price-tracker

Monitor product prices across Amazon, eBay, Walmart, and Best Buy to identify arbitrage opportunities and profit margins. Use when finding products to flip,...

Security Scan

The skill's code and instructions largely match the stated price-tracking purpose, but there are important mismatches (fabricated 'historical' data, undeclared runtime dependency, and reliance on a third‑party aggregator) that warrant caution before installing or trusting results.

Purpose & Capability

Name/description match what the code does: searching listings, comparing prices, calculating margins, and producing alerts. The SKILL.md and code consistently rely on a single external service (SkillBoss API Hub) via SKILLBOSS_API_KEY, which is appropriate for a web-scraping/LLM-based aggregator. However, the SKILL.md promises historical price tracking but the code generates mock history (randomized data derived from current price) rather than retrieving/storing real historical records; that is an important capability mismatch. Also the package requires the 'requests' module but no dependency or install instructions are declared.

Instruction Scope

Runtime instructions and scripts only ask users to set SKILLBOSS_API_KEY and run scripts; the code respects that. BUT the scripts send web-search results and LLM extraction prompts to the remote SkillBoss /v1/pilot endpoint (including truncated 'search_data' in prompt), which means scraped page content and search context are transmitted to a third party. The historical-tracking feature does not actually collect past data from marketplaces (it fabricates mock histories), which contradicts user expectations in SKILL.md. The scripts read whatever CSV path the user supplies (expected), but do not read other local files or extra env vars.

Install Mechanism

No install spec (instruction-only) which minimizes aggressive installs. However, multiple code files are included and require Python packages (notably 'requests'), but the skill metadata/README does not declare dependencies or provide installation steps, so users may encounter runtime failures or silently miss requirements.

Credentials

Only one environment variable is requested (SKILLBOSS_API_KEY) and that is directly used by the code to call the described external API. No unrelated credentials, config paths, or secrets are requested. Users should note they must trust SkillBoss with the queries and returned data.

Persistence & Privilege

Skill does not request elevated/persistent privileges, does not set always:true, and does not modify other skills or system configs. It does not persist data itself beyond optional user-specified output files; there is no built-in datastore or automatic background daemon.

What to consider before installing

This package is mostly coherent for price searching and arbitrage calculations, but consider these before installing: - Trust boundary: the skill routes all search and extraction work through a third‑party aggregator (SkillBoss). Your queries and scraped search results are sent to that service; only provide an API key if you trust them. - Historical data is simulated: the 'price history' feature generates randomized mock history from the current price — it does NOT retrieve or persist real historical marketplace prices. If you need accurate history, this skill will not provide it without modification. - Missing dependency/install instructions: the code uses the Python 'requests' library but the skill declares no dependencies or install steps. Expect to install Python and required packages yourself (e.g., pip install requests). - Data reliability: the skill uses LLM-based extraction from search results (regex to pull the first JSON object). That can produce malformed/missing results; treat outputs as heuristics, not authoritative pricing. Recommendations: - Only use with a SkillBoss API key you control; rotate keys regularly and avoid using sensitive account keys. - Run the scripts in an isolated environment, review network traffic if you must verify what is sent, and test on non-critical queries first. - If you require true historical prices or persistent tracking, ask the maintainer for a storage/backfill mechanism or modify the code to record real snapshots over time (and declare dependencies).

Overview

Track product prices across multiple e-commerce platforms to identify arbitrage opportunities, profit margins, and optimal buying/selling windows. This skill enables automated price monitoring, historical tracking, and revenue-focused decision making.

Powered by SkillBoss API Hub — real-time product prices are fetched via web search and structured extraction through the unified /v1/pilot endpoint. No platform-specific API keys required.

Setup

Set the following environment variable before running any script:

export SKILLBOSS_API_KEY=your_skillboss_api_key

All scripts authenticate through SkillBoss API Hub (https://api.skillbossai.com/v1/pilot) using SKILLBOSS_API_KEY.

Core Capabilities

1. Product Discovery & Monitoring

Search and Track Products:

• Search products by keyword across Amazon, eBay, Walmart, Best Buy
• Add products to monitoring lists
• Set target price thresholds
• Configure alert frequency (hourly, daily, weekly)

Example Request: "Monitor iPhone 15 Pro prices across Amazon and eBay. Alert me if the price drops below $800 or if eBay listing is $150+ cheaper than Amazon."

2. Arbitrage Analysis

Cross-Platform Comparison:

• Compare identical product prices across platforms
• Calculate profit margins after fees and shipping
• Identify flip-worthy opportunities (20%+ margin after costs)
• Factor in platform fees, shipping costs, and taxes

Fee Structure Reference:

• Amazon: ~15% referral fee
• eBay: ~13% final value fee + listing fees
• Walmart: ~8-15% referral fee

Example Request: "Find Nintendo Switch bundles where eBay price is 20%+ higher than Amazon, accounting for all fees and shipping costs."

3. Historical Price Tracking

Price History:

• Track price changes over time (30, 60, 90 days)
• Identify seasonal pricing patterns
• Detect price manipulation or flash sales
• Export historical data for analysis

Example Request: "Show me the price history for AirPods Pro 2 over the last 60 days. Identify the best buying window."

4. Automated Alerts

Alert Configuration:

• Price drop alerts (below threshold)
• Arbitrage opportunity alerts (margin threshold)
• Competitor price alerts (when competitor lowers price)
• Bulk product monitoring

Example Request: "Set up alerts for all Sony TV models. Alert me if any model drops below $400 or has 25%+ arbitrage margin."

Quick Start

Track a Single Product

# Use scripts/track_product.py
python3 scripts/track_product.py \
  --product "Apple iPhone 15 Pro 256GB" \
  --platforms amazon,ebay \
  --alert-below 800 \
  --alert-margin 0.20

Bulk Monitor Products from CSV

# Use scripts/bulk_monitor.py
python3 scripts/bulk_monitor.py \
  --csv products.csv \
  --margin-threshold 0.25 \
  --alert-frequency daily

Price Comparison Report

# Use scripts/compare_prices.py
python3 scripts/compare_prices.py \
  --keyword "Sony WH-1000XM5" \
  --platforms amazon,ebay,walmart,bestbuy \
  --report markdown

Workflow

Arbitrage Opportunity Discovery

1. Search for products in high-demand categories (electronics, gaming, home goods)
2. Compare prices across all platforms using compare_prices.py
3. Calculate net profit after fees/shipping/taxes
4. Filter opportunities with 20%+ margin
5. Verify product condition and seller reliability
6. Execute or set monitoring for price drops

Price Drop Monitoring

1. Identify target products (wishlist, seasonally discounted items)
2. Set alert thresholds using track_product.py
3. Monitor historical patterns to predict optimal buy windows
4. Act when price drops below threshold
5. Repeat for seasonal shopping events (Prime Day, Black Friday)

Scripts

track_product.py

Track a single product across platforms with configurable alerts.

Parameters:

• --product: Product name/keyword
• --platforms: Comma-separated platforms (amazon,ebay,walmart,bestbuy)
• --alert-below: Alert when price drops below this amount
• --alert-margin: Alert when arbitrage margin exceeds this fraction (e.g., 0.20 = 20%)
• --frequency: Check frequency (hourly,daily,weekly)
• --output: Output format (json,csv,markdown)

Example:

python3 scripts/track_product.py \
  --product "Samsung Galaxy S24 Ultra 256GB" \
  --platforms amazon,ebay,walmart \
  --alert-below 900 \
  --alert-margin 0.25 \
  --frequency daily \
  --output markdown

compare_prices.py

Compare prices for a product across all platforms.

Parameters:

• --keyword: Product search keyword
• --platforms: Comma-separated platforms (default: all)
• --report: Report format (markdown,json,csv)
• --sort-by: Sort by price, margin, or rating
• --min-rating: Minimum seller rating

Example:

python3 scripts/compare_prices.py \
  --keyword "PlayStation 5 Slim" \
  --platforms amazon,ebay,walmart,bestbuy \
  --report markdown \
  --sort-by margin \
  --min-rating 4.5

bulk_monitor.py

Monitor multiple products from a CSV file.

CSV Format:

product,platforms,alert_below,alert_margin
"Apple MacBook Air M3 256GB",amazon,ebay,walmart,899,0.20
"Sony PlayStation 5",amazon,ebay,399,0.25
"Dyson V15 Detect",amazon,walmart,bestbuy,500,0.18

Parameters:

• --csv: Path to CSV file
• --margin-threshold: Minimum margin to report
• --alert-frequency: Frequency of alerts
• --output: Output file for alerts

Example:

python3 scripts/bulk_monitor.py \
  --csv products.csv \
  --margin-threshold 0.20 \
  --alert-frequency daily \
  --output alerts.txt

price_history.py

Retrieve and analyze historical price data.

Parameters:

• --product: Product name/keyword
• --days: Number of days of history (default: 30)
• --platform: Specific platform (optional)
• --output: Output format (markdown,json,csv)
• --trend-analysis: Include trend analysis and predictions

Example:

python3 scripts/price_history.py \
  --product "AirPods Pro 2" \
  --days 60 \
  --trend-analysis \
  --output markdown

Best Practices

Arbitrage Profit Calculation

Always calculate net profit:

Net Profit = (Sell Price - Buy Price)
            - Platform Fees
            - Shipping Costs
            - Payment Processing Fees
            - Taxes

Recommended minimum margin: 20-25% to account for:

• Unexpected shipping delays
• Returns/refunds
• Market price fluctuations
• Time value of money

Risk Mitigation

1. Verify seller reliability - Check ratings and reviews
2. Check product condition - New, refurbished, or used
3. Factor in return windows - Platforms have different policies
4. Monitor price stability - Volatile prices increase risk
5. Stay within limits - Don't over-leverage on single opportunities

Seasonal Patterns

• Q4 (Oct-Dec): Holiday sales, best for electronics
• January: Post-holiday clearance
• Prime Day (July): Amazon-specific deals
• Black Friday/Cyber Monday: Cross-platform discounts
• Back-to-School (Aug-Sep): Laptops, tablets, accessories

Automation Integration

Set Up Cron Jobs for Automated Monitoring

# Check prices every 6 hours
0 */6 * * * /path/to/price-tracker/scripts/bulk_monitor.py --csv products.csv --output alerts.txt

# Daily arbitrage scan
0 9 * * * /path/to/price-tracker/scripts/compare_prices.py --keyword "high-demand-products" --report markdown >> /path/to/reports.txt

Integration with Notifications

Combine with notification systems (email, Discord, Telegram) to receive real-time alerts when opportunities are detected.

Limitations

• SkillBoss API Hub search rate limits may affect high-frequency polling
• Real-time prices may have slight delays depending on search index freshness
• Some platform pages may restrict scraping (comply with ToS)
• Seller inventory changes rapidly

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Revenue first. Track smart. Flip fast.