Point for identified bridges with name 'Main
Street Bridge', category 'infrastructure'"
- Add a sub-requirement under the summary section instructing the AI to
reference annotated features by name in the summary text (e.g., "I've marked the
identified commercial district as a polygon", "The red marker indicates the
location of...")

**`app/actions.tsx`**
- After analysis completes and `geoJson` is reconstructed, extract feature names
and categories from the features array
- Build a detected features summary string (e.g., "Detected: Main Street Bridge
(infrastructure), Central Park (land_feature)")
- Pass this summary as additional context to `querySuggestor`

**`lib/agents/query-suggestor.tsx`**
- Accept an optional `detectedFeatures` context parameter
- Incorporate `detectedFeatures` into the prompt to generate more contextual
follow-up queries such as "Tell me more about Main Street Bridge" or "What is
the history of Central Park?"

Research

The resolution search system captures map images and analyzes them via the resolutionSearch agent in lib/agents/resolution-search.tsx. The agent returns structured data conforming to resolutionSearchSchema in lib/schema/resolution-search.ts. Results flow through app/actions.tsx which stores them in AI state and renders them via ResolutionCarousel and GeoJsonLayer. The carousel builds slides from image props and currently uses hardcoded labels ('MAPBOX', 'GOOGLE SATELLITE', 'MAP CAPTURE'). The CompareSlider component also has hardcoded labels in its JSX. GeoJsonLayer renders Polygon features as teal bounding boxes and Point features as red dot markers using the Mapbox GL API. The schema's geoJson field contains features with name and description properties that are reconstructed into standard GeoJSON in actions.tsx. There are two render paths: a live streaming path that fires immediately (before AI completes) and a history replay path that reconstructs UI from stored AI state.

---

🚀 Next Steps

🤖 All AI agent prompts combined

Task: 1

Extend the resolution search schema and agent system prompt to support
AI-generated contextual labels. The goal is to have the AI model produce
location- and feature-aware labels as part of its structured output.

**`lib/schema/resolution-search.ts`**
- Add three new optional string fields to `resolutionSearchSchema`:
- `mapboxImageLabel`: annotated with `.describe()` to guide the AI to produce a
location-specific label for the Mapbox image
- `googleImageLabel`: annotated with `.describe()` to guide the AI to produce a
location-specific label for the Google Satellite image
- `analysisFocus`: annotated with `.describe()` to guide the AI to describe the
overall analysis focus or drawn feature
- Keep all three fields optional to maintain backward compatibility with
existing stored data

**`lib/agents/resolution-search.tsx`**
- Add a new requirement (item 8) to the system prompt for contextual labeling
- Instruct the AI to reference user-drawn features in labels when present
- Provide examples in the prompt: "Analysis of drawn area: [feature type]",
"[Location name] satellite view"
- Specify that labels should be concise, uppercase-friendly, and descriptive of
the analysis focus
===============================================================================

Task: 2

Wire the AI-generated labels from the action layer through to the UI components,
updating components to accept dynamic label props with appropriate fallbacks.
The goal is for contextual labels to appear on history replay while live
streaming continues to use static fallback labels.

**`components/resolution-carousel.tsx`**
- Extend the `ResolutionCarouselProps` interface to include three new optional
string fields: `mapboxImageLabel`, `googleImageLabel`, and `analysisFocus`
- In the slide generation logic (around lines 78-84), use the prop values when
available, falling back to `'MAPBOX'`, `'GOOGLE SATELLITE'`, and `'MAP CAPTURE'`
respectively
- Pass the appropriate label props down to `CompareSlider`

**`components/compare-slider.tsx`**
- Add `leftLabel` and `rightLabel` as optional string props
- Update the absolute-positioned label divs (lines 83-88) to render these props
with fallbacks to `'MAPBOX'` and `'GOOGLE SATELLITE'`

**`app/actions.tsx`**
- In the `resolution_search_result` JSON storage block (around lines 191-199),
include `mapboxImageLabel`, `googleImageLabel`, and `analysisFocus` from
`analysisResult`
- In `getUIStateFromAIState` for the `resolution_search_result` case (lines
819-839), extract the three label fields from parsed JSON and pass them as props
to `ResolutionCarousel`
- In the live streaming path (lines 225-234), pass `undefined` for all three
label props so that the component fallbacks are used
===============================================================================

Task: 3

Enhance GeoJSON map annotations to be semantically meaningful and tied to
analysis findings, and leverage detected features to improve related query
suggestions. The goal is to make map annotations contextually informative and
create a coherent connection between text summaries and visual map elements.

**`lib/schema/resolution-search.ts`**
- Add two new optional fields to each feature object in the `geoJson.features`
array:
- `featureCategory`: an enum field with values `'poi'`, `'land_feature'`,
`'infrastructure'`, `'drawn_area'`, `'other'`, with `.describe()` annotations
guiding the AI on when to use each category
- `displayLabel`: a short optional string for map tooltip/display, with a
`.describe()` annotation
- Keep existing `name` and `description` fields unchanged for backward
compatibility

**`lib/agents/resolution-search.tsx`**
- Add a new requirement (item 9) to the system prompt for semantic annotations:
- Instruct the AI to create Point features for POIs and landmarks, and Polygon
features for land areas and regions of interest
- Specify that each feature must have a `name`, `featureCategory`, and
`displayLabel` matching findings in the summary
- Provide an example: "Create a Point for identified bridges with name 'Main
Street Bridge', category 'infrastructure'"
- Add a sub-requirement under the summary section instructing the AI to
reference annotated features by name in the summary text (e.g., "I've marked the
identified commercial district as a polygon", "The red marker indicates the
location of...")

**`app/actions.tsx`**
- After analysis completes and `geoJson` is reconstructed, extract feature names
and categories from the features array
- Build a detected features summary string (e.g., "Detected: Main Street Bridge
(infrastructure), Central Park (land_feature)")
- Pass this summary as additional context to `querySuggestor`

**`lib/agents/query-suggestor.tsx`**
- Accept an optional `detectedFeatures` context parameter
- Incorporate `detectedFeatures` into the prompt to generate more contextual
follow-up queries such as "Tell me more about Main Street Bridge" or "What is
the history of Central Park?"

💡 Iterate on the plan with: @coderabbitai <feedback>

Example Feedback
- @coderabbitai You can skip phase 3. Add a simple unit test case for phase 2.
- @coderabbitai For design choice 1 go ahead with option 3 and replan.

---

💬 Have feedback or questions? Drop into our discord!

[ngoiyaeric]

@coderabbitai plan after the resolution search the model draws boxes and puts red dots like this on the map. This should be contextual and perhaps addressed in the response or the related queries

[coderabbitai]

I've updated the plan above based on your feedback. Ready to proceed when you are!