Signal To Lead

Execute signal-scanner with agency config:

For each ICP segment, generate search queries from services[].keywords + icp.segments[].markets:

Example queries for a Shopify agency targeting India D2C:

• "looking for shopify developer India"
• "need shopify agency d2c brand"
• "shopify store redesign help"
• "hiring shopify expert ecommerce"

Search platforms:

1. Reddit -- r/shopify, r/ecommerce, r/smallbusiness, r/Entrepreneur, r/DTC
2. Twitter/X -- keyword searches for service + intent keywords
3. LinkedIn -- job postings, posts mentioning services
4. Job boards -- Indeed, Wellfound for relevant hiring signals
5. Funding news -- Crunchbase, TechCrunch for recently funded companies
6. Freelance platforms -- Upwork, Freelancer for active gigs (if platform-scraper available)
7. Community forums -- Shopify Community, HackerNews, IndieHackers

Run max 3 searches concurrently. Pause 2 seconds between batches to avoid rate limits.

For each signal found, capture:

{
  "company": "company name if visible",
  "website": "their website if mentioned",
  "person": "who posted/is hiring",
  "url": "link to the signal source",
  "platform": "where found",
  "signal_type": "funding|hiring|community_post|gig_listing|social_post|news",
  "signal_date": "when the signal appeared",
  "description": "what they're looking for",
  "budget": "if mentioned",
  "market": "country/region",
  "contact": "contact info if available",
  "contact_surfaces": {
    "has_email": false,
    "has_linkedin": true,
    "has_instagram": false,
    "has_phone": false,
    "has_website": true,
    "channel_count": 2,
    "discovery_notes": "LinkedIn from post URL. Website from company profile."
  }
}

Phase 2: Score & Classify

For each signal, execute lead-scorer:

1. Apply platform base score from scoring.platform_weights
2. Check keyword matches against icp.primary_keywords, icp.secondary_keywords, icp.intent_keywords
3. Filter out matches against icp.negative_keywords (score = 0)
4. Apply hiring signal bonus (+20 if detected)
5. Apply budget tier bonus from scoring.budget_tiers
6. Apply recency bonus from scoring.recency_bonuses
7. Apply market boost from scoring.market_boosts
8. Apply contact surface bonus from scoring.contact_surface_bonus (channels_3_plus: +15, channels_2: +5)

Classify:

• HOT: score >= scoring.thresholds.hot (default 60)
• WARM: score >= scoring.thresholds.warm (default 35)
• COOL: score < warm threshold

Phase 3: Deduplicate

Before writing to CRM:

1. Read existing leads from CRM pipeline tab
2. Match by URL (exact) or company name + website (fuzzy)
3. Remove duplicates
4. Count: N new leads after dedup

Phase 4: Review

Present results:

SIGNAL SCAN RESULTS
---
Total signals found: N
After dedup: M new leads
Breakdown: X HOT, Y WARM, Z COOL

TOP HOT LEADS:
1. [Company] -- [Signal] -- Score: [N] -- Channels: EMAIL, LINKEDIN, WEBSITE (3) -- [URL]
2. ...
3. ...

WARM LEADS: [count]
COOL LEADS (not written to CRM): [count]

Only HOT and WARM leads are written to CRM. COOL leads are logged but not actioned.

Phase 5: Write to CRM

Execute crm-writer to append HOT and WARM leads:

Tab: crm.tabs.pipeline (or crm.tabs.hawk_leads for platform-scraped leads)

Columns:

• Date, Company, Platform, URL, Budget, Description, Urgency, Score, Market, Contact, Status, Signal_Type, Signal_Date, Contact_Surfaces, Channel_Count

Set initial status: "NEW"

Phase 6: Summary

DAILY SIGNAL-TO-LEAD SUMMARY
---
Platforms scanned: [list]
Total signals: N
New leads added to CRM: M (X HOT, Y WARM)
Duplicates filtered: D
Next step: Run /lead-enrichment-pipeline to research and enrich these leads

Example Usage

Trigger phrases:

• "Find new leads today"
• "Run the signal scanner"
• "Scan for buying signals"
• "Find Shopify leads on Reddit"
• "Run signal-to-lead for India D2C segment"

Daily automation: Run at 9 AM as part of /morning-pipeline