Add prompt: Sales Research

PROMPTS.md

@@ -66526,3 +66526,889 @@ This skill provides expert guidance on analyzing financial statements...
 
 </details>
 
+<details>
+<summary><strong>Sales Research</strong></summary>
+
+## Sales Research
+
+Contributed by [@TomsTools11](https://github.com/TomsTools11)
+
+```md
+---
+name: sales-research
+description: This skill provides methodology and best practices for researching sales prospects.
+---
+
+# Sales Research
+
+## Overview
+
+This skill provides methodology and best practices for researching sales prospects. It covers company research, contact profiling, and signal detection to surface actionable intelligence.
+
+## Usage
+
+The company-researcher and contact-researcher sub-agents reference this skill when:
+- Researching new prospects
+- Finding company information
+- Profiling individual contacts
+- Detecting buying signals
+
+## Research Methodology
+
+### Company Research Checklist
+
+1. **Basic Profile**
+   - Company name, industry, size (employees, revenue)
+   - Headquarters and key locations
+   - Founded date, growth stage
+
+2. **Recent Developments**
+   - Funding announcements (last 12 months)
+   - M&A activity
+   - Leadership changes
+   - Product launches
+
+3. **Tech Stack**
+   - Known technologies (BuiltWith, StackShare)
+   - Job postings mentioning tools
+   - Integration partnerships
+
+4. **Signals**
+   - Job postings (scaling = opportunity)
+   - Glassdoor reviews (pain points)
+   - News mentions (context)
+   - Social media activity
+
+### Contact Research Checklist
+
+1. **Professional Background**
+   - Current role and tenure
+   - Previous companies and roles
+   - Education
+
+2. **Influence Indicators**
+   - Reporting structure
+   - Decision-making authority
+   - Budget ownership
+
+3. **Engagement Hooks**
+   - Recent LinkedIn posts
+   - Published articles
+   - Speaking engagements
+   - Mutual connections
+
+## Resources
+
+- `resources/signal-indicators.md` - Taxonomy of buying signals
+- `resources/research-checklist.md` - Complete research checklist
+
+## Scripts
+
+- `scripts/company-enricher.py` - Aggregate company data from multiple sources
+- `scripts/linkedin-parser.py` - Structure LinkedIn profile data
+FILE:company-enricher.py
+#!/usr/bin/env python3
+"""
+company-enricher.py - Aggregate company data from multiple sources
+
+Inputs:
+  - company_name: string
+  - domain: string (optional)
+
+Outputs:
+  - profile:
+      name: string
+      industry: string
+      size: string
+      funding: string
+      tech_stack: [string]
+      recent_news: [news items]
+
+Dependencies:
+  - requests, beautifulsoup4
+"""
+
+# Requirements: requests, beautifulsoup4
+
+import json
+from typing import Any
+from dataclasses import dataclass, asdict
+from datetime import datetime
+
+
+@dataclass
+class NewsItem:
+    title: str
+    date: str
+    source: str
+    url: str
+    summary: str
+
+
+@dataclass
+class CompanyProfile:
+    name: str
+    domain: str
+    industry: str
+    size: str
+    location: str
+    founded: str
+    funding: str
+    tech_stack: list[str]
+    recent_news: list[dict]
+    competitors: list[str]
+    description: str
+
+
+def search_company_info(company_name: str, domain: str = None) -> dict:
+    """
+    Search for basic company information.
+    In production, this would call APIs like Clearbit, Crunchbase, etc.
+    """
+    # TODO: Implement actual API calls
+    # Placeholder return structure
+    return {
+        "name": company_name,
+        "domain": domain or f"{company_name.lower().replace(' ', '')}.com",
+        "industry": "Technology",  # Would come from API
+        "size": "Unknown",
+        "location": "Unknown",
+        "founded": "Unknown",
+        "description": f"Information about {company_name}"
+    }
+
+
+def search_funding_info(company_name: str) -> dict:
+    """
+    Search for funding information.
+    In production, would call Crunchbase, PitchBook, etc.
+    """
+    # TODO: Implement actual API calls
+    return {
+        "total_funding": "Unknown",
+        "last_round": "Unknown",
+        "last_round_date": "Unknown",
+        "investors": []
+    }
+
+
+def search_tech_stack(domain: str) -> list[str]:
+    """
+    Detect technology stack.
+    In production, would call BuiltWith, Wappalyzer, etc.
+    """
+    # TODO: Implement actual API calls
+    return []
+
+
+def search_recent_news(company_name: str, days: int = 90) -> list[dict]:
+    """
+    Search for recent news about the company.
+    In production, would call news APIs.
+    """
+    # TODO: Implement actual API calls
+    return []
+
+
+def main(
+    company_name: str,
+    domain: str = None
+) -> dict[str, Any]:
+    """
+    Aggregate company data from multiple sources.
+
+    Args:
+        company_name: Company name to research
+        domain: Company domain (optional, will be inferred)
+
+    Returns:
+        dict with company profile including industry, size, funding, tech stack, news
+    """
+    # Get basic company info
+    basic_info = search_company_info(company_name, domain)
+
+    # Get funding information
+    funding_info = search_funding_info(company_name)
+
+    # Detect tech stack
+    company_domain = basic_info.get("domain", domain)
+    tech_stack = search_tech_stack(company_domain) if company_domain else []
+
+    # Get recent news
+    news = search_recent_news(company_name)
+
+    # Compile profile
+    profile = CompanyProfile(
+        name=basic_info["name"],
+        domain=basic_info["domain"],
+        industry=basic_info["industry"],
+        size=basic_info["size"],
+        location=basic_info["location"],
+        founded=basic_info["founded"],
+        funding=funding_info.get("total_funding", "Unknown"),
+        tech_stack=tech_stack,
+        recent_news=news,
+        competitors=[],  # Would be enriched from industry analysis
+        description=basic_info["description"]
+    )
+
+    return {
+        "profile": asdict(profile),
+        "funding_details": funding_info,
+        "enriched_at": datetime.now().isoformat(),
+        "sources_checked": ["company_info", "funding", "tech_stack", "news"]
+    }
+
+
+if __name__ == "__main__":
+    import sys
+
+    # Example usage
+    result = main(
+        company_name="DataFlow Systems",
+        domain="dataflow.io"
+    )
+    print(json.dumps(result, indent=2))
+FILE:linkedin-parser.py
+#!/usr/bin/env python3
+"""
+linkedin-parser.py - Structure LinkedIn profile data
+
+Inputs:
+  - profile_url: string
+  - or name + company: strings
+
+Outputs:
+  - contact:
+      name: string
+      title: string
+      tenure: string
+      previous_roles: [role objects]
+      mutual_connections: [string]
+      recent_activity: [post summaries]
+
+Dependencies:
+  - requests
+"""
+
+# Requirements: requests
+
+import json
+from typing import Any
+from dataclasses import dataclass, asdict
+from datetime import datetime
+
+
+@dataclass
+class PreviousRole:
+    title: str
+    company: str
+    duration: str
+    description: str
+
+
+@dataclass
+class RecentPost:
+    date: str
+    content_preview: str
+    engagement: int
+    topic: str
+
+
+@dataclass
+class ContactProfile:
+    name: str
+    title: str
+    company: str
+    location: str
+    tenure: str
+    previous_roles: list[dict]
+    education: list[str]
+    mutual_connections: list[str]
+    recent_activity: list[dict]
+    profile_url: str
+    headline: str
+
+
+def search_linkedin_profile(name: str = None, company: str = None, profile_url: str = None) -> dict:
+    """
+    Search for LinkedIn profile information.
+    In production, would use LinkedIn API or Sales Navigator.
+    """
+    # TODO: Implement actual LinkedIn API integration
+    # Note: LinkedIn's API has strict terms of service
+
+    return {
+        "found": False,
+        "name": name or "Unknown",
+        "title": "Unknown",
+        "company": company or "Unknown",
+        "location": "Unknown",
+        "headline": "",
+        "tenure": "Unknown",
+        "profile_url": profile_url or ""
+    }
+
+
+def get_career_history(profile_data: dict) -> list[dict]:
+    """
+    Extract career history from profile.
+    """
+    # TODO: Implement career extraction
+    return []
+
+
+def get_mutual_connections(profile_data: dict, user_network: list = None) -> list[str]:
+    """
+    Find mutual connections.
+    """
+    # TODO: Implement mutual connection detection
+    return []
+
+
+def get_recent_activity(profile_data: dict, days: int = 30) -> list[dict]:
+    """
+    Get recent posts and activity.
+    """
+    # TODO: Implement activity extraction
+    return []
+
+
+def main(
+    name: str = None,
+    company: str = None,
+    profile_url: str = None
+) -> dict[str, Any]:
+    """
+    Structure LinkedIn profile data for sales prep.
+
+    Args:
+        name: Person's name
+        company: Company they work at
+        profile_url: Direct LinkedIn profile URL
+
+    Returns:
+        dict with structured contact profile
+    """
+    if not profile_url and not (name and company):
+        return {"error": "Provide either profile_url or name + company"}
+
+    # Search for profile
+    profile_data = search_linkedin_profile(
+        name=name,
+        company=company,
+        profile_url=profile_url
+    )
+
+    if not profile_data.get("found"):
+        return {
+            "found": False,
+            "name": name or "Unknown",
+            "company": company or "Unknown",
+            "message": "Profile not found or limited access",
+            "suggestions": [
+                "Try searching directly on LinkedIn",
+                "Check for alternative spellings",
+                "Verify the person still works at this company"
+            ]
+        }
+
+    # Get career history
+    previous_roles = get_career_history(profile_data)
+
+    # Find mutual connections
+    mutual_connections = get_mutual_connections(profile_data)
+
+    # Get recent activity
+    recent_activity = get_recent_activity(profile_data)
+
+    # Compile contact profile
+    contact = ContactProfile(
+        name=profile_data["name"],
+        title=profile_data["title"],
+        company=profile_data["company"],
+        location=profile_data["location"],
+        tenure=profile_data["tenure"],
+        previous_roles=previous_roles,
+        education=[],  # Would be extracted from profile
+        mutual_connections=mutual_connections,
+        recent_activity=recent_activity,
+        profile_url=profile_data["profile_url"],
+        headline=profile_data["headline"]
+    )
+
+    return {
+        "found": True,
+        "contact": asdict(contact),
+        "research_date": datetime.now().isoformat(),
+        "data_completeness": calculate_completeness(contact)
+    }
+
+
+def calculate_completeness(contact: ContactProfile) -> dict:
+    """Calculate how complete the profile data is."""
+    fields = {
+        "basic_info": bool(contact.name and contact.title and contact.company),
+        "career_history": len(contact.previous_roles) > 0,
+        "mutual_connections": len(contact.mutual_connections) > 0,
+        "recent_activity": len(contact.recent_activity) > 0,
+        "education": len(contact.education) > 0
+    }
+
+    complete_count = sum(fields.values())
+    return {
+        "fields": fields,
+        "score": f"{complete_count}/{len(fields)}",
+        "percentage": int((complete_count / len(fields)) * 100)
+    }
+
+
+if __name__ == "__main__":
+    import sys
+
+    # Example usage
+    result = main(
+        name="Sarah Chen",
+        company="DataFlow Systems"
+    )
+    print(json.dumps(result, indent=2))
+FILE:priority-scorer.py
+#!/usr/bin/env python3
+"""
+priority-scorer.py - Calculate and rank prospect priorities
+
+Inputs:
+  - prospects: [prospect objects with signals]
+  - weights: {deal_size, timing, warmth, signals}
+
+Outputs:
+  - ranked: [prospects with scores and reasoning]
+
+Dependencies:
+  - (none - pure Python)
+"""
+
+import json
+from typing import Any
+from dataclasses import dataclass
+
+
+# Default scoring weights
+DEFAULT_WEIGHTS = {
+    "deal_size": 0.25,
+    "timing": 0.30,
+    "warmth": 0.20,
+    "signals": 0.25
+}
+
+# Signal score mapping
+SIGNAL_SCORES = {
+    # High-intent signals
+    "recent_funding": 10,
+    "leadership_change": 8,
+    "job_postings_relevant": 9,
+    "expansion_news": 7,
+    "competitor_mention": 6,
+
+    # Medium-intent signals
+    "general_hiring": 4,
+    "industry_event": 3,
+    "content_engagement": 3,
+
+    # Relationship signals
+    "mutual_connection": 5,
+    "previous_contact": 6,
+    "referred_lead": 8,
+
+    # Negative signals
+    "recent_layoffs": -3,
+    "budget_freeze_mentioned": -5,
+    "competitor_selected": -7,
+}
+
+
+@dataclass
+class ScoredProspect:
+    company: str
+    contact: str
+    call_time: str
+    raw_score: float
+    normalized_score: int
+    priority_rank: int
+    score_breakdown: dict
+    reasoning: str
+    is_followup: bool
+
+
+def score_deal_size(prospect: dict) -> tuple[float, str]:
+    """Score based on estimated deal size."""
+    size_indicators = prospect.get("size_indicators", {})
+
+    employee_count = size_indicators.get("employees", 0)
+    revenue_estimate = size_indicators.get("revenue", 0)
+
+    # Simple scoring based on company size
+    if employee_count > 1000 or revenue_estimate > 100_000_000:
+        return 10.0, "Enterprise-scale opportunity"
+    elif employee_count > 200 or revenue_estimate > 20_000_000:
+        return 7.0, "Mid-market opportunity"
+    elif employee_count > 50:
+        return 5.0, "SMB opportunity"
+    else:
+        return 3.0, "Small business"
+
+
+def score_timing(prospect: dict) -> tuple[float, str]:
+    """Score based on timing signals."""
+    timing_signals = prospect.get("timing_signals", [])
+
+    score = 5.0  # Base score
+    reasons = []
+
+    for signal in timing_signals:
+        if signal == "budget_cycle_q4":
+            score += 3
+            reasons.append("Q4 budget planning")
+        elif signal == "contract_expiring":
+            score += 4
+            reasons.append("Contract expiring soon")
+        elif signal == "active_evaluation":
+            score += 5
+            reasons.append("Actively evaluating")
+        elif signal == "just_funded":
+            score += 3
+            reasons.append("Recently funded")
+
+    return min(score, 10.0), "; ".join(reasons) if reasons else "Standard timing"
+
+
+def score_warmth(prospect: dict) -> tuple[float, str]:
+    """Score based on relationship warmth."""
+    relationship = prospect.get("relationship", {})
+
+    if relationship.get("is_followup"):
+        last_outcome = relationship.get("last_outcome", "neutral")
+        if last_outcome == "positive":
+            return 9.0, "Warm follow-up (positive last contact)"
+        elif last_outcome == "neutral":
+            return 7.0, "Follow-up (neutral last contact)"
+        else:
+            return 5.0, "Follow-up (needs re-engagement)"
+
+    if relationship.get("referred"):
+        return 8.0, "Referred lead"
+
+    if relationship.get("mutual_connections", 0) > 0:
+        return 6.0, f"{relationship['mutual_connections']} mutual connections"
+
+    if relationship.get("inbound"):
+        return 7.0, "Inbound interest"
+
+    return 4.0, "Cold outreach"
+
+
+def score_signals(prospect: dict) -> tuple[float, str]:
+    """Score based on buying signals detected."""
+    signals = prospect.get("signals", [])
+
+    total_score = 0
+    signal_reasons = []
+
+    for signal in signals:
+        signal_score = SIGNAL_SCORES.get(signal, 0)
+        total_score += signal_score
+        if signal_score > 0:
+            signal_reasons.append(signal.replace("_", " "))
+
+    # Normalize to 0-10 scale
+    normalized = min(max(total_score / 2, 0), 10)
+
+    reason = f"Signals: {', '.join(signal_reasons)}" if signal_reasons else "No strong signals"
+    return normalized, reason
+
+
+def calculate_priority_score(
+    prospect: dict,
+    weights: dict = None
+) -> ScoredProspect:
+    """Calculate overall priority score for a prospect."""
+    weights = weights or DEFAULT_WEIGHTS
+
+    # Calculate component scores
+    deal_score, deal_reason = score_deal_size(prospect)
+    timing_score, timing_reason = score_timing(prospect)
+    warmth_score, warmth_reason = score_warmth(prospect)
+    signal_score, signal_reason = score_signals(prospect)
+
+    # Weighted total
+    raw_score = (
+        deal_score * weights["deal_size"] +
+        timing_score * weights["timing"] +
+        warmth_score * weights["warmth"] +
+        signal_score * weights["signals"]
+    )
+
+    # Compile reasoning
+    reasons = []
+    if timing_score >= 8:
+        reasons.append(timing_reason)
+    if signal_score >= 7:
+        reasons.append(signal_reason)
+    if warmth_score >= 7:
+        reasons.append(warmth_reason)
+    if deal_score >= 8:
+        reasons.append(deal_reason)
+
+    return ScoredProspect(
+        company=prospect.get("company", "Unknown"),
+        contact=prospect.get("contact", "Unknown"),
+        call_time=prospect.get("call_time", "Unknown"),
+        raw_score=round(raw_score, 2),
+        normalized_score=int(raw_score * 10),
+        priority_rank=0,  # Will be set after sorting
+        score_breakdown={
+            "deal_size": {"score": deal_score, "reason": deal_reason},
+            "timing": {"score": timing_score, "reason": timing_reason},
+            "warmth": {"score": warmth_score, "reason": warmth_reason},
+            "signals": {"score": signal_score, "reason": signal_reason}
+        },
+        reasoning="; ".join(reasons) if reasons else "Standard priority",
+        is_followup=prospect.get("relationship", {}).get("is_followup", False)
+    )
+
+
+def main(
+    prospects: list[dict],
+    weights: dict = None
+) -> dict[str, Any]:
+    """
+    Calculate and rank prospect priorities.
+
+    Args:
+        prospects: List of prospect objects with signals
+        weights: Optional custom weights for scoring components
+
+    Returns:
+        dict with ranked prospects and scoring details
+    """
+    weights = weights or DEFAULT_WEIGHTS
+
+    # Score all prospects
+    scored = [calculate_priority_score(p, weights) for p in prospects]
+
+    # Sort by raw score descending
+    scored.sort(key=lambda x: x.raw_score, reverse=True)
+
+    # Assign ranks
+    for i, prospect in enumerate(scored, 1):
+        prospect.priority_rank = i
+
+    # Convert to dicts for JSON serialization
+    ranked = []
+    for s in scored:
+        ranked.append({
+            "company": s.company,
+            "contact": s.contact,
+            "call_time": s.call_time,
+            "priority_rank": s.priority_rank,
+            "score": s.normalized_score,
+            "reasoning": s.reasoning,
+            "is_followup": s.is_followup,
+            "breakdown": s.score_breakdown
+        })
+
+    return {
+        "ranked": ranked,
+        "weights_used": weights,
+        "total_prospects": len(prospects)
+    }
+
+
+if __name__ == "__main__":
+    import sys
+
+    # Example usage
+    example_prospects = [
+        {
+            "company": "DataFlow Systems",
+            "contact": "Sarah Chen",
+            "call_time": "2pm",
+            "size_indicators": {"employees": 200, "revenue": 25_000_000},
+            "timing_signals": ["just_funded", "active_evaluation"],
+            "signals": ["recent_funding", "job_postings_relevant"],
+            "relationship": {"is_followup": False, "mutual_connections": 2}
+        },
+        {
+            "company": "Acme Manufacturing",
+            "contact": "Tom Bradley",
+            "call_time": "10am",
+            "size_indicators": {"employees": 500},
+            "timing_signals": ["contract_expiring"],
+            "signals": [],
+            "relationship": {"is_followup": True, "last_outcome": "neutral"}
+        },
+        {
+            "company": "FirstRate Financial",
+            "contact": "Linda Thompson",
+            "call_time": "4pm",
+            "size_indicators": {"employees": 300},
+            "timing_signals": [],
+            "signals": [],
+            "relationship": {"is_followup": False}
+        }
+    ]
+
+    result = main(prospects=example_prospects)
+    print(json.dumps(result, indent=2))
+FILE:research-checklist.md
+# Prospect Research Checklist
+
+## Company Research
+
+### Basic Information
+- [ ] Company name (verify spelling)
+- [ ] Industry/vertical
+- [ ] Headquarters location
+- [ ] Employee count (LinkedIn, website)
+- [ ] Revenue estimate (if available)
+- [ ] Founded date
+- [ ] Funding stage/history
+
+### Recent News (Last 90 Days)
+- [ ] Funding announcements
+- [ ] Acquisitions or mergers
+- [ ] Leadership changes
+- [ ] Product launches
+- [ ] Major customer wins
+- [ ] Press mentions
+- [ ] Earnings/financial news
+
+### Digital Footprint
+- [ ] Website review
+- [ ] Blog/content topics
+- [ ] Social media presence
+- [ ] Job postings (careers page + LinkedIn)
+- [ ] Tech stack (BuiltWith, job postings)
+
+### Competitive Landscape
+- [ ] Known competitors
+- [ ] Market position
+- [ ] Differentiators claimed
+- [ ] Recent competitive moves
+
+### Pain Point Indicators
+- [ ] Glassdoor reviews (themes)
+- [ ] G2/Capterra reviews (if B2B)
+- [ ] Social media complaints
+- [ ] Job posting patterns
+
+## Contact Research
+
+### Professional Profile
+- [ ] Current title
+- [ ] Time in role
+- [ ] Time at company
+- [ ] Previous companies
+- [ ] Previous roles
+- [ ] Education
+
+### Decision Authority
+- [ ] Reports to whom
+- [ ] Team size (if manager)
+- [ ] Budget authority (inferred)
+- [ ] Buying involvement history
+
+### Engagement Hooks
+- [ ] Recent LinkedIn posts
+- [ ] Published articles
+- [ ] Podcast appearances
+- [ ] Conference talks
+- [ ] Mutual connections
+- [ ] Shared interests/groups
+
+### Communication Style
+- [ ] Post tone (formal/casual)
+- [ ] Topics they engage with
+- [ ] Response patterns
+
+## CRM Check (If Available)
+
+- [ ] Any prior touchpoints
+- [ ] Previous opportunities
+- [ ] Related contacts at company
+- [ ] Notes from colleagues
+- [ ] Email engagement history
+
+## Time-Based Research Depth
+
+| Time Available | Research Depth |
+|----------------|----------------|
+| 5 minutes | Company basics + contact title only |
+| 15 minutes | + Recent news + LinkedIn profile |
+| 30 minutes | + Pain point signals + engagement hooks |
+| 60 minutes | Full checklist + competitive analysis |
+FILE:signal-indicators.md
+# Signal Indicators Reference
+
+## High-Intent Signals
+
+### Job Postings
+- **3+ relevant roles posted** = Active initiative, budget allocated
+- **Senior hire in your domain** = Strategic priority
+- **Urgency language ("ASAP", "immediate")** = Pain is acute
+- **Specific tool mentioned** = Competitor or category awareness
+
+### Financial Events
+- **Series B+ funding** = Growth capital, buying power
+- **IPO preparation** = Operational maturity needed
+- **Acquisition announced** = Integration challenges coming
+- **Revenue milestone PR** = Budget available
+
+### Leadership Changes
+- **New CXO in your domain** = 90-day priority setting
+- **New CRO/CMO** = Tech stack evaluation likely
+- **Founder transition to CEO** = Professionalizing operations
+
+## Medium-Intent Signals
+
+### Expansion Signals
+- **New office opening** = Infrastructure needs
+- **International expansion** = Localization, compliance
+- **New product launch** = Scaling challenges
+- **Major customer win** = Delivery pressure
+
+### Technology Signals
+- **RFP published** = Active buying process
+- **Vendor review mentioned** = Comparison shopping
+- **Tech stack change** = Integration opportunity
+- **Legacy system complaints** = Modernization need
+
+### Content Signals
+- **Blog post on your topic** = Educating themselves
+- **Webinar attendance** = Interest confirmed
+- **Whitepaper download** = Problem awareness
+- **Conference speaking** = Thought leadership, visibility
+
+## Low-Intent Signals (Nurture)
+
+### General Activity
+- **Industry event attendance** = Market participant
+- **Generic hiring** = Company growing
+- **Positive press** = Healthy company
+- **Social media activity** = Engaged leadership
+
+## Signal Scoring
+
+| Signal Type | Score | Action |
+|-------------|-------|--------|
+| Job posting (relevant) | +3 | Prioritize outreach |
+| Recent funding | +3 | Reference in conversation |
+| Leadership change | +2 | Time-sensitive opportunity |
+| Expansion news | +2 | Growth angle |
+| Negative reviews | +2 | Pain point angle |
+| Content engagement | +1 | Nurture track |
+| No signals | 0 | Discovery focus |
+```
+
+</details>
+

prompts.csv

@@ -52186,3 +52186,877 @@ This skill provides expert guidance on analyzing financial statements...
 - [ ] Consistent output format
 - [ ] Resistant to jailbreaks
 ```",FALSE,TEXT,TomsTools11
+Sales Research,"---
+name: sales-research
+description: This skill provides methodology and best practices for researching sales prospects.
+---
+
+# Sales Research
+
+## Overview
+
+This skill provides methodology and best practices for researching sales prospects. It covers company research, contact profiling, and signal detection to surface actionable intelligence.
+
+## Usage
+
+The company-researcher and contact-researcher sub-agents reference this skill when:
+- Researching new prospects
+- Finding company information
+- Profiling individual contacts
+- Detecting buying signals
+
+## Research Methodology
+
+### Company Research Checklist
+
+1. **Basic Profile**
+   - Company name, industry, size (employees, revenue)
+   - Headquarters and key locations
+   - Founded date, growth stage
+
+2. **Recent Developments**
+   - Funding announcements (last 12 months)
+   - M&A activity
+   - Leadership changes
+   - Product launches
+
+3. **Tech Stack**
+   - Known technologies (BuiltWith, StackShare)
+   - Job postings mentioning tools
+   - Integration partnerships
+
+4. **Signals**
+   - Job postings (scaling = opportunity)
+   - Glassdoor reviews (pain points)
+   - News mentions (context)
+   - Social media activity
+
+### Contact Research Checklist
+
+1. **Professional Background**
+   - Current role and tenure
+   - Previous companies and roles
+   - Education
+
+2. **Influence Indicators**
+   - Reporting structure
+   - Decision-making authority
+   - Budget ownership
+
+3. **Engagement Hooks**
+   - Recent LinkedIn posts
+   - Published articles
+   - Speaking engagements
+   - Mutual connections
+
+## Resources
+
+- `resources/signal-indicators.md` - Taxonomy of buying signals
+- `resources/research-checklist.md` - Complete research checklist
+
+## Scripts
+
+- `scripts/company-enricher.py` - Aggregate company data from multiple sources
+- `scripts/linkedin-parser.py` - Structure LinkedIn profile data
+FILE:company-enricher.py
+#!/usr/bin/env python3
+""""""
+company-enricher.py - Aggregate company data from multiple sources
+
+Inputs:
+  - company_name: string
+  - domain: string (optional)
+
+Outputs:
+  - profile:
+      name: string
+      industry: string
+      size: string
+      funding: string
+      tech_stack: [string]
+      recent_news: [news items]
+
+Dependencies:
+  - requests, beautifulsoup4
+""""""
+
+# Requirements: requests, beautifulsoup4
+
+import json
+from typing import Any
+from dataclasses import dataclass, asdict
+from datetime import datetime
+
+
+@dataclass
+class NewsItem:
+    title: str
+    date: str
+    source: str
+    url: str
+    summary: str
+
+
+@dataclass
+class CompanyProfile:
+    name: str
+    domain: str
+    industry: str
+    size: str
+    location: str
+    founded: str
+    funding: str
+    tech_stack: list[str]
+    recent_news: list[dict]
+    competitors: list[str]
+    description: str
+
+
+def search_company_info(company_name: str, domain: str = None) -> dict:
+    """"""
+    Search for basic company information.
+    In production, this would call APIs like Clearbit, Crunchbase, etc.
+    """"""
+    # TODO: Implement actual API calls
+    # Placeholder return structure
+    return {
+        ""name"": company_name,
+        ""domain"": domain or f""{company_name.lower().replace(' ', '')}.com"",
+        ""industry"": ""Technology"",  # Would come from API
+        ""size"": ""Unknown"",
+        ""location"": ""Unknown"",
+        ""founded"": ""Unknown"",
+        ""description"": f""Information about {company_name}""
+    }
+
+
+def search_funding_info(company_name: str) -> dict:
+    """"""
+    Search for funding information.
+    In production, would call Crunchbase, PitchBook, etc.
+    """"""
+    # TODO: Implement actual API calls
+    return {
+        ""total_funding"": ""Unknown"",
+        ""last_round"": ""Unknown"",
+        ""last_round_date"": ""Unknown"",
+        ""investors"": []
+    }
+
+
+def search_tech_stack(domain: str) -> list[str]:
+    """"""
+    Detect technology stack.
+    In production, would call BuiltWith, Wappalyzer, etc.
+    """"""
+    # TODO: Implement actual API calls
+    return []
+
+
+def search_recent_news(company_name: str, days: int = 90) -> list[dict]:
+    """"""
+    Search for recent news about the company.
+    In production, would call news APIs.
+    """"""
+    # TODO: Implement actual API calls
+    return []
+
+
+def main(
+    company_name: str,
+    domain: str = None
+) -> dict[str, Any]:
+    """"""
+    Aggregate company data from multiple sources.
+
+    Args:
+        company_name: Company name to research
+        domain: Company domain (optional, will be inferred)
+
+    Returns:
+        dict with company profile including industry, size, funding, tech stack, news
+    """"""
+    # Get basic company info
+    basic_info = search_company_info(company_name, domain)
+
+    # Get funding information
+    funding_info = search_funding_info(company_name)
+
+    # Detect tech stack
+    company_domain = basic_info.get(""domain"", domain)
+    tech_stack = search_tech_stack(company_domain) if company_domain else []
+
+    # Get recent news
+    news = search_recent_news(company_name)
+
+    # Compile profile
+    profile = CompanyProfile(
+        name=basic_info[""name""],
+        domain=basic_info[""domain""],
+        industry=basic_info[""industry""],
+        size=basic_info[""size""],
+        location=basic_info[""location""],
+        founded=basic_info[""founded""],
+        funding=funding_info.get(""total_funding"", ""Unknown""),
+        tech_stack=tech_stack,
+        recent_news=news,
+        competitors=[],  # Would be enriched from industry analysis
+        description=basic_info[""description""]
+    )
+
+    return {
+        ""profile"": asdict(profile),
+        ""funding_details"": funding_info,
+        ""enriched_at"": datetime.now().isoformat(),
+        ""sources_checked"": [""company_info"", ""funding"", ""tech_stack"", ""news""]
+    }
+
+
+if __name__ == ""__main__"":
+    import sys
+
+    # Example usage
+    result = main(
+        company_name=""DataFlow Systems"",
+        domain=""dataflow.io""
+    )
+    print(json.dumps(result, indent=2))
+FILE:linkedin-parser.py
+#!/usr/bin/env python3
+""""""
+linkedin-parser.py - Structure LinkedIn profile data
+
+Inputs:
+  - profile_url: string
+  - or name + company: strings
+
+Outputs:
+  - contact:
+      name: string
+      title: string
+      tenure: string
+      previous_roles: [role objects]
+      mutual_connections: [string]
+      recent_activity: [post summaries]
+
+Dependencies:
+  - requests
+""""""
+
+# Requirements: requests
+
+import json
+from typing import Any
+from dataclasses import dataclass, asdict
+from datetime import datetime
+
+
+@dataclass
+class PreviousRole:
+    title: str
+    company: str
+    duration: str
+    description: str
+
+
+@dataclass
+class RecentPost:
+    date: str
+    content_preview: str
+    engagement: int
+    topic: str
+
+
+@dataclass
+class ContactProfile:
+    name: str
+    title: str
+    company: str
+    location: str
+    tenure: str
+    previous_roles: list[dict]
+    education: list[str]
+    mutual_connections: list[str]
+    recent_activity: list[dict]
+    profile_url: str
+    headline: str
+
+
+def search_linkedin_profile(name: str = None, company: str = None, profile_url: str = None) -> dict:
+    """"""
+    Search for LinkedIn profile information.
+    In production, would use LinkedIn API or Sales Navigator.
+    """"""
+    # TODO: Implement actual LinkedIn API integration
+    # Note: LinkedIn's API has strict terms of service
+
+    return {
+        ""found"": False,
+        ""name"": name or ""Unknown"",
+        ""title"": ""Unknown"",
+        ""company"": company or ""Unknown"",
+        ""location"": ""Unknown"",
+        ""headline"": """",
+        ""tenure"": ""Unknown"",
+        ""profile_url"": profile_url or """"
+    }
+
+
+def get_career_history(profile_data: dict) -> list[dict]:
+    """"""
+    Extract career history from profile.
+    """"""
+    # TODO: Implement career extraction
+    return []
+
+
+def get_mutual_connections(profile_data: dict, user_network: list = None) -> list[str]:
+    """"""
+    Find mutual connections.
+    """"""
+    # TODO: Implement mutual connection detection
+    return []
+
+
+def get_recent_activity(profile_data: dict, days: int = 30) -> list[dict]:
+    """"""
+    Get recent posts and activity.
+    """"""
+    # TODO: Implement activity extraction
+    return []
+
+
+def main(
+    name: str = None,
+    company: str = None,
+    profile_url: str = None
+) -> dict[str, Any]:
+    """"""
+    Structure LinkedIn profile data for sales prep.
+
+    Args:
+        name: Person's name
+        company: Company they work at
+        profile_url: Direct LinkedIn profile URL
+
+    Returns:
+        dict with structured contact profile
+    """"""
+    if not profile_url and not (name and company):
+        return {""error"": ""Provide either profile_url or name + company""}
+
+    # Search for profile
+    profile_data = search_linkedin_profile(
+        name=name,
+        company=company,
+        profile_url=profile_url
+    )
+
+    if not profile_data.get(""found""):
+        return {
+            ""found"": False,
+            ""name"": name or ""Unknown"",
+            ""company"": company or ""Unknown"",
+            ""message"": ""Profile not found or limited access"",
+            ""suggestions"": [
+                ""Try searching directly on LinkedIn"",
+                ""Check for alternative spellings"",
+                ""Verify the person still works at this company""
+            ]
+        }
+
+    # Get career history
+    previous_roles = get_career_history(profile_data)
+
+    # Find mutual connections
+    mutual_connections = get_mutual_connections(profile_data)
+
+    # Get recent activity
+    recent_activity = get_recent_activity(profile_data)
+
+    # Compile contact profile
+    contact = ContactProfile(
+        name=profile_data[""name""],
+        title=profile_data[""title""],
+        company=profile_data[""company""],
+        location=profile_data[""location""],
+        tenure=profile_data[""tenure""],
+        previous_roles=previous_roles,
+        education=[],  # Would be extracted from profile
+        mutual_connections=mutual_connections,
+        recent_activity=recent_activity,
+        profile_url=profile_data[""profile_url""],
+        headline=profile_data[""headline""]
+    )
+
+    return {
+        ""found"": True,
+        ""contact"": asdict(contact),
+        ""research_date"": datetime.now().isoformat(),
+        ""data_completeness"": calculate_completeness(contact)
+    }
+
+
+def calculate_completeness(contact: ContactProfile) -> dict:
+    """"""Calculate how complete the profile data is.""""""
+    fields = {
+        ""basic_info"": bool(contact.name and contact.title and contact.company),
+        ""career_history"": len(contact.previous_roles) > 0,
+        ""mutual_connections"": len(contact.mutual_connections) > 0,
+        ""recent_activity"": len(contact.recent_activity) > 0,
+        ""education"": len(contact.education) > 0
+    }
+
+    complete_count = sum(fields.values())
+    return {
+        ""fields"": fields,
+        ""score"": f""{complete_count}/{len(fields)}"",
+        ""percentage"": int((complete_count / len(fields)) * 100)
+    }
+
+
+if __name__ == ""__main__"":
+    import sys
+
+    # Example usage
+    result = main(
+        name=""Sarah Chen"",
+        company=""DataFlow Systems""
+    )
+    print(json.dumps(result, indent=2))
+FILE:priority-scorer.py
+#!/usr/bin/env python3
+""""""
+priority-scorer.py - Calculate and rank prospect priorities
+
+Inputs:
+  - prospects: [prospect objects with signals]
+  - weights: {deal_size, timing, warmth, signals}
+
+Outputs:
+  - ranked: [prospects with scores and reasoning]
+
+Dependencies:
+  - (none - pure Python)
+""""""
+
+import json
+from typing import Any
+from dataclasses import dataclass
+
+
+# Default scoring weights
+DEFAULT_WEIGHTS = {
+    ""deal_size"": 0.25,
+    ""timing"": 0.30,
+    ""warmth"": 0.20,
+    ""signals"": 0.25
+}
+
+# Signal score mapping
+SIGNAL_SCORES = {
+    # High-intent signals
+    ""recent_funding"": 10,
+    ""leadership_change"": 8,
+    ""job_postings_relevant"": 9,
+    ""expansion_news"": 7,
+    ""competitor_mention"": 6,
+
+    # Medium-intent signals
+    ""general_hiring"": 4,
+    ""industry_event"": 3,
+    ""content_engagement"": 3,
+
+    # Relationship signals
+    ""mutual_connection"": 5,
+    ""previous_contact"": 6,
+    ""referred_lead"": 8,
+
+    # Negative signals
+    ""recent_layoffs"": -3,
+    ""budget_freeze_mentioned"": -5,
+    ""competitor_selected"": -7,
+}
+
+
+@dataclass
+class ScoredProspect:
+    company: str
+    contact: str
+    call_time: str
+    raw_score: float
+    normalized_score: int
+    priority_rank: int
+    score_breakdown: dict
+    reasoning: str
+    is_followup: bool
+
+
+def score_deal_size(prospect: dict) -> tuple[float, str]:
+    """"""Score based on estimated deal size.""""""
+    size_indicators = prospect.get(""size_indicators"", {})
+
+    employee_count = size_indicators.get(""employees"", 0)
+    revenue_estimate = size_indicators.get(""revenue"", 0)
+
+    # Simple scoring based on company size
+    if employee_count > 1000 or revenue_estimate > 100_000_000:
+        return 10.0, ""Enterprise-scale opportunity""
+    elif employee_count > 200 or revenue_estimate > 20_000_000:
+        return 7.0, ""Mid-market opportunity""
+    elif employee_count > 50:
+        return 5.0, ""SMB opportunity""
+    else:
+        return 3.0, ""Small business""
+
+
+def score_timing(prospect: dict) -> tuple[float, str]:
+    """"""Score based on timing signals.""""""
+    timing_signals = prospect.get(""timing_signals"", [])
+
+    score = 5.0  # Base score
+    reasons = []
+
+    for signal in timing_signals:
+        if signal == ""budget_cycle_q4"":
+            score += 3
+            reasons.append(""Q4 budget planning"")
+        elif signal == ""contract_expiring"":
+            score += 4
+            reasons.append(""Contract expiring soon"")
+        elif signal == ""active_evaluation"":
+            score += 5
+            reasons.append(""Actively evaluating"")
+        elif signal == ""just_funded"":
+            score += 3
+            reasons.append(""Recently funded"")
+
+    return min(score, 10.0), ""; "".join(reasons) if reasons else ""Standard timing""
+
+
+def score_warmth(prospect: dict) -> tuple[float, str]:
+    """"""Score based on relationship warmth.""""""
+    relationship = prospect.get(""relationship"", {})
+
+    if relationship.get(""is_followup""):
+        last_outcome = relationship.get(""last_outcome"", ""neutral"")
+        if last_outcome == ""positive"":
+            return 9.0, ""Warm follow-up (positive last contact)""
+        elif last_outcome == ""neutral"":
+            return 7.0, ""Follow-up (neutral last contact)""
+        else:
+            return 5.0, ""Follow-up (needs re-engagement)""
+
+    if relationship.get(""referred""):
+        return 8.0, ""Referred lead""
+
+    if relationship.get(""mutual_connections"", 0) > 0:
+        return 6.0, f""{relationship['mutual_connections']} mutual connections""
+
+    if relationship.get(""inbound""):
+        return 7.0, ""Inbound interest""
+
+    return 4.0, ""Cold outreach""
+
+
+def score_signals(prospect: dict) -> tuple[float, str]:
+    """"""Score based on buying signals detected.""""""
+    signals = prospect.get(""signals"", [])
+
+    total_score = 0
+    signal_reasons = []
+
+    for signal in signals:
+        signal_score = SIGNAL_SCORES.get(signal, 0)
+        total_score += signal_score
+        if signal_score > 0:
+            signal_reasons.append(signal.replace(""_"", "" ""))
+
+    # Normalize to 0-10 scale
+    normalized = min(max(total_score / 2, 0), 10)
+
+    reason = f""Signals: {', '.join(signal_reasons)}"" if signal_reasons else ""No strong signals""
+    return normalized, reason
+
+
+def calculate_priority_score(
+    prospect: dict,
+    weights: dict = None
+) -> ScoredProspect:
+    """"""Calculate overall priority score for a prospect.""""""
+    weights = weights or DEFAULT_WEIGHTS
+
+    # Calculate component scores
+    deal_score, deal_reason = score_deal_size(prospect)
+    timing_score, timing_reason = score_timing(prospect)
+    warmth_score, warmth_reason = score_warmth(prospect)
+    signal_score, signal_reason = score_signals(prospect)
+
+    # Weighted total
+    raw_score = (
+        deal_score * weights[""deal_size""] +
+        timing_score * weights[""timing""] +
+        warmth_score * weights[""warmth""] +
+        signal_score * weights[""signals""]
+    )
+
+    # Compile reasoning
+    reasons = []
+    if timing_score >= 8:
+        reasons.append(timing_reason)
+    if signal_score >= 7:
+        reasons.append(signal_reason)
+    if warmth_score >= 7:
+        reasons.append(warmth_reason)
+    if deal_score >= 8:
+        reasons.append(deal_reason)
+
+    return ScoredProspect(
+        company=prospect.get(""company"", ""Unknown""),
+        contact=prospect.get(""contact"", ""Unknown""),
+        call_time=prospect.get(""call_time"", ""Unknown""),
+        raw_score=round(raw_score, 2),
+        normalized_score=int(raw_score * 10),
+        priority_rank=0,  # Will be set after sorting
+        score_breakdown={
+            ""deal_size"": {""score"": deal_score, ""reason"": deal_reason},
+            ""timing"": {""score"": timing_score, ""reason"": timing_reason},
+            ""warmth"": {""score"": warmth_score, ""reason"": warmth_reason},
+            ""signals"": {""score"": signal_score, ""reason"": signal_reason}
+        },
+        reasoning=""; "".join(reasons) if reasons else ""Standard priority"",
+        is_followup=prospect.get(""relationship"", {}).get(""is_followup"", False)
+    )
+
+
+def main(
+    prospects: list[dict],
+    weights: dict = None
+) -> dict[str, Any]:
+    """"""
+    Calculate and rank prospect priorities.
+
+    Args:
+        prospects: List of prospect objects with signals
+        weights: Optional custom weights for scoring components
+
+    Returns:
+        dict with ranked prospects and scoring details
+    """"""
+    weights = weights or DEFAULT_WEIGHTS
+
+    # Score all prospects
+    scored = [calculate_priority_score(p, weights) for p in prospects]
+
+    # Sort by raw score descending
+    scored.sort(key=lambda x: x.raw_score, reverse=True)
+
+    # Assign ranks
+    for i, prospect in enumerate(scored, 1):
+        prospect.priority_rank = i
+
+    # Convert to dicts for JSON serialization
+    ranked = []
+    for s in scored:
+        ranked.append({
+            ""company"": s.company,
+            ""contact"": s.contact,
+            ""call_time"": s.call_time,
+            ""priority_rank"": s.priority_rank,
+            ""score"": s.normalized_score,
+            ""reasoning"": s.reasoning,
+            ""is_followup"": s.is_followup,
+            ""breakdown"": s.score_breakdown
+        })
+
+    return {
+        ""ranked"": ranked,
+        ""weights_used"": weights,
+        ""total_prospects"": len(prospects)
+    }
+
+
+if __name__ == ""__main__"":
+    import sys
+
+    # Example usage
+    example_prospects = [
+        {
+            ""company"": ""DataFlow Systems"",
+            ""contact"": ""Sarah Chen"",
+            ""call_time"": ""2pm"",
+            ""size_indicators"": {""employees"": 200, ""revenue"": 25_000_000},
+            ""timing_signals"": [""just_funded"", ""active_evaluation""],
+            ""signals"": [""recent_funding"", ""job_postings_relevant""],
+            ""relationship"": {""is_followup"": False, ""mutual_connections"": 2}
+        },
+        {
+            ""company"": ""Acme Manufacturing"",
+            ""contact"": ""Tom Bradley"",
+            ""call_time"": ""10am"",
+            ""size_indicators"": {""employees"": 500},
+            ""timing_signals"": [""contract_expiring""],
+            ""signals"": [],
+            ""relationship"": {""is_followup"": True, ""last_outcome"": ""neutral""}
+        },
+        {
+            ""company"": ""FirstRate Financial"",
+            ""contact"": ""Linda Thompson"",
+            ""call_time"": ""4pm"",
+            ""size_indicators"": {""employees"": 300},
+            ""timing_signals"": [],
+            ""signals"": [],
+            ""relationship"": {""is_followup"": False}
+        }
+    ]
+
+    result = main(prospects=example_prospects)
+    print(json.dumps(result, indent=2))
+FILE:research-checklist.md
+# Prospect Research Checklist
+
+## Company Research
+
+### Basic Information
+- [ ] Company name (verify spelling)
+- [ ] Industry/vertical
+- [ ] Headquarters location
+- [ ] Employee count (LinkedIn, website)
+- [ ] Revenue estimate (if available)
+- [ ] Founded date
+- [ ] Funding stage/history
+
+### Recent News (Last 90 Days)
+- [ ] Funding announcements
+- [ ] Acquisitions or mergers
+- [ ] Leadership changes
+- [ ] Product launches
+- [ ] Major customer wins
+- [ ] Press mentions
+- [ ] Earnings/financial news
+
+### Digital Footprint
+- [ ] Website review
+- [ ] Blog/content topics
+- [ ] Social media presence
+- [ ] Job postings (careers page + LinkedIn)
+- [ ] Tech stack (BuiltWith, job postings)
+
+### Competitive Landscape
+- [ ] Known competitors
+- [ ] Market position
+- [ ] Differentiators claimed
+- [ ] Recent competitive moves
+
+### Pain Point Indicators
+- [ ] Glassdoor reviews (themes)
+- [ ] G2/Capterra reviews (if B2B)
+- [ ] Social media complaints
+- [ ] Job posting patterns
+
+## Contact Research
+
+### Professional Profile
+- [ ] Current title
+- [ ] Time in role
+- [ ] Time at company
+- [ ] Previous companies
+- [ ] Previous roles
+- [ ] Education
+
+### Decision Authority
+- [ ] Reports to whom
+- [ ] Team size (if manager)
+- [ ] Budget authority (inferred)
+- [ ] Buying involvement history
+
+### Engagement Hooks
+- [ ] Recent LinkedIn posts
+- [ ] Published articles
+- [ ] Podcast appearances
+- [ ] Conference talks
+- [ ] Mutual connections
+- [ ] Shared interests/groups
+
+### Communication Style
+- [ ] Post tone (formal/casual)
+- [ ] Topics they engage with
+- [ ] Response patterns
+
+## CRM Check (If Available)
+
+- [ ] Any prior touchpoints
+- [ ] Previous opportunities
+- [ ] Related contacts at company
+- [ ] Notes from colleagues
+- [ ] Email engagement history
+
+## Time-Based Research Depth
+
+| Time Available | Research Depth |
+|----------------|----------------|
+| 5 minutes | Company basics + contact title only |
+| 15 minutes | + Recent news + LinkedIn profile |
+| 30 minutes | + Pain point signals + engagement hooks |
+| 60 minutes | Full checklist + competitive analysis |
+FILE:signal-indicators.md
+# Signal Indicators Reference
+
+## High-Intent Signals
+
+### Job Postings
+- **3+ relevant roles posted** = Active initiative, budget allocated
+- **Senior hire in your domain** = Strategic priority
+- **Urgency language (""ASAP"", ""immediate"")** = Pain is acute
+- **Specific tool mentioned** = Competitor or category awareness
+
+### Financial Events
+- **Series B+ funding** = Growth capital, buying power
+- **IPO preparation** = Operational maturity needed
+- **Acquisition announced** = Integration challenges coming
+- **Revenue milestone PR** = Budget available
+
+### Leadership Changes
+- **New CXO in your domain** = 90-day priority setting
+- **New CRO/CMO** = Tech stack evaluation likely
+- **Founder transition to CEO** = Professionalizing operations
+
+## Medium-Intent Signals
+
+### Expansion Signals
+- **New office opening** = Infrastructure needs
+- **International expansion** = Localization, compliance
+- **New product launch** = Scaling challenges
+- **Major customer win** = Delivery pressure
+
+### Technology Signals
+- **RFP published** = Active buying process
+- **Vendor review mentioned** = Comparison shopping
+- **Tech stack change** = Integration opportunity
+- **Legacy system complaints** = Modernization need
+
+### Content Signals
+- **Blog post on your topic** = Educating themselves
+- **Webinar attendance** = Interest confirmed
+- **Whitepaper download** = Problem awareness
+- **Conference speaking** = Thought leadership, visibility
+
+## Low-Intent Signals (Nurture)
+
+### General Activity
+- **Industry event attendance** = Market participant
+- **Generic hiring** = Company growing
+- **Positive press** = Healthy company
+- **Social media activity** = Engaged leadership
+
+## Signal Scoring
+
+| Signal Type | Score | Action |
+|-------------|-------|--------|
+| Job posting (relevant) | +3 | Prioritize outreach |
+| Recent funding | +3 | Reference in conversation |
+| Leadership change | +2 | Time-sensitive opportunity |
+| Expansion news | +2 | Growth angle |
+| Negative reviews | +2 | Pain point angle |
+| Content engagement | +1 | Nurture track |
+| No signals | 0 | Discovery focus |",FALSE,TEXT,TomsTools11