# Lead Qualifier > Multi-dimensional lead qualification scoring. Evaluates leads against BANT criteria, firmographic fit, behavioral signals, and intent indicators. Outputs qualified/disqualified verdict with detailed reasoning. - Skill: `ekatasingh1107/lead-qualifier` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add ekatasingh1107/lead-qualifier` - Raw SKILL.md: https://api.skillmd.com/api/skills/ekatasingh1107/lead-qualifier/raw - Safety review: PASS (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: ekatasingh1107 (https://skillmd.com/u/ekatasingh1107) - Updated: 2026-08-19 - Page: https://skillmd.com/skills/ekatasingh1107/lead-qualifier --- # Lead Qualifier Performs deep, multi-dimensional qualification on a single lead or batch of leads. Goes beyond simple lead scoring by evaluating across BANT (Budget, Authority, Need, Timeline), firmographic fit against ICP, behavioral engagement signals, and intent indicators. Outputs a clear qualified/disqualified verdict with detailed reasoning and recommended next action. ## Prerequisites - `agency.config.json` populated (ICP, services, pricing context, CRM config) - Lead data: at minimum, company name and contact name - WebSearch tool available for enrichment during qualification - Optional: CRM data for behavioral signal analysis - Optional: `lead-scorer` capability output as input ## Capabilities Used 1. `company-researcher` -- firmographic data for qualification 2. `person-researcher` -- decision-maker validation 3. `lead-scorer` -- initial lead score as input signal 4. `crm-writer` -- pull engagement history and update qualification status 5. `decision-maker-finder` -- verify authority dimension ## Phase 0: Intake Read `agency.config.json`: - `icp.segments[]` -- ideal customer profile criteria - `services[]` -- what you sell (for need mapping) - `pricing` -- minimum engagement size, retainer ranges (for budget qualification) - `crm.tabs` -- locate lead data - `qualification_rules` -- custom rules if defined in config Accept parameters: - `lead` -- (required) lead object or lead name to look up in CRM - `mode` -- `single` | `batch`. Default: `single` - `leads` -- (required if mode = `batch`) array of lead objects - `auto_route` -- boolean, automatically trigger `lead-router` after qualification. Default: `false` - `depth` -- `quick` | `full`. Default: `full` Lead object structure: ```json { "contact_name": "Priya Mehta", "company_name": "FreshSkin Co", "email": "priya@freshskin.co", "title": "Founder", "source": "inbound_form", "notes": "Interested in Shopify store revamp" } ``` ## Phase 1: BANT Assessment ### Budget (Weight: 25%) Evaluate ability and willingness to pay: **Direct signals** (from form data, emails, conversations): - Mentioned budget range - Asked about pricing (positive signal -- they're evaluating) - Mentioned "cost-effective" or "affordable" (may signal tight budget) - Mentioned specific investment amount **Indirect signals** (from research): - Company revenue/funding (proxy for budget capacity) - Current spending on similar services (if visible) - Number of employees (proxy for operational budget) - Premium vs budget positioning of their brand **Scoring**: ``` Budget Score: 5 = Budget confirmed, aligns with services pricing 4 = Strong budget indicators (funded, premium brand, asked about pricing) 3 = Moderate indicators (mid-size company, no budget mentioned) 2 = Weak indicators (small company, mentioned budget constraints) 1 = Disqualifying (explicitly stated budget far below minimum) 0 = No data available ``` ### Authority (Weight: 25%) Evaluate decision-making power: **Title analysis**: - C-suite (CEO, CMO, CTO): Score 5 - VP/Director level: Score 4 - Head/Manager level: Score 3 - Coordinator/Specialist: Score 2 - Unknown/Intern: Score 1 **Decision-maker validation**: - Run `decision-maker-finder` if title suggests non-decision-maker - Check if they mentioned "I need to check with..." (lower authority) - Check if they said "I'm the one who decides..." (higher authority) - Company size context: at a 10-person startup, a "Marketing Manager" may be the sole decision-maker **Scoring**: ``` Authority Score: 5 = Confirmed decision-maker, signs contracts 4 = Strong authority (C-suite, founder, sole ecomm person) 3 = Influencer with access to decision-maker 2 = Influencer without confirmed path to decision-maker 1 = No authority, no path identified 0 = No data available ``` ### Need (Weight: 30%) Evaluate alignment between their problems and your services: **Explicit need signals**: - Mentioned specific pain points matching `services[]` - Requested a specific service by name - Described a project scope - Referenced competitors doing something they want **Implicit need signals**: - Website quality issues visible on their store - Outdated design or poor mobile experience - Low conversion signals (poor product pages, no trust elements) - Missing features competitors have - Growing traffic but flat revenue (CRO need) - New product launches with basic pages (catalog need) **Service mapping**: For each detected need, map to a specific service from `services[]`: ``` Detected need -> Service match -> Fit strength "Store looks outdated" -> Shopify Store Development -> STRONG "Want more traffic" -> Performance Marketing -> STRONG "Conversion is low" -> CRO -> STRONG "Need better photos" -> Catalog Management -> MODERATE ``` **Scoring**: ``` Need Score: 5 = Explicit, urgent need perfectly matching core services 4 = Clear need matching services, moderate urgency 3 = Need exists but vague, or matches secondary services 2 = Peripheral need, weak match to services 1 = No apparent need for offered services 0 = No data available ``` ### Timeline (Weight: 20%) Evaluate urgency and readiness to start: **Urgency indicators**: - Mentioned specific deadline ("need this before Diwali sale") - Mentioned event-driven timeline ("launching new line in Q2") - Mentioned competitive pressure ("competitor just relaunched") - Mentioned internal pressure ("board wants ecomm growth this quarter") **Readiness indicators**: - Already evaluated other agencies (deep in buying process) - Has a brief or scope document ready - Mentioned budget is approved - Asked about start dates and timelines **Anti-urgency indicators**: - "Just exploring for now" - "Maybe next quarter" - "No rush" - "We're in early stages of thinking about this" **Scoring**: ``` Timeline Score: 5 = Immediate need, ready to start within 2 weeks 4 = Near-term, wants to start within 1-2 months 3 = Medium-term, planning for next quarter 2 = Long-term, 6+ months out 1 = No timeline, "just exploring" 0 = No data available ``` ## Phase 2: Firmographic Qualification Compare the lead's company against ICP segments: ### Segment Match For each `icp.segments[]` entry, evaluate: - Industry match: Does their vertical match? - Company size match: Within the segment's range? - Geography match: Correct market? - Revenue/stage match: Post-PMF? Mid-market? Early-stage? - Platform match: On Shopify or moving to Shopify? ### Firmographic Score ``` Firmographic Score: 5 = Perfect ICP match (right industry, size, geo, stage, platform) 4 = Strong match (3-4 criteria match) 3 = Partial match (2-3 criteria match) 2 = Weak match (1-2 criteria match) 1 = No match but adjacent (could become ICP with growth) 0 = Anti-ICP (wrong industry, too small/large, wrong platform) ``` ### Deal Breakers Check for automatic disqualifiers: - Company in a prohibited industry - Company too small (below minimum engagement size) - Company on a platform you don't serve with no migration interest - Geographic restrictions (if applicable) - Direct competitor of an existing client (conflict of interest) ## Phase 3: Behavioral Qualification Pull engagement data from CRM (if available): ### Engagement Signals - Email opens and clicks (frequency, recency) - Website visits (pages viewed, time on site) - Content downloads (guides, templates) - Webinar attendance - Social media engagement (liked, commented, shared) - Form submissions (which forms, how detailed) - Meeting requests or demo bookings ### Engagement Score ``` Engagement Score: 5 = High engagement (multiple touchpoints, recent, deep) 4 = Good engagement (responded to outreach, visited key pages) 3 = Moderate engagement (opened emails, some website visits) 2 = Low engagement (minimal interaction) 1 = No engagement (cold lead, no response to outreach) 0 = No data available ``` ## Phase 4: Intent Signal Analysis Look for buying intent signals: ### High-Intent Signals (score 3 each) - Searched for "[your service] agency" or similar - Visited pricing page - Requested a proposal or audit - Asked about contracts or terms - Compared you to competitors by name - Mentioned urgency or deadline ### Medium-Intent Signals (score 2 each) - Downloaded a case study - Attended a webinar - Engaged with multiple content pieces - Followed on LinkedIn - Visited portfolio/case study page ### Low-Intent Signals (score 1 each) - Subscribed to newsletter - Single blog post visit - Social media follow only - Opened a cold email but no reply ### Intent Score ``` Intent Score: sum(signals) normalized to 1-5 scale 5 = Strong buying intent (4+ high-intent signals) 4 = Good intent (2-3 high-intent signals) 3 = Moderate intent (medium signals present) 2 = Weak intent (low signals only) 1 = No intent signals detected ``` ## Phase 5: Composite Qualification Calculate the composite qualification score: ``` composite_score = ( budget_score * 0.25 + authority_score * 0.25 + need_score * 0.30 + timeline_score * 0.20 ) firmographic_modifier: 5 = +0.5 4 = +0.25 3 = 0 2 = -0.25 1 = -0.5 0 = DISQUALIFY (automatic) behavioral_modifier: 5 = +0.3 4 = +0.15 3 = 0 2 = -0.1 1 = -0.2 intent_modifier: 5 = +0.3 4 = +0.15 3 = 0 2 = -0.1 1 = -0.2 final_score = composite_score + firmographic_modifier + behavioral_modifier + intent_modifier ``` ### Qualification Verdict ``` QUALIFIED (score >= 3.5): - MQL (Marketing Qualified Lead): score 3.5-4.0 - SQL (Sales Qualified Lead): score 4.0-4.5 - HOT (Ready to close): score 4.5+ DISQUALIFIED (score < 3.5 or deal breaker triggered): - NURTURE: score 2.5-3.49 (not ready, but could be) - UNQUALIFIED: score < 2.5 (poor fit) - DEAL_BREAKER: automatic disqualifier triggered ``` ## Phase 6: Output Return structured JSON: ```json { "lead": { "contact_name": "Priya Mehta", "company_name": "FreshSkin Co", "title": "Founder", "source": "inbound_form" }, "qualification": { "verdict": "SQL", "final_score": 4.2, "confidence": "HIGH", "reasoning": "Strong ICP fit (D2C skincare, post-PMF), founder-level authority, explicit need for store revamp, Q2 timeline mentioned. Budget unconfirmed but funding round suggests capacity." }, "bant": { "budget": {"score": 3, "notes": "No explicit budget mentioned. Series A funded, premium pricing suggests healthy margins."}, "authority": {"score": 5, "notes": "Founder and sole decision-maker."}, "need": {"score": 5, "notes": "Explicitly requested Shopify store revamp. Current store has poor mobile experience and outdated design."}, "timeline": {"score": 4, "notes": "Mentioned wanting to launch before Q2 seasonal push."} }, "firmographic": { "score": 4, "icp_segment": "Post-PMF D2C India", "match_details": {"industry": true, "size": true, "geo": true, "stage": true, "platform": true} }, "behavioral": { "score": 4, "signals": ["Opened 3/3 emails", "Visited case study page twice", "Downloaded CRO guide"] }, "intent": { "score": 4, "signals": ["Requested audit", "Visited pricing page", "Asked about timelines"] }, "deal_breakers": [], "recommended_action": { "next_step": "Schedule discovery call with founder", "assigned_to": "founder_direct", "urgency": "This week", "talking_points": ["Reference their mobile experience issues", "Share skincare D2C case study if available"] }, "generated_at": "2026-03-07T10:00:00Z" } ``` ## Phase 7: Review and Routing Present qualification results. If `auto_route` = true, trigger `lead-router` with the qualification output. **APPROVAL GATE**: "Lead qualified as [verdict]. Route to [assignment]?" Update CRM via `crm-writer`: - Set qualification status - Set lead score - Add qualification notes - Set next action and assigned owner ## Example Usage Trigger phrases: - "Qualify this lead: [name] at [company]" - "Is [prospect] a good fit for us?" - "Run BANT qualification on the new inbound leads" - "Score and qualify [lead]" - "Should we pursue [company]? Qualify them." - "Batch qualify this week's inbound leads"