Marketplace Scout

A marketplace research assistant that searches Facebook Marketplace, analyzes listings against fair market prices, grades them A+ through F, saves results to CSV, and launches an interactive dashboard.

References

• references/grading-algorithm.md — Full grading weights, formulas, red flag definitions, and grade boundaries
• references/resale-arbitrage.md — Complete resale/flip mode: profitable categories, eBay cross-referencing, modified grading weights
• references/shipping-estimates.md — Shipping cost table by item category and eBay fee calculations
• references/image-extraction.md — Playwright code pattern for extracting product images from listing pages
• references/dashboard-guidelines.md — Accessibility, styling, and responsive rules for dashboard modifications

Examples

• examples/sample-output.csv — 3-row sample demonstrating the 37-column CSV schema with proper escaping, JSON-in-CSV, and signed percentages
• examples/sample-searches.json — Sample search index entry showing the expected format

Dashboard

The assets/dashboard/ directory contains a complete Vite + React + TypeScript dashboard application. On first run, if no dashboard/ directory exists in the working directory, scaffold it by copying assets/dashboard/ to ./dashboard/ and running npm install. On subsequent runs, just update the data files and tell the user to refresh.

Workflow

1. Gather Search Parameters

Ask the user (one at a time via AskUserQuestion):

1. "What are you looking for?" — accept comma-separated items
2. "Search location?" — city and state, default to last used if available
3. "Search radius in miles?" — default 25
4. "Max price? (optional, press Enter to skip)"
5. "Min condition? (new, like new, good, fair, any)" — default "any"

Parse the first answer into separate search terms by splitting on commas.

2. Verify Facebook Login

Navigate to https://www.facebook.com/marketplace/ using Playwright. Take a snapshot. If a login form appears, tell the user to log in manually in the Playwright browser and confirm when ready.

3. Search Facebook Marketplace

For each search term:

1. Navigate to https://www.facebook.com/marketplace/search/?query={encoded_search_term}&exact=false
2. Set location and radius on the first search only (clear location field, type slowly, select autocomplete suggestion, set radius, apply)
3. Scroll 2-3 times (random 1-3s pauses) to load results
4. Extract up to 30 listings: title, price, location, URL, condition
5. Filter out wrong products, over-budget items, below-condition items
6. Between search terms, wait 30-60 seconds (use this time for market research)

4. Market Research

For each unique product type, use WebSearch and WebFetch:

• Fair market price: Search "{product}" sold site:ebay.com and "{product}" price site:swappa.com — extract low/high/median prices
• Price ceiling: Search "{product}" site:apple.com/shop/refurbished (or manufacturer refurbished)
• Vendor link: Search "{product}" buy official site — get retail URL and price
• Review link: Search "{product}" review site:wirecutter.com OR site:tomsguide.com OR site:rtings.com — get URL and score

5. Tiered Deep-Dive

Sort listings by price attractiveness. Deep-dive priority:

1. Vague listings that could be great deals — titles missing key specs where price suggests a higher-end config. These need investigation first.
2. Top 10-15 by price attractiveness — lowest price relative to market research.

For each deep-dive listing:

• Navigate to the listing URL, extract full description, photo count, condition, days listed
• Click through ALL photos — look for "About This Mac" screenshots, spec stickers, serial numbers, system profiler screens
• Extract the product image URL for the dashboard card display (see image extraction below)
• Check seller profile: name, rating, reviews, account age, response time, other listings
• For vague listings, escalate: read description, check photos, check structured attributes, visit seller profile. If still unknown, mark "Specs unverified" and grade conservatively.

Image extraction — save a product photo locally for each listing. Facebook CDN URLs expire quickly, so images must be downloaded during the scrape and saved to ./data/images/.

For each listing page already open in Playwright: Extract a product image per listing and save to ./data/images/{listing_id}.jpg. Set image_url in the CSV to /data/images/{listing_id}.jpg. Leave image_url empty if no image can be extracted — the dashboard shows a styled placeholder with the search term. See references/image-extraction.md for the browser code pattern and fallback selectors.

Anti-detection: wait 2-5s between listings, 5-15s pause every 5 listings.

6. Grade Each Listing

Consult references/grading-algorithm.md for the full grading algorithm. Apply the five weighted categories (Price Value 35%, Seller Trust 25%, Listing Quality 20%, Red Flags 15%, Condition vs Price 5%) and compute the final grade.

For resale arbitrage mode, consult references/resale-arbitrage.md for modified grading weights that prioritize flip profitability, and references/shipping-estimates.md for shipping cost estimates and eBay fee calculations.

Grading output rules:

• Only output grades from the GradeLetter union: A+, A, B, C, D, F. Never output grade X or any other value.
• If grading fails or specs cannot be determined after full investigation, default to grade F with a red flag explanation (e.g. "Specs unverified — graded conservatively as F"). Do not use a placeholder grade.
• grade_breakdown must be a JSON object with exactly 5 string fields: priceValue, sellerTrust, listingQuality, redFlags, conditionConsistency. Each value must be a single letter grade (A+, A, B, C, D, or F) as a string — never a number, array, or nested object.

7. Save to CSV and Update Search Index

Generate timestamped filename: marketplace_results_{YYYY-MM-DD_HH-mm}.csv

Write to ./data/{filename} with columns:

id,search_term,title,price,market_price_low,market_price_high,market_price_median,price_vs_market,grade,grade_breakdown,summary,condition,seller_name,seller_rating,seller_reviews,seller_account_age,seller_response_time,location,distance,description,photo_count,photos_original,red_flags,listing_url,listing_age,vendor_url,vendor_price,review_url,review_score,shipping_estimate_low,shipping_estimate_high,ebay_fees,net_profit_low,net_profit_high,roi_low,roi_high,platform,image_url,date_scraped,search_location,search_radius

See examples/sample-output.csv for the expected format. Rules:

• grade_breakdown is a JSON string with exactly 5 fields (see Step 6 grading output rules)
• red_flags is comma-separated
• price_vs_market is a signed integer percentage (negative = below market)
• Escape commas in text fields by wrapping in double quotes
• Always populate the platform column with "facebook" or "ebay" — the dashboard reads this field directly and must not infer platform from URL parsing
• Never leave price-like fields (price, market_price_low, market_price_high, market_price_median, vendor_price, shipping_estimate_low, shipping_estimate_high, ebay_fees, net_profit_low, net_profit_high) empty — use 0 for unknown values
• Always include pre-computed shipping_estimate_low, shipping_estimate_high, ebay_fees, net_profit_low, net_profit_high, roi_low, and roi_high columns with real calculated values — the dashboard should not re-derive these
• Only output grades from the GradeLetter set (A+, A, B, C, D, F) in the grade column — never X or other values
• Validate the final CSV row count matches the expected listing count before writing the file

Update ./data/searches.json (create if missing) — append a new entry:

{
  "id": "YYYY-MM-DD_HH-mm",
  "timestamp": "ISO 8601",
  "label": "Short summary of search terms",
  "searchTerms": ["term1", "term2"],
  "location": "City, ST",
  "radius": 25,
  "maxPrice": null,
  "csvFile": "marketplace_results_YYYY-MM-DD_HH-mm.csv",
  "listingCount": 32,
  "gradeDistribution": {"A": 3, "B": 11, "C": 11, "D": 3, "F": 4}
}

Copy both files to ./dashboard/public/data/ and maintain latest.csv as a copy of the newest CSV.

8. Launch Dashboard

Check if the dashboard exists at ./dashboard/. If not, scaffold it from the plugin's assets/dashboard/ directory:

cp -r "${CLAUDE_PLUGIN_ROOT}/skills/marketplace-scout/assets/dashboard/" ./dashboard/
cd dashboard && npm install

Check if the dev server is already running on port 5173. If running, tell the user to refresh. If not:

cd dashboard && npm run dev

Summarize findings: total listings per search term, grade distribution, top 3 deals with grades and prices, critical red flags, and changes from prior searches if applicable.

When modifying the dashboard, follow the compatibility rules in references/dashboard-guidelines.md.

Guardrails

• Anti-detection: Randomize wait times between actions (2-5s between listings, 30-60s between searches, 5-15s every 5 listings). Never navigate in predictable patterns.
• Location persistence: Only set location once for the first search. Subsequent searches inherit it.
• Photo investigation: Always click through all listing photos. Spec details hidden in photos are the difference between a bad grade and a good one.
• Conservative grading: When specs cannot be confirmed after full investigation, assume the lower-end configuration. Note the potential upside.
• Data integrity: Always escape CSV fields containing commas. Verify searches.json has valid JSON before appending.
• Dashboard scaffolding: Only copy assets/dashboard/ on first run. Never overwrite an existing dashboard directory — the user may have customized it.