Product Researcher

Extract per product:

• price — current retail price
• image_url — direct CDN/product image URL (from brand site, NOT Amazon thumbnail)
• buy_links — up to 3: Amazon, brand official, Best Buy, etc. — must be exact product URLs
• ratings — from as many of these sources as you can find:
  • amazon: {"score": 4.5, "count": "1,234 ratings"}
  • google: {"score": 4.3, "count": "500+ reviews"}
  • reddit: {"score": 4.0, "label": "Highly recommended"}
  • rtings / pcmag / wirecutter / techradar — use "max": 10 or "max": 5 as appropriate
• pros — 4–6 concrete bullet points from reviews
• cons — 3–5 real weaknesses from reviews
• summary — one expert sentence (e.g. "Pack Hacker's top pick for one-bag travel")
• recommended — true for the single overall best pick
• tag — e.g. "In Your Cart", "Best Value", "Recommended", "Budget Pick"

Image URL tips:

• Fetch the brand's product page to find CDN image URLs (usually cdn.shopify.com, brand.com/cdn/shop/files/...)
• Avoid Amazon image URLs (often blocked). Use brand site or retailer CDN URLs.
• URL should end in .jpg, .webp, or .png with no login required

Ratings accuracy: Only include ratings you actually found via search — never fabricate scores.

3. Build the JSON Data File

Write all product data to /tmp/pr_research.json as a JSON array:

[
  {
    "name": "Product Name — Variant",
    "price": "$99.99",
    "tag": "In Your Cart",
    "image_url": "https://cdn.example.com/product.jpg",
    "buy_links": [
      {"store": "Amazon", "url": "https://amazon.com/dp/ASIN", "price": "$99.99"},
      {"store": "Official Site", "url": "https://brand.com/product"}
    ],
    "ratings": {
      "amazon": {"score": 4.5, "count": "1,234 ratings"},
      "google": {"score": 4.3, "count": "500+ reviews"},
      "wirecutter": {"score": 4.0, "max": 5, "label": "Top Pick"}
    },
    "pros": ["Pro 1", "Pro 2", "Pro 3"],
    "cons": ["Con 1", "Con 2"],
    "recommended": false,
    "summary": "Optional expert one-liner"
  }
]

4. Render the Canvas

Find the render_canvas.py script inside this skill's scripts/ directory.

python3 /path/to/product-researcher/scripts/render_canvas.py \
  --data /tmp/pr_research.json \
  --title "🔍 [Category] Research"

Override the canvas host if needed (e.g. for Tailscale/remote access):

python3 render_canvas.py --data /tmp/pr_research.json --host YOUR_HOST_IP --title "🔍 ..."

Or set it via environment variable:

CANVAS_HOST=YOUR_HOST_IP python3 render_canvas.py --data /tmp/pr_research.json --title "🔍 ..."

The script auto-downloads images, builds a summary highlights bar (Best Value / Top Pick / Highest Rated), and renders all product cards with ratings, pros/cons, and buy links.

To update an existing canvas session instead of opening a new tab:

... --session SESSION_ID

The canvas lib path defaults to ../../live-canvas/claude/implementation relative to the script. Override with CANVAS_LIB_PATH env var if your live-canvas skill is installed elsewhere.

5. Report to User

• Share the canvas URL: http://CANVAS_HOST:18793/?session=SESSION_ID
• Summarize top picks in chat: best value, overall winner, best for their specific use case
• Note that buy links require right-click → open in new tab

Tips

• Buy link validity: Verify the ASIN or URL slug points to the exact product — not a search page
• Image fallback: If image fails to download, the script shows a 📦 placeholder automatically
• Parallel searching: Run multiple WebSearch calls in parallel to speed up research on large product lists
• Session reuse: Use --session to push updates to the same canvas tab without opening a new one