Visual Research

Visual Research

Full-pipeline skill for researching a brand's visual identity, capturing imagery from every available source, and packaging it into a branded HTML report with a 13-section competitive analysis.

Setup

Before starting, check available tools and build a capabilities report.

Required:

• curl — Instagram API calls, direct image downloads. Pre-installed on macOS/Linux.

Recommended (API keys):

Check: echo $GEMINI_API_KEY

Optional (CLI tools):

Check each: which playwright, which yt-dlp, python3 -c "import scrapling" 2>/dev/null && echo ok

Setup behavior:

1. Check each prerequisite and report what's available

2. For missing items, explain what the user loses and how to install

3. If anything is missing, STOP and wait for the user to confirm before proceeding. Present a clear summary like:

   Ready:     Playwright, yt-dlp, curl
   Missing:   GEMINI_API_KEY (vision analysis will use Claude instead)
              Scrapling (will use web_fetch fallback)
   
   Install now, or proceed with what's available?
   

   Do NOT continue to Phase 1 until the user explicitly says to proceed.

   If everything is available, report the green status and proceed directly to Phase 1 — no confirmation needed.

4. Record the capabilities in the output document's Setup section

Source Selection

After Setup confirms tool availability, present the user with source choices before starting the pipeline. Use AskUserQuestion with multiSelect: true to let the user pick which sources to research.

Always included (not selectable): Website homepage + hero image pipeline (~20K tokens). This is the core of any brand research.

Present these 3 questions in a single AskUserQuestion call:

Question 1 — Social sources (header: "Social", multiSelect: true):

Question 2 — Design/industry sources (header: "Design", multiSelect: true):

Question 3 — Other sources (header: "Other", multiSelect: true):

After the user responds, record the selected sources as the Source Plan. Before starting Phase 1, print the Source Plan and total estimated token cost:

Source Plan:
  ✓ Website homepage (always included)  ~20K tokens
  ✓ Instagram                           ~30K tokens
  ✓ Agency case studies                 ~25K tokens
  ✓ Press / media kit                   ~12K tokens
  ✗ TikTok (skipped)
  ✗ YouTube (skipped)
  ...
  ─────────────────────────────────
  Estimated total: ~87K tokens

Token estimates are approximate — actual usage varies by brand (a Fortune 500 with extensive press coverage will use more tokens than a small startup). Estimates are based on: web searches ~2-3K tokens each, web fetches ~5-20K, image vision analysis ~2-5K per image.

Input

Accept a brand config inline or as JSON:

{
  "brand": "Brand Name",
  "website": "https://example.com",
  "social": {
    "instagram": "handle",
    "tiktok": "handle",
    "youtube": "handle",
    "x": "handle"
  },
  "known_agencies": [],
  "known_campaigns": []
}

If the user provides just a brand name, discover the rest during Phase 1.

Pipeline

DISCOVER → CAPTURE → EXTRACT → ANALYZE → PACKAGE

Phase 1 — Discover

Source gating: Only run searches for sources included in the Source Plan. Skip discovery for deselected sources (e.g., don't search for agency case studies if "Agency case studies" was not selected).

Find agency relationships, campaigns, and source URLs.

Read references/source-discovery-guide.md for detailed search strategies.

Core searches:

web_search "[Brand] rebrand agency case study"
web_search "[Brand] brand identity agency"
web_search "[Brand] campaign [Year] case study"
web_search "[Brand] [Agency] behance"
web_search "site:underconsideration.com/brandnew [Brand]"
web_search "site:awwwards.com [Brand]"

Check the brand's press room (/press, /newsroom, /media). Verify social handles exist and are public.

Output: List of source URLs for capture, confirmed social handles, agency identification.

Phase 2 — Capture

Source gating: Only capture sources included in the Source Plan. Always capture the website homepage. Skip social platform screenshots for deselected platforms.

Screenshot key brand touchpoints. Output to captures/ directory.

If Playwright is available:

playwright screenshot --full-page "https://[brand-website]" captures/website-homepage.png
playwright screenshot "https://www.instagram.com/[handle]/" captures/instagram-grid.png
playwright screenshot "https://www.tiktok.com/@[handle]" captures/tiktok-grid.png
playwright screenshot "https://www.youtube.com/@[handle]" captures/youtube-channel.png

Also capture: app store pages, campaign pages, and agency case study pages found in Phase 1.

Cookie consent / overlay dismissal: Third-party pages (agency portfolios, press sites, campaign microsites) almost always show cookie consent banners that will contaminate screenshots. Before capturing any third-party page, dismiss overlays:

// In Playwright script, after page.goto() and before screenshot:
// 1. Try clicking common consent buttons
for (const selector of [
  'button:has-text("Accept")', 'button:has-text("Reject All")',
  'button:has-text("Accept All")', 'button:has-text("Got it")',
  'button:has-text("OK")', 'button:has-text("I agree")',
  '[id*="cookie"] button', '[class*="consent"] button',
  '[id*="onetrust"] button#onetrust-accept-btn-handler'
]) {
  const btn = page.locator(selector).first();
  if (await btn.isVisible({ timeout: 1000 }).catch(() => false)) {
    await btn.click();
    await page.waitForTimeout(500);
    break;
  }
}
// 2. Nuclear fallback — remove overlay elements via JS
await page.evaluate(() => {
  document.querySelectorAll('[id*="cookie"],[id*="consent"],[class*="cookie"],[class*="consent"],[id*="onetrust"],[class*="gdpr"]')
    .forEach(el => el.remove());
});

When using playwright screenshot CLI (not scripted), add a wait and use --timeout to allow the page to settle, but note the CLI cannot dismiss banners. For pages known to have consent modals, prefer scripted Playwright over the CLI command.

Without Playwright: Use web_fetch to grab page content as text. Note visual capture was skipped.

Phase 3 — Extract

Source gating: Only extract images from sources included in the Source Plan. Always extract the hero image and logo. Skip deselected sources (e.g., skip Instagram API if Instagram was not selected, skip App Store if not selected).

Pull actual images from APIs and CDNs.

Read references/image-extraction-techniques.md for detailed recipes.

Core extractions:

Hero Image Sourcing (Priority — Slot 0)

The hero image is the most important visual in the report. It must be:

• High resolution (minimum 1200px wide, ideally 1600px+)
• Brand-representative — shows the brand's actual visual identity, not just something with the logo on it
• Professionally produced — press photos, campaign key art, homepage heroes. Not phone photos, not user-generated content
• Compositionally suitable — works at 16:9 aspect ratio with a gradient overlay on the left 40% (text will sit there)

Search in this priority order, collecting candidates into hero-candidates/:

1. Homepage hero image

   • Use Playwright to screenshot the above-the-fold hero section
   • Also extract the actual image URL from the page source (look for hero/banner container <img> tags, CSS background-image, <video poster>)
   • Apply resolution maximizer (see below) to get the full-size asset
   • Check if the CMS (DatoCMS, Contentful, Prismic, Sanity) exposes the raw asset URL without resize params

2. Press/media kit hero imagery

   • Check /press, /newsroom, /media, /brand pages for downloadable high-res imagery
   • Look for "brand assets" or "media kit" download links
   • Press photos from product launches or conferences are often the highest quality

3. Agency case study hero

   • Behance/Dribbble case studies typically lead with a hero image that the agency art-directed
   • These are purpose-built to showcase the brand's visual identity at its best
   • Apply resolution maximizer to Prismic/Sanity/CMS URLs

4. Brand New (Under Consideration) hero

   • Rebrand articles often lead with a high-quality hero image showing the new identity (800–2000px)
   • Best for brands that have undergone a major visual refresh
   • Before/after comparison images are also strong hero candidates

5. Campaign key art

   • Search: web_search "[Brand] campaign key art [Year]"
   • Search: web_search "[Brand] brand campaign hero image"
   • Trade press (It's Nice That, Creative Review, The Drum) often publishes agency-supplied high-res campaign imagery
   • Conference/event photography from the brand's own events

6. Awwwards project screenshots

   • Website showcases include 1600×1200 project screenshots
   • Good when the brand's digital presence is its strongest visual expression
   • Newer projects (2022+) are best quality

7. Product marketing screenshots

   • Product pages often have polished, art-directed hero shots
   • App Store screenshots via iTunes Search API (1242×2208 iPhone) — no auth needed
   • Marketing landing pages for specific features/products

8. Social media — last resort only

   • Only if nothing above yields a usable image
   • Prefer official brand posts over user photos
   • Instagram carousel first slides are usually highest production value
   • Avoid: phone photos, UGC, nighttime shots, grainy/compressed images

Selection criteria — choose the best candidate:

If Gemini or Claude vision is available, validate each candidate image before loading it (see Image Validation section). Only load validated images for evaluation — a corrupted hero candidate will poison the entire conversation context. Pick the best one. Save as hero-candidates/hero-selected.jpg and note the source.

If no candidate meets minimum quality: Use the best available option but flag it in the research doc: "Hero image is below ideal quality — [reason]. Recommend replacing if a better source is found."

• Official logo — Priority extraction. Try these sources in order until you get a clean, full logo on a solid or transparent background:

  1. Brand press/media kit — /press, /newsroom, /media, /brand, /brand-assets. Many brands offer downloadable logo files (PNG/SVG)
  2. Brandfetch / brand databases — web_search "[Brand] logo PNG transparent", web_search "[Brand] logo brandfetch"
  3. Wikipedia / Wikimedia Commons — Often has high-quality SVG/PNG logos
  4. Agency case study — Behance/Dribbble posts often show the logo isolated on a clean background
  5. Homepage extraction — If the logo is in the site header, screenshot just the header area or extract the logo <img> URL from the page source

  Logo quality requirements: The image must show the complete wordmark/logomark — no cropping, no partial text. Prefer images where the logo sits on a white, light, or transparent background with clear space around it. If the best available logo is small or embedded in a busy scene, note it as low-quality and flag during verification.

  Save to press/[brand]-logo.png (or .jpg/.svg).

• Instagram API — 12 most recent posts with engagement data (curl, no auth)

• YouTube thumbnails — via yt-dlp if available

• Agency case study images — from Prismic/Sanity APIs or direct HTML extraction

• Brand press kit — downloadable assets from press pages

• App Store screenshots — via iTunes Search API (no auth). Returns iPhone screenshots at 1242×2208, iPad screenshots, and 512×512 icons. See references/image-extraction-techniques.md for the recipe. Skip silently if the brand has no iOS app.

• Brand New images — from articles found in Phase 1. Before/after rebrand imagery and brand evolution visuals (800–2000px). See references/image-extraction-techniques.md for extraction recipe.

• Awwwards project screenshots — from project pages found in Phase 1. Website design showcases at 1600×1200 (newer) or 700×500 (older). See references/image-extraction-techniques.md for extraction recipe.

Download to organized directories: instagram/, youtube/, agency/, press/, app-store/, brand-new/, awwwards/.

Image Resolution Maximizer

Apply this procedure to every image URL before downloading. The goal is to always get the highest resolution version available.

1. Strip CDN resize parameters. Many CDNs append width/height params that downsample:

   • Contentful/Prismic: remove ?w=800 or &w=800, or set ?w=2000
   • Cloudinary: change /w_400/ to /w_2000/ or remove transform path segments
   • Imgix: remove ?w=, ?h=, ?fit= params
   • Shopify: change _200x200 suffix to _2000x2000 or remove it
   • WordPress: remove -800x600 before the extension
   • DatoCMS: remove query params for original, or set ?w=2000
   • Sanity: remove ?w= param for full resolution
   • Vercel/Next.js: extract the url param from /_next/image?url=...&w=640&q=75, fetch directly
   • Webflow: remove -w-800 or replace with larger value
   • Squarespace: change format=500w to format=2500w or remove param
   • General: try removing all query parameters and check if the raw URL returns a larger image

   See references/image-extraction-techniques.md for the full CDN pattern reference.

2. Check srcset for larger versions. When extracting from HTML:

   • Parse srcset attribute for the highest w descriptor
   • Check data-src, data-full, data-original attributes
   • Look for <picture> elements with larger <source> URLs

3. Verify resolution after download. For critical images (hero, logo, campaign heroes):

   sips -g pixelWidth -g pixelHeight [image]  # macOS
   

   If width < 800px, flag as low-res and attempt alternative sources.

4. Prefer PNG/WebP over JPEG when both are available at the same size (lossless > lossy for brand assets).

Image Validation (required before ANY image enters context)

CRITICAL: A single corrupted or unsupported image read into Claude's conversation context will poison the entire session — every subsequent API call (even plain text) will fail with "Could not process image". The only recovery is starting a new conversation. Never read an image file without validating it first.

Run this validation on every image before using Read to view it or passing it to vision analysis:

# Validate a single image — returns PASS or FAIL with reason
validate_image() {
  local f="$1"
  if [ ! -f "$f" ]; then echo "FAIL: file not found"; return 1; fi

  local ftype=$(file -b "$f")
  case "$ftype" in
    *JPEG*|*PNG*|*GIF*|*WebP*) ;;
    *) echo "FAIL: unsupported format — $ftype"; return 1 ;;
  esac

  local size=$(stat -f%z "$f" 2>/dev/null || stat -c%s "$f" 2>/dev/null)
  if [ "$size" -gt 5000000 ]; then echo "FAIL: too large (${size} bytes, max 5MB)"; return 1; fi
  if [ "$size" -lt 100 ]; then echo "FAIL: too small (${size} bytes, likely corrupt)"; return 1; fi

  echo "PASS: $ftype, ${size} bytes"
}

Batch validate all downloaded images after Phase 3 completes:

echo "=== Image Validation ==="
fail_count=0
for img in instagram/*.jpg youtube/*.jpg agency/*.{jpg,png} press/*.{jpg,png} app-store/*.{jpg,png} brand-new/*.{jpg,png} awwwards/*.{jpg,png} hero-candidates/*.{jpg,png} captures/*.png; do
  [ -f "$img" ] || continue
  result=$(file -b "$img")
  size=$(stat -f%z "$img" 2>/dev/null || stat -c%s "$img" 2>/dev/null)
  case "$result" in
    *JPEG*|*PNG*|*GIF*|*WebP*) status="OK" ;;
    *) status="BAD"; fail_count=$((fail_count+1)) ;;
  esac
  if [ "$size" -gt 5000000 ]; then status="TOO_LARGE"; fail_count=$((fail_count+1)); fi
  if [ "$size" -lt 100 ]; then status="CORRUPT"; fail_count=$((fail_count+1)); fi
  printf "%-50s %-10s %s bytes  %s\n" "$img" "$status" "$size" "$result"
done
echo "=== $fail_count failures ==="

For images that fail validation:

• Wrong format (HTML, SVG, AVIF): Re-download or find alternate source. HTML usually means the URL returned an error page, not an image.
• Too large (>5MB): Compress with sips --resampleWidth 1600 -s format jpeg "$img" --out "$img" (macOS)
• Too small (<100 bytes): Download failed — re-attempt or remove
• Never read a failed image into context. Skip it entirely and note the gap.

Phase 4 — Analyze

Run vision analysis on the best 8-12 captured images. Only analyze images that passed validation.

With Gemini Vision (GEMINI_API_KEY set):

image: [path-to-image]
prompt: "Analyze this brand image. Extract: (1) dominant colors with hex values, (2) composition/layout pattern, (3) mood/energy, (4) what makes this distinctive vs competitors. Be specific."

Without Gemini (fallback to Claude vision): Read each image file directly and analyze. Claude can extract colors, composition, and mood from images natively.

IMPORTANT: Before reading any image with the Read tool, validate it first (see Image Validation above). If validation fails, skip that image — do NOT attempt to read it. A bad image in context is unrecoverable.

Compile findings into the research doc: colors → Color Palette section, composition → Layout & UX section.

Phase 5 — Package

Generate two outputs: a markdown research document and a branded HTML report.

5a. Markdown research document

Read references/brand-audit-framework.md for the 13-section template. Populate every section with sourced data. Append an image inventory mapping each captured image to its report section.

Save as [brand]-visual-research.md.

5b. HTML report

Read references/ce-styleguide.md and apply all rules strictly. Read templates/report.html for the page structure.

The template uses a full-width layout with hero block, pill-track navigation, and 12 report sections. The 13-section markdown research doc maps to the HTML report as follows:

Section 13 (Audience & Community) content is absorbed into Social Strategy and Brand Personality.

Populate the template with research findings:

1. Write the TL;DR verdict — one punchy sentence capturing the brand's strategic position
2. Write the Signal — the single most important strategic insight from the research
3. Fill each section with content from the research doc using the mapping above
4. Render color swatches with actual extracted hex values in {{SWATCH_N_HEX}} / {{SWATCH_N_NAME}}
5. Fill the Quick Facts grid cells ({{FACT_N_VALUE}} / {{FACT_N_LABEL}}, N=1..8)
6. Fill the Brand Evolution timeline ({{EVOLUTION_N_YEARS}} / {{EVOLUTION_N_TITLE}} / {{EVOLUTION_N_DESC}}, N=1..4)
7. Generate {{ASSESSMENT_ROWS}} — each row must include a data-rating attribute on the Dimension <td>: <tr><td data-rating="Exceptional">Cultural Relevance</td><td class="strength">Exceptional</td><td>Notes here</td></tr> The data-rating value must match the rating text. This powers the mobile layout where the Rating column is hidden and its value is appended inline.
8. Embed images into data-slot positions (see procedure below)
9. Save as [brand]-visual-research.html
10. OG social image: Create a social-optimized version of the hero image (the same image used for data-slot="0"):

    # Resize to 1200px wide (social card standard) and compress to <300KB
    sips --resampleWidth 1200 -s format jpeg -s formatOptions 80 \
      hero-candidates/hero-selected.jpg --out [brand]-og.jpg
    # Verify: must be <300KB and ~1200×630-800px
    stat -f%z [brand]-og.jpg  # should be under 307200
    sips -g pixelWidth -g pixelHeight [brand]-og.jpg
    

    Why: WhatsApp, Telegram, and iMessage silently drop OG previews when the image exceeds ~300KB or ~2000px. Always resize and compress — never use the raw hero file as the OG image. Then replace {{OG_IMAGE_URL}} in the HTML with the published URL of that file once the hosting URL is known. If you don't yet know the publish URL, leave the placeholder — update it after the report is hosted.

STOP — DO NOT open in browser, DO NOT publish, DO NOT report completion. The report is NOT finished until Phase 5c runs and verify/PASSED exists.

12. Run Phase 5c — Visual Verification (below)
13. Only after verify/PASSED exists: open in the user's browser

Image embedding procedure:

The template has numbered image slots using data-slot attributes. Each <img> tag has a data-slot="N" and data-aspect indicating the expected aspect ratio.

Image deduplication — CRITICAL:

Before assigning images to slots, maintain an Image Assignment Log. For each image you embed:

1. Check if that image filename/URL already appears in the log
2. If it does, skip it and choose a different unused image
3. If no unused images remain, leave the slot with its placeholder

After all slots are assigned, review the log. If any image appears more than once, replace the duplicate with an unused image or revert it to the placeholder. It is better to leave a slot empty than to show a duplicate image.

For each slot with a captured image:

1. Convert the image to base64: base64 -i captures/website-homepage.png -b 0 (macOS) or base64 -w 0 (Linux)
2. Find the <img> tag with the matching data-slot="N" attribute
3. Replace the src attribute value with data:image/png;base64,{BASE64} (or image/jpeg for JPGs)
4. Update the alt text with an actual description of the image
5. Record the assignment in your Image Assignment Log: slot N ← filename.jpg

Logo slot (slot 2) special handling: The template uses object-fit:contain with padding for this slot so the full logo is always visible regardless of aspect ratio. Before embedding, visually verify the logo image shows the complete wordmark — if it's cropped or partial, find a better source. Logos are the single most recognizable brand element; getting this wrong is immediately obvious.

For slots without captured images, leave the transparent 1px placeholder (data:image/gif;base64,R0lGODlh...). The template renders gracefully with empty slots.

5c. Visual verification

After saving the HTML report, verify it renders correctly before opening in the browser or publishing. This catches cropping issues, missing images, layout breaks, and unreplaced placeholders.

Step 1 — Screenshot the report at 8 positions (5 desktop + 3 mobile):

mkdir -p verify

# Desktop (1280x800)
playwright screenshot --viewport-size="1280,800" "file:///path/to/[brand]-visual-research.html" verify/d-01-hero-tldr.png
playwright screenshot --viewport-size="1280,800" "file:///path/to/[brand]-visual-research.html#brand-book" verify/d-02-brand-identity.png
playwright screenshot --viewport-size="1280,800" "file:///path/to/[brand]-visual-research.html#brand-in-practice" verify/d-03-practice-ux.png
playwright screenshot --viewport-size="1280,800" "file:///path/to/[brand]-visual-research.html#campaigns" verify/d-04-campaigns.png
playwright screenshot --viewport-size="1280,800" "file:///path/to/[brand]-visual-research.html#vs-client" verify/d-05-comparison-footer.png

# Mobile (390x844)
playwright screenshot --viewport-size="390,844" "file:///path/to/[brand]-visual-research.html" verify/m-01-hero-tldr.png
playwright screenshot --viewport-size="390,844" "file:///path/to/[brand]-visual-research.html#brand-book" verify/m-02-brand-identity.png
playwright screenshot --viewport-size="390,844" "file:///path/to/[brand]-visual-research.html#campaigns" verify/m-03-campaigns.png

Step 2 — Validate screenshots, then review with vision:

Before reading any screenshot into context, validate it:

for f in verify/*.png; do
  size=$(stat -f%z "$f" 2>/dev/null || stat -c%s "$f" 2>/dev/null)
  ftype=$(file -b "$f")
  if [ "$size" -lt 100 ] || [ "$size" -gt 5000000 ]; then
    echo "SKIP $f — bad size ($size bytes)"
  elif echo "$ftype" | grep -qvE "PNG|JPEG|GIF|WebP"; then
    echo "SKIP $f — bad format ($ftype)"
  else
    echo "OK $f — $size bytes"
  fi
done

Only read screenshots that passed validation. If a screenshot fails (e.g., Playwright crashed and wrote an empty file), re-take it rather than reading the bad file.

Read each validated screenshot file and check for these defects:

Step 3 — Fix detected issues:

For each defect:

• Cropping/aspect: Change object-fit from cover to contain (add background: #f5f5f5; padding: 40px), or swap image source
• Missing images: Re-check file exists, re-encode to base64, re-embed into the HTML
• Layout breaks: Adjust inline styles or fix HTML structure
• Unreplaced placeholders: Fill with researched content or sensible default
• Styleguide violations: Correct colors, weights, or decorative elements
• Cookie/consent overlays: Re-capture the source page with banner dismissed (see Phase 2 dismissal procedure), re-encode, and re-embed. This is a fixable defect — never classify as cosmetic or "source content issue"

Apply fixes directly to the saved HTML file.

Step 4 — Re-verify (max 3 passes):

After fixing, re-screenshot only the sections that had issues and re-review. If all screenshots pass, write the gate artifact and proceed. If 3 passes exhausted, note remaining issues in the gate artifact and proceed — don't block indefinitely.

echo "PASSED — $(date -u +%Y-%m-%dT%H:%M:%SZ) — [X] defects found, [Y] fixed, [Z] remaining" > verify/PASSED

The verify/PASSED file is the gate artifact. Phase 5b's publish step checks for it. Do not proceed to browser open or report completion without it.

Step 5 — Clean up:

Delete screenshot PNGs from verify/ after verification passes. Keep verify/PASSED — it is the gate artifact for the publish step.

OpenClaw Deployment

This skill was designed for Claude Code running locally (no hard timeout). When running via OpenClaw sub-agents, the full pipeline will likely exceed the default 10-minute timeout — phases 1-4 alone (web research, screenshots, image extraction, vision analysis) take ~8-10 minutes, leaving no time for report assembly.

Option A — Single agent, longer timeout (simplest)

Set timeout: 1200000 (20 min) on the sub-agent task. This gives enough headroom for the full pipeline in one shot.

Option B — Two-agent split (recommended for reliability)

Split the work across two sequential sub-agents:

The gather agent should save its output to a known location so the package agent can pick it up. Use the workspace directory for handoff.

Sub-agent verification rule: When using Option B, the Package agent must run Phase 5c itself. If a sub-agent reports "complete," the orchestrating agent MUST still verify verify/PASSED exists before accepting the result. Never trust a sub-agent's completion signal without checking the gate artifact.

Option C — Parallel gather + sequential package

For maximum speed, split Phase 1 (Discover) and Phases 2-3 (Capture + Extract) across parallel agents, then run Phase 4-5 sequentially once images are collected. Only worth the complexity for time-sensitive briefs.

Quality Checklist

Gate (must pass before reporting completion or opening browser):

• verify/PASSED file exists — Phase 5c ran to completion
• HTML report passes visual verification — desktop and mobile screenshots reviewed
• No cropping, missing images, placeholder text, or layout breaks detected
• CE styleguide compliance confirmed via vision review (white bg, correct fonts, red accent sparingly)
• Mobile layout verified — grids collapse correctly, text readable, no overflow

Content (verify during pipeline):

• Agency identified and case study images extracted
• Instagram posts downloaded — at least 6 images
• Website homepage captured
• Social grids captured — at minimum Instagram + one other platform
• 8+ images analyzed with vision model
• All 13 sections of research doc populated with sourced data
• Images organized and mapped to data-slot positions
• Financial data includes date stamps
• Campaign data includes agency attribution
• TL;DR section has opinionated verdict and threat level
• Signal dark break contains the single most important strategic insight
• Campaign cards have hero images (data-slot 10, 14)
• Brand in Practice grid has at least 4 of 6 images populated
• No image file assigned to more than one data-slot (check Image Assignment Log)
• Assessment table rows include data-rating attribute on Dimension cells
• Hero image (slot 0) sourced from dedicated hero search — not just picked from Instagram batch
• Hero image resolution ≥1200px wide
• Hero image represents the brand's visual identity (not just "has the logo on it")
• Hero image is professionally produced (not a phone photo or UGC)
• All downloaded images passed through resolution maximizer (no CDN thumbnails)
• HTML report passes visual verification (Phase 5c) — desktop and mobile screenshots reviewed
• No cropping, missing images, placeholder text, or layout breaks detected
• OG meta tags populated — og:image uses an absolute https:// URL (not base64), og:url set once hosted
• CE styleguide compliance confirmed via vision review (white bg, correct fonts, red accent sparingly)
• Mobile layout verified — grids collapse correctly, text readable, no overflow