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+---
+name: serp-ads
+description: >
+  Specialized skill for Google Ads intelligence workflows via SerpApi — competitor ad research,
+  advertiser discovery, ad creative analysis, ad format tracking, political ad monitoring,
+  platform-specific ad filtering, and ad timeline analysis. Use when: (1) researching a
+  competitor's Google Ads campaigns or ad history, (2) finding all ads run by a specific
+  advertiser or domain, (3) analyzing ad creatives (text, image, video) for messaging and
+  strategy, (4) tracking when and where ads were shown (dates, regions, platforms),
+  (5) monitoring political advertising by region, (6) comparing ad strategies across
+  competitors, (7) finding ads on specific platforms (YouTube, Search, Shopping, Maps, Play),
+  (8) investigating ad creative formats and messaging patterns, (9) any task involving
+  the Google Ads Transparency Center. This skill builds on the foundational serpapi skill
+  for all API details.
+metadata: {"openclaw": {"emoji": "📢", "requires": {"env": ["SERPAPI_KEY"]}, "primaryEnv": "SERPAPI_KEY"}}
+---
+
+# Google Ads Intelligence Workflows
+
+Competitor ad research, creative analysis, timeline tracking, and political ad monitoring via SerpApi's Google Ads Transparency Center engines. This skill covers workflow logic — load the **serpapi** foundational skill for API details, parameters, and wrapper script usage.
+
+## Dependencies
+
+This skill builds on:
+- **serpapi** — SerpApi wrapper script and full API reference (engines: `google_ads_transparency_center`, `google_ads_transparency_center_ad_details`)
+
+## Core Concepts
+
+### Engines
+
+| Engine | Purpose | Key Inputs |
+|--------|---------|------------|
+| `google_ads_transparency_center` | List ads by advertiser or domain | `advertiser_id` or `text` (domain/name) |
+| `google_ads_transparency_center_ad_details` | Detailed info for a specific ad creative | `advertiser_id` + `creative_id` |
+
+### Finding Advertisers
+
+Advertisers are identified by:
+- **Advertiser ID** — format `AR` + digits (e.g., `AR17828074650563772417` for Tesla Inc.)
+- **Domain search** — pass the domain as the `text` parameter (e.g., `text=apple.com`)
+
+The `text` parameter is the easiest way to discover an advertiser — use whatever you'd type into the Google Ads Transparency Center search bar (domain, brand name, etc.).
+
+### Ad Creative Results
+
+Each ad creative in the listing response includes:
+- `advertiser_id` / `advertiser` — Advertiser info
+- `ad_creative_id` — Unique ID (format `CR` + digits), used for fetching details
+- `format` — `text`, `image`, or `video`
+- `image` — Screenshot/preview of the ad
+- `width` / `height` — Creative dimensions
+- `first_shown` / `last_shown` — Unix timestamps for when the ad was active
+- `details_link` — Google's transparency page for this ad
+- `serpapi_details_link` — Direct SerpApi link to fetch details
+
+### Ad Detail Results
+
+The details endpoint returns richer data per creative:
+
+**For text ads:**
+- `title`, `headline`, `long_headline` — Ad copy elements
+- `snippet` — Ad description text
+- `visible_link` / `link` — Display URL and destination URL
+- `sitelink_texts` / `sitelink_descriptions` — Sitelink extensions
+- `advertiser_logo` / `advertiser_logo_alt` — Branding
+
+**For image ads:**
+- `image` — Full creative image URL
+- `call_to_action` — CTA button text
+- `snippet` — Ad text overlay
+- `link` — Destination URL
+- `carousel_data[]` — If carousel format: images, headlines, button links
+- `images[]` — Multiple image variants with tags
+
+**For video ads:**
+- `video_link` — Video URL (often YouTube-hosted)
+- `raw_video_link` — Direct video file link (when available)
+- `video_duration` — Duration string
+- `headline`, `call_to_action`, `snippet` — Ad copy
+- `channel_name` / `channel_icon` — YouTube channel info
+
+**Common detail fields:**
+- `rating` / `reviews` / `reviews_link` — If product-related
+- `address` — For local/store ads
+- `is_verified` — Advertiser verification status
+- `extensions[]` — Additional ad extensions
+
+**Search information (from details):**
+- `format` — Creative type
+- `last_shown` — Most recent display timestamp
+- `region_name` — Target region
+- `ad_funded_by` — Funding entity (especially for political ads)
+- `regions[]` — Full list of regions where ad ran, with `first_shown`, `last_shown`, and `times_shown`
+
+### Pagination
+
+The listing endpoint returns up to `num` results (default 40). Paginate with `next_page_token` from the response. Each page costs one search credit.
+
+### Filters
+
+| Filter | Parameter | Values |
+|--------|-----------|--------|
+| Platform | `platform` | `SEARCH`, `YOUTUBE`, `SHOPPING`, `MAPS`, `PLAY` |
+| Format | `creative_format` | `text`, `image`, `video` |
+| Region | `region` | Region code (see Google Ads Transparency regions) |
+| Date range | `start_date` / `end_date` | `YYYYMMDD` format |
+| Political | `political_ads` | `true` (requires `region`) |
+
+## Workflows
+
+### 1. Competitor Ad Research
+
+Discover what ads a competitor is running and how they position themselves.
+
+**Step 1: Find the advertiser.**
+Use the **serpapi** skill with `google_ads_transparency_center` engine and `text` set to the competitor's domain (e.g., `text=competitor.com`).
+
+**Step 2: Browse their ad catalog.**
+Results return `ad_creatives[]` — scan `format`, `first_shown`/`last_shown` timestamps, and the ad preview `image`. Note which creatives are currently active (recent `last_shown`).
+
+**Step 3: Analyze key creatives.**
+For interesting ads, use `google_ads_transparency_center_ad_details` with the `advertiser_id` + `creative_id` to get full creative details — headlines, descriptions, CTAs, sitelinks, and destination URLs.
+
+**What to extract:**
+- **Messaging themes** — What value propositions do they lead with?
+- **CTAs** — What actions do they push? (Shop now, Learn more, Get started)
+- **Landing pages** — Where do ads link? (Product pages, landing pages, blog posts)
+- **Sitelink strategy** — What additional links do they promote?
+- **Format mix** — Ratio of text vs image vs video ads
+- **Active period** — How long do they run ads? Seasonal patterns?
+
+**Presentation:**
+
+```
+📢 Ad Intelligence: [Competitor]
+Advertiser: [name] (ID: [advertiser_id])
+Total ads found: [count]
+
+🔤 Text Ads ([count]):
+• "[headline]" — [snippet excerpt]
+  CTA: [call_to_action] | 🔗 [visible_link]
+  Active: [first_shown] → [last_shown]
+
+🖼️ Image Ads ([count]):
+• [call_to_action] — [snippet excerpt]
+  📐 [width]×[height] | 🔗 [link]
+
+🎬 Video Ads ([count]):
+• "[headline]" — [video_duration]
+  CTA: [call_to_action] | 🔗 [link]
+
+💡 Key Insights:
+- [Messaging patterns observed]
+- [CTA preferences]
+- [Landing page strategy]
+```
+
+### 2. Ad Creative Deep Dive
+
+When the user wants detailed analysis of specific ad creatives.
+
+**Step 1: Get the creative listing** (if not already done).
+Search by advertiser ID or domain using `google_ads_transparency_center`.
+
+**Step 2: Fetch details for target creatives.**
+Use `google_ads_transparency_center_ad_details` with `advertiser_id` + `creative_id`.
+
+**Step 3: Analyze the creative.**
+
+For **text ads**, focus on:
+- Headline progression (title → headline → long_headline)
+- Description/snippet copywriting
+- Sitelink strategy and what pages they promote
+- URL structure (vanity URLs, UTM patterns)
+
+For **image ads**, focus on:
+- Visual style and branding consistency
+- CTA placement and text
+- If carousel: how many cards, what progression/story
+- Image dimensions and aspect ratios (indicates platform targeting)
+
+For **video ads**, focus on:
+- Video duration (6s bumper, 15s, 30s, long-form)
+- Channel association (branded YouTube channel?)
+- CTA overlay text
+- Whether it's a repurposed organic video or ad-specific
+
+### 3. Platform-Specific Analysis
+
+Understand how an advertiser targets different Google platforms.
+
+**Strategy:** Run the listing search multiple times with different `platform` filters:
+- `SEARCH` — Google Search text ads
+- `YOUTUBE` — Video ads and display on YouTube
+- `SHOPPING` — Product listing ads
+- `MAPS` — Local/map ads
+- `PLAY` — App install ads
+
+**What to compare:**
+- Which platforms they invest in most (ad count per platform)
+- How messaging differs across platforms
+- Format preferences per platform (text on Search, video on YouTube)
+- Whether they use platform-specific features (Shopping product feeds, Maps local info)
+
+**Presentation:**
+
+```
+📊 Platform Breakdown: [Advertiser]
+
+🔍 Google Search: [count] ads
+   Formats: [text: X, image: Y]
+   Top themes: [themes]
+
+▶️ YouTube: [count] ads
+   Formats: [video: X, image: Y]
+   Avg duration: [duration]
+
+🛒 Shopping: [count] ads
+🗺️ Maps: [count] ads
+📱 Play: [count] ads
+
+💡 Platform strategy: [summary]
+```
+
+### 4. Ad Timeline Analysis
+
+Track how an advertiser's ad strategy evolves over time.
+
+**Step 1: Get the full ad listing** for the advertiser.
+
+**Step 2: Analyze timestamps.**
+Each creative has `first_shown` and `last_shown` (Unix timestamps). Convert to dates and map out:
+- Currently active ads (recent `last_shown`)
+- Campaign duration patterns (how long each ad runs)
+- Launch clusters (multiple ads starting around the same date = new campaign)
+- Seasonal patterns (holiday campaigns, product launches)
+
+**Step 3: For deeper timeline data**, fetch details on key creatives.
+The detail response includes `regions[]` with per-region `first_shown`/`last_shown`/`times_shown` — showing geographic rollout patterns.
+
+**Step 4: Use date-range filters** to focus on specific periods.
+Pass `start_date` and `end_date` (YYYYMMDD) to narrow results to a time window. For today's ads only, set `end_date` = `start_date` + 1 day.
+
+**What to look for:**
+- Campaign cycles — How often do they refresh creatives?
+- Geographic expansion — Did they start in one region and expand?
+- Seasonal spikes — More ads during holidays, events, or product launches?
+- Format evolution — Shifting from text to video over time?
+
+### 5. Competitor Comparison
+
+Compare ad strategies across multiple competitors.
+
+**Step 1: List competitors** (2-5 domains or advertiser IDs).
+
+**Step 2: For each competitor**, run a listing search and collect:
+- Total ad count
+- Format breakdown (text/image/video)
+- Active vs historical ads
+- Platform distribution
+- Key messaging themes (from headlines/snippets of top creatives)
+
+**Step 3: Compare across dimensions:**
+
+```
+📊 Competitive Ad Comparison
+
+| Metric | [Brand A] | [Brand B] | [Brand C] |
+|--------|-----------|-----------|-----------|
+| Total ads | X | Y | Z |
+| Text ads | X | Y | Z |
+| Image ads | X | Y | Z |
+| Video ads | X | Y | Z |
+| Active (last 30d) | X | Y | Z |
+| Primary platform | Search | YouTube | Shopping |
+| Top CTA | "Shop Now" | "Learn More" | "Get Started" |
+
+Key Differences:
+- [Brand A] focuses heavily on text ads with aggressive CTAs
+- [Brand B] invests in video content on YouTube
+- [Brand C] leads with Shopping/product ads
+```
+
+### 6. Political Ad Monitoring
+
+Track political advertising in a specific region.
+
+**Important:** Political ads require `political_ads=true` AND a `region` parameter. Without both, political ads won't appear.
+
+**Step 1: Search for political ads.**
+Use `google_ads_transparency_center` with `political_ads=true` and `region` set to the target region code. Optionally add `text` to search for a specific candidate, party, or PAC.
+
+**Step 2: Analyze the results.**
+Political ad details include `ad_funded_by` — the entity paying for the ad.
+
+**What to track:**
+- Which entities are advertising
+- Volume of ads per entity
+- Regional targeting patterns
+- Format preferences (text vs video for different messages)
+- Funding sources (`ad_funded_by`)
+- Time patterns around elections/events
+
+### 7. Ad Format Analysis
+
+Research creative format trends for an industry or advertiser.
+
+**Step 1: Get ad listings** with `creative_format` filter to isolate a specific format.
+
+**Step 2: For each format, analyze patterns:**
+
+**Text ads:**
+- Average headline length
+- Common power words and phrases
+- CTA language patterns
+- Sitelink topics and count
+- Use of extensions
+
+**Image ads:**
+- Common dimensions (indicates platform targeting)
+- Carousel vs single image ratio
+- Visual style patterns (photography vs illustration vs design)
+- CTA overlay patterns
+
+**Video ads:**
+- Duration distribution
+- YouTube vs non-YouTube hosting
+- Whether ads are repurposed content or ad-specific
+
+### 8. Regional Ad Research
+
+Understand how an advertiser targets different geographic markets.
+
+**Step 1: Run listing search** for the advertiser.
+
+**Step 2: Fetch details on key creatives** — the `regions[]` array in details shows every region where that ad ran, with timing data.
+
+**Step 3: Map regional strategy:**
+- Which regions get the most ads?
+- Do they localize creatives or use the same ads globally?
+- Regional launch timing — staggered rollout or simultaneous?
+- Which regions see the ad most (`times_shown`)?
+
+**Use `region` filter** on the listing search to see only ads shown in a specific region.
+
+## Common Patterns
+
+### "What ads is [company] running?"
+1. Search with `text=[company domain]`
+2. Present total count, format breakdown, and top 5-10 most recent creatives
+3. Offer to drill into specific ads or filter by format/platform
+
+### "Compare [brand A] vs [brand B] ads"
+1. Search each brand separately
+2. Build comparison table (counts, formats, platforms, themes)
+3. Fetch details on 2-3 top creatives per brand for messaging comparison
+
+### "Show me their YouTube ads"
+1. Search with `text=[domain]` and `platform=YOUTUBE`
+2. Fetch details on video creatives for duration, headline, CTA
+3. Present with video durations and messaging summaries
+
+### "What political ads are running in [region]?"
+1. Search with `political_ads=true`, `region=[code]`
+2. Group by `advertiser` / `ad_funded_by`
+3. Present volume per entity with date ranges
+
+### "How has [company]'s advertising changed over the past year?"
+1. Search with `text=[domain]`, `start_date` = 1 year ago, `end_date` = today
+2. Analyze `first_shown`/`last_shown` patterns
+3. Break into quarterly or monthly clusters
+4. Note format shifts, messaging changes, new campaigns
+
+## Tips
+
+- **Domain search is easiest** — use `text=domain.com` to find any advertiser. No need to know the advertiser ID upfront.
+- **Multiple advertiser IDs** — `advertiser_id` accepts comma-separated IDs to search multiple advertisers at once.
+- **Active vs historical** — Compare `last_shown` timestamps against today to determine which ads are currently active vs archived.
+- **Date filtering for campaigns** — Use `start_date`/`end_date` to isolate specific campaign periods (product launches, holidays, events).
+- **Political ads are separate** — They never appear in regular results. Always set `political_ads=true` to find them.
+- **`num` for batch size** — Default is 40 results. Set `num=100` for larger batches (one credit per page regardless of size).
+- **Creative screenshots** — The `image` field in listing results is a rendered screenshot of the ad. Useful for visual analysis even without fetching full details.
+- **Timestamp conversion** — `first_shown` and `last_shown` are Unix timestamps. Convert with standard date functions for human-readable dates.
+- **Region codes** — These are numeric codes used by Google's Ads Transparency Center. Common ones: `2840` (US), `2826` (UK), `2276` (Germany), `2250` (France). When unsure, omit region to search globally.
diff --git a/Specialized/serp-google-images/SKILL.md b/Specialized/serp-google-images/SKILL.md
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+---
+name: serp-google-images
+description: >
+  Specialized skill for Google Images search workflows via SerpApi — find images by topic,
+  filter by size/color/type/license, reverse image search, explore related content, and
+  curate visual collections. Use when: (1) searching for images on a topic or keyword,
+  (2) finding high-resolution or specific-size images, (3) filtering images by color,
+  type (photo, clipart, line art, animated), or license (Creative Commons, commercial),
+  (4) performing reverse image search to find where an image appears online,
+  (5) finding visually similar or related images, (6) curating a collection of images
+  for a project, presentation, or mood board, (7) sourcing stock-style or reference images
+  with specific dimensions or aspect ratios, (8) time-filtered image search for recent
+  images only, (9) any image discovery or visual research task via Google Images.
+  This skill builds on the foundational serpapi skill for all API details.
+metadata: {"openclaw": {"emoji": "🖼️", "requires": {"env": ["SERPAPI_KEY"]}, "primaryEnv": "SERPAPI_KEY"}}
+---
+
+# Google Images Search Workflows
+
+Image search, filtering, reverse lookup, related content discovery, and visual curation via SerpApi's Google Images engines. This skill covers workflow logic — load the **serpapi** foundational skill for API details, parameters, and wrapper script usage.
+
+## Dependencies
+
+This skill builds on:
+- **serpapi** — SerpApi wrapper script and full API reference (engines: `google_images`, `google_images_light`, `google_images_related_content`, `google_reverse_image`)
+
+## Engine Selection
+
+| Engine | Purpose | Speed | Best For |
+|--------|---------|-------|----------|
+| `google_images` | Full image search | Normal | Detailed results with shopping, suggested searches |
+| `google_images_light` | Lightweight image search | Faster | Quick searches, bulk queries, when you don't need shopping/suggestions |
+| `google_images_related_content` | Related images for a specific result | Normal | "More like this" exploration |
+| `google_reverse_image` | Reverse image search by URL | Normal | Finding image sources, visual matches, identification |
+
+**Default to `google_images`** for most workflows. Use `google_images_light` when speed matters or you're doing many searches in sequence.
+
+## Response Structure
+
+### Image Results
+
+Each item in `images_results[]` includes:
+- `position` — Result index
+- `title` — Image description/caption
+- `original` — Full-resolution image URL
+- `original_width` / `original_height` — Image dimensions in pixels
+- `thumbnail` — Smaller preview URL
+- `source` — Domain hosting the image
+- `link` — Page URL where the image appears
+- `is_product` — Whether the image links to a product page
+- `in_stock` — Product availability (when `is_product` is true)
+- `related_content_id` — ID for fetching related/similar images
+- `tag` — Optional label (e.g., "Recipe", "Product")
+
+### Additional Response Sections
+
+- `suggested_searches[]` — Refinement suggestions with `name`, `chips`, `thumbnail`
+- `shopping_results[]` — Product results with `title`, `price`, `source`, `link`, `thumbnail`
+- `related_searches[]` — Related query suggestions with `query`, `thumbnail`
+
+### Pagination
+
+Images return in batches of ~100. Paginate with the `ijn` parameter:
+- `ijn=0` — First page (default)
+- `ijn=1` — Second page (results 101-200)
+- `ijn=2` — Third page (results 201-300)
+
+For `google_images_light`, use the `start` parameter instead (offset-based, 0-999).
+
+## Workflows
+
+### 1. Basic Image Search
+
+Search for images on any topic using the `google_images` engine.
+
+**Required:** `q` (search query)
+**Recommended:** Size and type filters when the user has specific needs
+
+**What to present:** 5-10 results with title, source, dimensions, and original URL. Include thumbnails descriptions when helpful.
+
+**Query tips:**
+- Be specific: "golden retriever puppy outdoors" beats "dog"
+- Use quotes for exact phrases: `"modern kitchen design"`
+- Add context: "logo transparent background" or "infographic template"
+
+### 2. Filtered Image Search
+
+Apply filters to narrow results. All filters go through the `google_images` or `google_images_light` engine as parameters.
+
+#### Size Filters
+
+| Need | Parameter Value | Notes |
+|------|----------------|-------|
+| Large images | `imgsz=l` | Good default for high-quality results |
+| Medium images | `imgsz=m` | Balanced size/speed |
+| Icons/thumbnails | `imgsz=i` | Small images only |
+| Minimum resolution | `imgsz=2mp`, `4mp`, `8mp`, etc. | Specific megapixel minimum (up to 70mp) |
+| Specific minimum | `imgsz=qsvga` (400×300), `vga` (640×480), `svga` (800×600), `xga` (1024×768) | Named size thresholds |
+
+#### Aspect Ratio Filters
+
+| Ratio | Parameter Value |
+|-------|----------------|
+| Square | `imgar=s` |
+| Tall (portrait) | `imgar=t` |
+| Wide (landscape) | `imgar=w` |
+| Panoramic (ultra-wide) | `imgar=xw` |
+
+#### Color Filters
+
+| Need | Parameter Value |
+|------|----------------|
+| Black and white | `image_color=bw` |
+| Transparent background | `image_color=trans` |
+| Specific color dominant | `image_color=red`, `orange`, `yellow`, `green`, `teal`, `blue`, `purple`, `pink`, `white`, `gray`, `black`, `brown` |
+
+#### Type Filters
+
+| Type | Parameter Value |
+|------|----------------|
+| Photos only | `image_type=photo` (or `imgtype=photo`) |
+| Clip art | `image_type=clipart` |
+| Line drawings | `image_type=lineart` |
+| Animated/GIF | `image_type=animated` |
+| Face-focused | `image_type=face` |
+
+#### License Filters
+
+| License | Parameter Value | Use Case |
+|---------|----------------|----------|
+| Creative Commons | `licenses=cl` | Free to use with attribution |
+| Commercial use | `licenses=ol` | Safe for business use |
+| Free to share | `licenses=f` | Personal sharing OK |
+| Free commercial share | `licenses=fc` | Commercial sharing OK |
+| Free to modify | `licenses=fm` | Can edit/remix |
+| Free commercial modify | `licenses=fmc` | Can edit/remix commercially |
+
+**Combining filters:** Pass multiple filter parameters in the same request. They stack — e.g., large + transparent + photo narrows results to large transparent photos.
+
+### 3. Time-Filtered Search
+
+Find images from a specific time period.
+
+**Option A — Relative time:**
+- `period_unit` + `period_value` — e.g., `period_unit=d`, `period_value=7` for past week
+- Units: `s` (second), `n` (minute), `h` (hour), `d` (day), `w` (week), `m` (month), `y` (year)
+
+**Option B — Date range:**
+- `start_date` + `end_date` in `YYYYMMDD` format
+- `start_date` alone = from that date to today
+- `end_date` alone = before that date
+
+**Use cases:**
+- Recent product photos: `period_unit=m`, `period_value=3`
+- Event coverage: date range around the event dates
+- Trending visuals: `period_unit=w`, `period_value=1`
+
+### 4. Reverse Image Search
+
+Find where an image appears online, identify objects, or find similar images using the `google_reverse_image` engine.
+
+**Required:** `image_url` — a publicly accessible URL of the image to search
+
+**Optional:** `q` — add a text query alongside the image for more targeted results
+
+**What you get:**
+- `image_results[]` — Pages containing the same or similar image
+- `inline_images[]` — Visually similar images
+- `knowledge_graph` — Identified subject (if recognizable)
+
+**Use cases:**
+- **Source finding:** "Where did this image originate?" — Check the oldest/most authoritative results
+- **Fact checking:** Verify if an image is being used in context or is misleading
+- **Identification:** "What is this object/landmark/plant?" — Check knowledge graph
+- **Higher resolution:** Find the same image at better quality from different sources
+- **Copyright check:** See where an image is used across the web
+
+**Presentation pattern:**
+1. If knowledge graph identifies the subject, lead with that
+2. List top sources where the image appears (domain, page title)
+3. Show visually similar images if the user wants alternatives
+
+### 5. Related Content Exploration
+
+When a user likes a specific image and wants more like it, use the `google_images_related_content` engine.
+
+**Required:** `related_content_id` — from an image result's `related_content_id` field
+
+**Workflow:**
+1. Perform initial image search
+2. User identifies an image they like (by position or description)
+3. Use that image's `related_content_id` to fetch related content
+4. Present the visually similar results
+
+This is Google's "more like this" feature — it finds images with similar visual characteristics, subject matter, and style.
+
+### 6. Visual Curation / Mood Board
+
+When a user wants to collect images for a project, presentation, or creative brief:
+
+**Step 1: Broad search** — Start with a general query to understand what's available.
+
+**Step 2: Refine with filters** — Apply size, color, type, and aspect ratio filters based on the user's project needs.
+
+**Step 3: Explore related content** — For images the user likes, use related content to find similar ones.
+
+**Step 4: Compile results** — Present a curated selection organized by theme or style.
+
+**Presentation format:**
+
+```
+🖼️ Image Collection: [Theme]
+
+1. "[Title]" — [Source]
+   📐 [Width]×[Height] | 🔗 [original URL]
+   
+2. "[Title]" — [Source]
+   📐 [Width]×[Height] | 🔗 [original URL]
+
+...
+
+💡 Suggested refinements: [from suggested_searches]
+```
+
+**Tips:**
+- Use `imgsz=l` or specific megapixel minimums for print/high-quality projects
+- Use `image_color=trans` for images that need to overlay other content
+- Use `imgar=w` for banner/header images, `imgar=s` for social media posts
+- Filter by license when images will be used commercially
+
+### 7. Product Image Search
+
+When looking for product images, shopping results are especially useful:
+
+**Strategy:**
+1. Search with product-specific query (brand + product name)
+2. Check `shopping_results[]` for product images with prices and availability
+3. Check `images_results[]` where `is_product=true` for additional product images
+4. Filter `in_stock=true` results if linking to purchasable items matters
+
+**Presentation pattern:**
+
+```
+🛒 Product Images: [Product]
+
+Shopping Results:
+• [Title] — $[Price] from [Source]
+  🔗 [link]
+
+Image Results (product pages):
+• "[Title]" — [Source] | 📐 [dimensions]
+  🔗 [original URL]
+```
+
+### 8. Reference Image Search
+
+When searching for images as reference material (design, art, architecture, etc.):
+
+**Strategy:**
+1. Use descriptive, style-specific queries: "brutalist architecture exterior", "watercolor landscape tutorial", "80s retro neon aesthetic"
+2. Filter to large images (`imgsz=l` or `imgsz=4mp`+) for detail
+3. Use color filters to match a palette: `image_color=teal` for cool tones
+4. Use type filters: `image_type=photo` for realism, `image_type=lineart` for technical references
+5. Explore suggested searches for related style terms
+6. Use related content to branch out from strong matches
+
+### 9. Iterative Refinement
+
+When initial results don't match what the user needs:
+
+**Refine the query:**
+- Add adjectives: "minimalist", "vintage", "high-contrast", "aerial view"
+- Add context: "for website", "on white background", "editorial style"
+- Exclude terms with `-`: `"modern kitchen -IKEA"`
+
+**Add filters:**
+- Too many small images → add `imgsz=l`
+- Wrong style → add `image_type=photo` or `image_type=clipart`
+- Wrong colors → add `image_color` filter
+- Wrong proportions → add `imgar` filter
+
+**Explore branches:**
+- Use `suggested_searches[]` from results — these are Google's refinement suggestions
+- Use `related_searches[]` for tangential topics
+- Use related content for visual similarity exploration
+
+**Paginate:**
+- With `google_images`: increment `ijn` (0, 1, 2...)
+- With `google_images_light`: increment `start` (0, 100, 200...)
+
+## Presenting Results
+
+### Standard Image Search Results
+
+```
+🖼️ Google Images: "[query]"
+
+1. "[Title]" — [Source]
+   📐 [Width]×[Height] | [tag if present]
+   🔗 [original URL]
+
+2. "[Title]" — [Source]
+   📐 [Width]×[Height]
+   🔗 [original URL]
+
+...
+
+💡 Try also: [2-3 suggested searches from results]
+```
+
+### Reverse Image Search Results
+
+```
+🔍 Reverse Image Search
+
+🧠 Identified: [knowledge graph result, if present]
+
+📍 Found on:
+1. [Page title] — [domain]
+   🔗 [link]
+2. [Page title] — [domain]
+   🔗 [link]
+
+🖼️ Visually Similar:
+• "[Title]" — [Source] | 📐 [dimensions]
+• "[Title]" — [Source] | 📐 [dimensions]
+```
+
+## Common Patterns
+
+### "Find images of [topic]"
+1. Search with `google_images`, `q=[topic]`
+2. Present top 5-8 results with titles, sources, and dimensions
+3. Offer to filter by size, color, or type
+
+### "Find large/HD images of [topic]"
+1. Search with `imgsz=l` or specific megapixel minimum
+2. Sort presentation by dimensions (largest first)
+3. Note which images have the best resolution
+
+### "Find images with transparent background"
+1. Search with `image_color=trans`
+2. Note: results are typically PNG format
+3. Verify by checking file extensions in original URLs when possible
+
+### "Find free-to-use images of [topic]"
+1. Search with `licenses=cl` (Creative Commons) or `licenses=ol` (commercial)
+2. Always note the license type — "free to use" doesn't mean "no attribution"
+3. Recommend the user verify the license on the source page
+
+### "Where is this image from?" / "What is this?"
+1. Use `google_reverse_image` with `image_url`
+2. Check knowledge graph for identification
+3. List the top sources by authority/date
+4. Show visually similar images if the user wants alternatives
+
+### "Find images similar to this one"
+1. First search to locate the reference image in results (or use reverse search)
+2. Get the `related_content_id` from the matching result
+3. Use `google_images_related_content` to fetch similar images
+4. Present the related results
+
+### "Find [topic] images from the last [time period]"
+1. Search with `period_unit` and `period_value` matching the requested timeframe
+2. Or use `start_date`/`end_date` for specific date ranges
+3. Useful for news events, recent product launches, seasonal content
+
+## Tips
+
+- **Default to large images** unless the user asks otherwise — `imgsz=l` gives better quality results with minimal downside.
+- **`google_images_light` for speed** — when doing multiple searches in sequence (e.g., comparing results across queries), use the light engine to save time and API credits.
+- **Combine filters freely** — size + color + type + license all work together. More filters = fewer but more relevant results.
+- **Suggested searches are gold** — the `suggested_searches[]` array contains Google's own refinement ideas. Use them to help users who aren't sure exactly what they want.
+- **Product detection** — `is_product` and `in_stock` fields help distinguish commercial images from editorial/informational ones.
+- **`tbs` for advanced users** — the `tbs` parameter accepts raw Google filter strings. If the user knows the exact filter string from a Google Images URL, pass it directly.
+- **Aspect ratio for specific uses** — `imgar=w` for banners/headers, `imgar=t` for phone wallpapers/Pinterest, `imgar=s` for avatars/profile pictures, `imgar=xw` for panoramic/landscape.
+- **Localization matters** — use `gl` and `hl` to get region-appropriate image results (e.g., food images vary significantly by region).
+- **License disclaimer** — always remind users that license filters are based on metadata and should be verified on the source page before commercial use.
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