Signal Scanner

Signal Scanner

Searches multiple platforms for companies and individuals showing real-time buying signals that match the agency's ICP. Uses WebSearch to find leads across Reddit, Twitter/X, LinkedIn, job boards, funding news, Shopify Community, HackerNews, and gig platforms.

Prerequisites

• agency.config.json at repo root with services, icp, and scoring sections
• WebSearch tool available
• Optional: crm-writer skill for dedup against existing CRM leads

Phase 0: Intake

1. Read agency.config.json from the project root.

2. Extract:

   • services[].keywords -- all service keyword arrays
   • icp.segments[] -- each segment's markets, industries, titles, description
   • icp.primary_keywords, icp.secondary_keywords, icp.intent_keywords
   • icp.negative_keywords -- to filter out self-promoters
   • scoring.platform_weights -- to know which platforms to search
   • scoring.hiring_signals -- phrases that indicate active buying

   CRITICAL: All search queries MUST be derived from agency.config.json keywords. When Plasho's positioning changes week-to-week, queries change automatically. NEVER hardcode search queries.

3. Accept optional parameters:

   • platforms -- list of platforms to search (default: all from platform_weights)
   • max_results -- max leads to return (default: 25)
   • time_window -- how far back to search (default: "past week")
   • focus_segment -- specific ICP segment to target (default: all)
   • daily_targets -- read from agency.config.json outreach.daily_targets (default: 20 gig + 5 company = 25 total)
   • geo_split -- read from agency.config.json outreach.geo_split (default: 75% international, 25% India)

Phase 1: Query Generation

Build search queries by combining service keywords with intent signals and platform-specific syntax.

Lead Type Targeting

Queries are split into two categories:

GIG LEAD QUERIES (target: 20 leads) Platforms: Freelancer, Upwork, PeoplePerHour, Guru, Fiverr Must show clear buying signal and budget.

COMPANY LEAD QUERIES (target: 5 leads) Platforms: LinkedIn, Reddit, Twitter/X, Funding News, Product Hunt, Shopify Community, Instagram Brands, Google Maps, Shopify Store Discovery Must show very strong signal + ICP fit.

Geo Split Enforcement

75% of queries target international markets (US, UK, AU, EU):

• Add geo modifiers: "United States", "UK", "Australia", "Europe"
• Filter by .com, .co.uk, .com.au domains where applicable

25% of queries target India:

• Add geo modifiers: "India", "Mumbai", "Delhi", "Bangalore"
• Include Indian platforms and directories

Query templates per platform:

Reddit:

• site:reddit.com "{service_keyword}" "{intent_keyword}" after:{date}
• Subreddits to target: r/shopify, r/ecommerce, r/smallbusiness, r/startups, r/Entrepreneur, r/DTC
• Example: site:reddit.com "shopify developer" "need help" after:2024-01-01

Twitter/X:

• site:twitter.com OR site:x.com "{service_keyword}" "{intent_keyword}"
• Example: site:x.com "looking for shopify expert" "hire"

LinkedIn:

• site:linkedin.com/posts "{service_keyword}" "{intent_keyword}"
• site:linkedin.com/jobs "{service_keyword}"
• Example: site:linkedin.com/posts "shopify developer" "hiring"

Job Boards:

• site:indeed.com OR site:glassdoor.com "{service_keyword}" "{market}"
• site:angel.co OR site:wellfound.com "{service_keyword}"

Funding News:

• "{industry} startup" "raises" OR "funding" OR "seed round" OR "series A" {time_window}
• Filter to companies in ICP industries

Shopify Community:

• site:community.shopify.com "{intent_keyword}" "{service_keyword}"

HackerNews:

• site:news.ycombinator.com "{service_keyword}" "{intent_keyword}"

Gig Platforms (Freelancer, Upwork, PeoplePerHour, Guru, Fiverr):

• site:freelancer.com/projects "{service_keyword}"
• site:peopleperhour.com "{service_keyword}"

Upwork:

• site:upwork.com/jobs "{service_keyword}"
• Example: site:upwork.com/jobs "shopify developer"

Twitter/X (Brand Discovery):

• site:twitter.com OR site:x.com "{d2c_keyword}" "shopify" OR "ecommerce" OR "d2c"
• Also: site:x.com "{industry}" "just launched" OR "coming soon" OR "new store"
• Focus on brand accounts, not agencies

Instagram Brands:

• site:instagram.com "{d2c_keyword}" "shop" OR "store" OR "link in bio"
• Target hashtags: #shopifystore, #d2cbrand, #ecommerce, #shopifyseller
• Extract: profile name, handle, website link from bio

Product Hunt:

• site:producthunt.com "{d2c_keyword}" OR "ecommerce" OR "shopify"
• Focus on recently launched D2C products

Google Maps / Business Directories:

• site:google.com/maps "{industry}" "{market}" + justdial.com (India), yelp.com (US/UK)
• Extract: business name, phone, website

Shopify Store Discovery:

• site:myshopify.com "{industry_keyword}" + site:builtwith.com "shopify" "{industry}"
• Look for stores with basic themes (Dawn, Debut) indicating redesign need

Query generation rules:

• Generate queries to hit daily targets: 20 gig leads + 5 company leads.
• Prioritize platforms with higher platform_weights scores.
• Include market/country filters for geo-targeted segments.
• Allocate queries based on daily targets. Gig platforms need more queries (20 leads vs 5).
• Enforce geo split: 75% of queries must target US/UK/AU/EU markets, 25% India.

Phase 2: Search Execution

Execute searches using WebSearch with rate limiting:

• Max 3 concurrent searches at any time.
• 2-second pause between batches of 3.
• Process results as they return; do not wait for all to complete.

For each search result, extract:

• url -- the source URL
• title -- page title or post title
• snippet -- the text excerpt from the search result
• platform -- which platform (inferred from URL domain)
• posted_date -- if visible in the search result

Phase 3: Signal Validation

For each raw result, validate it is a genuine buying signal:

1. Negative keyword filter: Check title + snippet against icp.negative_keywords. Discard if any match (these are self-promoters, not buyers).

2. Relevance check: The result must contain at least one primary_keyword OR one secondary_keyword AND at least one intent_keyword. If it has only keywords but no intent, mark as LOW confidence.

3. Recency check: Prefer results from the last 7 days. Flag anything older than 30 days as STALE.

4. Duplicate check: Compare URLs against previously returned results in this session. Skip exact URL duplicates. Also skip if the same company/person appears from a different URL (same underlying signal).

5. Lead type classification: Classify each result as either "gig" or "company" based on the source platform. Gig platforms (Freelancer, Upwork, PPH, Guru, Fiverr) = gig lead. Everything else = company lead.

6. Geo classification: Determine the lead's geography. Classify as "international" (US/UK/AU/EU) or "india" based on content, URL, currency, or explicit location mentions.

Phase 4: Entity Extraction

For each validated signal, extract structured data:

{
  "company": "Company name (if identifiable)",
  "website": "Company website (if findable from context)",
  "person": "Name of the person posting/mentioned",
  "title": "Their job title (if available)",
  "url": "Source URL where signal was found",
  "platform": "Reddit | Twitter | LinkedIn | Freelancer | Upwork | etc.",
  "signal_type": "hiring | funding | job_posting | community_question | gig_request | tech_migration | product_launch | brand_discovery | store_discovery",
  "urgency": "HIGH | MEDIUM | LOW",
  "reason": "1-line summary of why this is a signal",
  "country": "Country (if determinable)",
  "posted_date": "ISO date or relative",
  "raw_snippet": "The relevant text excerpt",
  "confidence": "HIGH | MEDIUM | LOW",
  "lead_type": "gig | company",
  "geo_bucket": "international | india",
  "contact_surfaces": {
    "has_email": true,
    "has_linkedin": false,
    "has_instagram": false,
    "has_phone": false,
    "has_website": true,
    "channel_count": 2,
    "discovery_notes": "Email from gig platform. Website from company field."
  }
}

Contact surface defaults by platform:

Use these defaults when extracting contact surfaces. Override with actual data when found in the signal content. channel_count = number of YES/MAYBE channels confirmed as available.

Urgency rules:

• HIGH: Explicit hiring/buying language + budget mentioned + recent (< 48h)
• MEDIUM: Intent keywords present + relevant service match + recent (< 7 days)
• LOW: Relevant topic but weak intent signal or older than 7 days

Phase 5: Dedup Against CRM

If crm-writer skill is available and CRM is configured:

1. Read existing leads from the relevant CRM tab (e.g., "Hawk Leads", "Researched Leads").
2. Compare by: company name (fuzzy), URL (exact), person name + company combo.
3. Mark duplicates as "is_duplicate": true but still include them in output with a note.

Phase 6: Review

Present the results to the user, separated by lead type:

Found {N} signals across {M} platforms:

COMPANY LEADS ({count}/5 target):
1. [Platform] Company - "reason" (urgency: HIGH, confidence: HIGH, geo: US)
2. ...

GIG LEADS ({count}/20 target):
1. [Platform] "gig title" - "reason" (urgency: HIGH, budget: $X, geo: UK)
2. ...

GEO SPLIT: {X}% international, {Y}% India (target: 75/25)
Duplicates skipped: {count}
Rejected (self-promoters): {count}

Write directly to CRM (no approval gate).

Phase 7: Log to CRM

Use crm-writer to append new signals to the configured CRM tab. Write one row per signal with columns: Date, Platform, Company, Person, URL, Signal Type, Urgency, Reason, Country, Lead Type, Geo Bucket, Score.

Example Usage

Trigger phrases:

• "Scan for signals"
• "Find buying signals"
• "Run a signal scan"
• "Search for leads showing intent"
• "Find companies looking for Shopify help"

User: Scan for buying signals this week
Assistant: [reads config, generates queries across Reddit/LinkedIn/Freelancer/Upwork/etc., executes with rate limiting, validates and structures results, presents sorted by lead type with geo split]

User: Find signals on Reddit and Shopify Community only
Assistant: [same flow but limited to those 2 platforms]