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Real Estate Scout
Monitor real estate listings matching your criteria and get daily alerts on new properties
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intermediate⏱ 30 minutes🔄 10 swappable alternatives
🧂 Ingredients
🔌 APIs
property_listings_prices_and_home_details
🔄 Alternatives:
Redfin — More accurate estimatesRealtor Com — MLS-connected listings
calculate_commute_times_and_nearby_amenities
🔄 Alternatives:
Mapbox — Better customization, developer-friendlyHere — Good for logistics/routingOpenstreetmap — Free, open data
instant_alerts_for_hot_new_listings
🔄 Alternatives:
Ntfy — Free, open-source push notificationsTelegram — Free push via TelegramSlack — Team notification channel
track_listings_and_comparison_data
🔄 Alternatives:
Airtable — Better for structured data + APINotion Databases — More flexible views
📋 Step-by-Step Build Guide
STEP 1
1. Define your criteria: location/neighborhoods, price range, bedrooms, bathroom
1. Define your criteria: location/neighborhoods, price range, bedrooms, bathrooms, square footage, must-have features
Define your criteria: location/neighborhoods, price range, bedrooms, bathrooms, square footage, must-have features Steps: 1. Validate all required inputs are available 2. Execute the operation described above 3. Verify the result meets expected output format 4. Handle errors gracefully — retry transient failures, log and alert on persistent ones 5. Return structured output with status and any relevant data If any required data is missing, request it from the user before proceeding.
STEP 2
2. Daily: search listings APIs for new properties matching your criteria (listed
2. Daily: search listings APIs for new properties matching your criteria (listed in last 24h)
Use the GitHub API to fetch the relevant data.
GET https://api.github.com/repos/{owner}/{repo}/{endpoint}
Headers: Authorization: Bearer {GITHUB_TOKEN}, Accept: application/vnd.github.v3+json
Parse the response and extract the key fields.
Handle pagination if results exceed one page (check Link header).
Rate limit: GitHub allows 5,000 requests/hour with auth. If you get 403, check X-RateLimit-Remaining header.
Format the output concisely with the most important information first.STEP 3
3. For each match, pull: address, price, beds/baths, sqft, year built, listing p
3. For each match, pull: address, price, beds/baths, sqft, year built, listing photos, days on market
For each match, pull: address, price, beds/baths, sqft, year built, listing photos, days on market Steps: 1. Validate all required inputs are available 2. Execute the operation described above 3. Verify the result meets expected output format 4. Handle errors gracefully — retry transient failures, log and alert on persistent ones 5. Return structured output with status and any relevant data If any required data is missing, request it from the user before proceeding.
STEP 4
4. Calculate value metrics: price per sqft vs neighborhood average, price vs Zes
4. Calculate value metrics: price per sqft vs neighborhood average, price vs Zestimate, price change history
Process the data and calculate the requested metrics. Steps: 1. Validate input data — check for nulls, out-of-range values, duplicates 2. Apply the calculation/aggregation logic 3. Compare against benchmarks or previous periods if available 4. Format results with appropriate precision (2 decimal places for percentages, whole numbers for counts) Include: current value, previous value, change (absolute and %), trend direction (↑↓→). Flag any anomalies: values >2 standard deviations from the mean. If insufficient data for a reliable calculation, state the minimum needed and return partial results.
STEP 5
5. Optionally: calculate commute time to your office via Google Maps
5. Optionally: calculate commute time to your office via Google Maps
Process the data and calculate the requested metrics. Steps: 1. Validate input data — check for nulls, out-of-range values, duplicates 2. Apply the calculation/aggregation logic 3. Compare against benchmarks or previous periods if available 4. Format results with appropriate precision (2 decimal places for percentages, whole numbers for counts) Include: current value, previous value, change (absolute and %), trend direction (↑↓→). Flag any anomalies: values >2 standard deviations from the mean. If insufficient data for a reliable calculation, state the minimum needed and return partial results.
STEP 6
6. Score each listing: value score (price vs market), condition (new listing vs
6. Score each listing: value score (price vs market), condition (new listing vs price reduced), commute time
Use the GitHub API to fetch the relevant data.
GET https://api.github.com/repos/{owner}/{repo}/{endpoint}
Headers: Authorization: Bearer {GITHUB_TOKEN}, Accept: application/vnd.github.v3+json
Parse the response and extract the key fields.
Handle pagination if results exceed one page (check Link header).
Rate limit: GitHub allows 5,000 requests/hour with auth. If you get 403, check X-RateLimit-Remaining header.
Format the output concisely with the most important information first.STEP 7
7. Instant alert for high-scoring listings (great deal + good commute + matches
7. Instant alert for high-scoring listings (great deal + good commute + matches must-haves)
Instant alert for high-scoring listings (great deal + good commute + matches must-haves) Steps: 1. Validate all required inputs are available 2. Execute the operation described above 3. Verify the result meets expected output format 4. Handle errors gracefully — retry transient failures, log and alert on persistent ones 5. Return structured output with status and any relevant data If any required data is missing, request it from the user before proceeding.
STEP 8
8. Daily: summary of all new matches with scores and links. Weekly: market trend
8. Daily: summary of all new matches with scores and links. Weekly: market trends in your target area
Daily: summary of all new matches with scores and links. Weekly: market trends in your target area Steps: 1. Validate all required inputs are available 2. Execute the operation described above 3. Verify the result meets expected output format 4. Handle errors gracefully — retry transient failures, log and alert on persistent ones 5. Return structured output with status and any relevant data If any required data is missing, request it from the user before proceeding.
🤖 Example Agent Prompt
Define your criteria: location/neighborhoods, price range, bedrooms, bathrooms, square footage, must-have features Steps: 1. Validate all required inputs are available 2. Execute the operation described above 3. Verify the result meets expected output format 4. Handle errors gracefully — retry transient failures, log and alert on persistent ones 5. Return structured output with status and any relevant data If any required data is missing, request it from the user before proceeding.
Copy this prompt into your agent to get started.