# Lead Qualifier > Scores, enriches, and prioritizes inbound sales leads using firmographic data, behavioral signals, and ICP criteria. Invoke when asked to qualify leads, score prospects, prioritize a sales pipeline, enrich contact data, or evaluate if a lead matches the ideal customer profile. - Skill: `vril-labs/lead-qualifier` (Agent Skill) - Install (CLI): `npx skillmds@latest add vril-labs/lead-qualifier` - Raw SKILL.md: https://api.skillmd.com/api/skills/vril-labs/lead-qualifier/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: DevOps & Infra - Author: VRIL-LABS (https://skillmd.com/u/vril-labs) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/vril-labs/lead-qualifier --- # Lead Qualifier Scores, enriches, and prioritizes inbound sales leads by evaluating firmographic fit, behavioral engagement signals, and alignment with the Ideal Customer Profile (ICP) โ€” delivering a ranked, actionable lead queue with qualification rationale to help sales teams focus on the highest-probability opportunities. ## When to Use - User provides a list of leads or a CRM export and wants them scored and prioritized - An inbound lead needs to be quickly evaluated for sales follow-up urgency - User asks to "qualify", "score", or "enrich" a prospect or lead list - A sales pipeline needs to be triaged to focus effort on the best opportunities - An ICP needs to be defined and then applied to a set of prospects - Lead routing logic needs to be designed (which rep gets which type of lead) - User wants to identify the characteristics of their best-fit customers ## Process 1. **Define or confirm the Ideal Customer Profile (ICP)**: If no ICP is provided, ask for or infer from context: - **Firmographic criteria**: company size (employees, revenue range), industry/vertical, geography, business model (B2B/B2C), growth stage (startup/SMB/mid-market/enterprise) - **Technographic criteria**: tech stack signals (e.g., "uses Salesforce", "runs on AWS", "built with React") - **Behavioral criteria**: visited pricing page, started trial, engaged with specific content, attended webinar - **Intent signals**: recent funding round, job posting for roles that use your product, leadership change - **Disqualifiers**: industries you don't serve, company sizes below minimum deal size, geographies outside your market 2. **Enrich the lead data**: For each lead, gather missing data from available signals: - Company: industry, size, revenue, funding history, headquarters, tech stack - Contact: title, seniority level, department, LinkedIn profile - Behavioral: pages visited, content downloaded, email opens/clicks, trial activity, time-on-site - Intent: third-party intent data signals (G2 reviews browsed, competitor comparisons, job postings) 3. **Score each lead**: Apply a weighted scoring model across dimensions: **Firmographic fit (up to 40 points)**: - Industry match: +15 if in target vertical, +5 if adjacent - Company size: +15 at ideal size range, scaled down for smaller/larger - Geography: +10 if in target market - Revenue/stage: +10 if aligned with your ACV range **Behavioral engagement (up to 30 points)**: - Visited pricing page: +10 - Started free trial or demo request: +15 - Returned to site 3+ times: +8 - Engaged with email / attended webinar: +5 each **Intent signals (up to 20 points)**: - Active buying intent (recent RFP, comparison browsing): +15 - Recent relevant job posting: +10 - New funding round (can afford your product): +8 - Leadership change: +5 **Contact quality (up to 10 points)**: - Decision-maker or budget holder: +10 - Influencer/evaluator: +5 - Unknown seniority: +0 **Total score โ†’ tier**: - 80โ€“100: ๐Ÿ”ฅ Hot โ€” immediate outreach (same business day) - 60โ€“79: ๐ŸŸก Warm โ€” nurture + outreach within 48 hours - 40โ€“59: ๐ŸŸข Qualified โ€” add to nurture sequence - <40: โŒ Not yet qualified โ€” add to long-term nurture or disqualify 4. **Apply disqualifier checks**: - If a hard disqualifier is met (blocked industry, too small, wrong geography): mark as Disqualified regardless of score and note the reason - Soft disqualifiers (e.g., no budget signals): lower score but don't auto-disqualify 5. **Generate qualification summary per lead**: - Score and tier - Top 3 reasons for the score (positive signals) - Top 1โ€“2 disqualifying or derisking factors - Recommended next action: call, email, personalized outreach, nurture sequence, or disqualify - Suggested talk track or messaging angle based on the strongest qualifying signals 6. **Route lead to the appropriate owner**: - Apply routing rules: enterprise leads โ†’ enterprise AE, SMB leads โ†’ SDR, specific verticals โ†’ vertical specialist - Output: lead card with all enriched data, score, and recommended action attached ## Output Format ``` ## Lead Qualification Report **Date:** June 1, 2025 | **ICP:** B2B SaaS companies, 50โ€“500 employees, US/Canada, using Salesforce --- ### Lead #1: Jordan Martinez โ€” VP Sales, Acme Corp **Score: 84/100 ๐Ÿ”ฅ HOT** **Recommended Action:** Immediate outreach โ€” personalized email + call within 24 hours | Dimension | Score | Signal | |--------------------|-------|---------------------------------------------------------| | Firmographic fit | 35/40 | B2B SaaS โœ… ยท 180 employees โœ… ยท San Francisco โœ… | | Behavioral | 28/30 | Visited pricing page โœ… ยท Started trial (Day 3) โœ… | | Intent | 12/20 | 3 open SDR roles posted this month (scaling signal) | | Contact quality | 9/10 | VP Sales โ€” budget holder / decision-maker โœ… | **Key Qualifiers:** Trial activity, decision-maker title, scaling sales team **Risk Factors:** Trial engagement dropped after Day 3 โ€” possible blocker **Talk Track:** "We saw you were exploring [feature] in your trial โ€” many VP Sales at [similar company] use that to [outcome]. Can I show you how?" **Route to:** Enterprise AE โ€” Sarah K. --- ### Lead #2: Anonymous Form Fill โ€” marketing@genericco.com **Score: 28/100 โŒ NOT YET QUALIFIED** **Recommended Action:** Add to nurture email sequence (monthly touchpoints) | Dimension | Score | Signal | |--------------------|-------|-----------------------------------| | Firmographic fit | 10/40 | Industry unknown ยท Company unknown | | Behavioral | 8/30 | Downloaded 1 ebook | | Intent | 5/20 | No intent signals | | Contact quality | 5/10 | Generic email โ€” unknown seniority | **Risk Factors:** No company data available for enrichment. Generic email address. **Action:** Trigger enrichment workflow; if company is identified, re-score. ``` ## Examples ### Example Input ``` Here are 5 inbound leads from this week. Our ICP is B2B SaaS companies, 100โ€“1000 employees, in the US. Score and prioritize them. [lead data] ``` ### Example Output ``` Lead Prioritization โ€” Week of June 1 1. ๐Ÿ”ฅ Jordan Martinez (VP Sales, Acme Corp) โ€” Score: 84 ยท Immediate outreach 2. ๐ŸŸก Priya Sharma (Head of Ops, Beta Inc) โ€” Score: 67 ยท 48-hour follow-up 3. ๐ŸŸข Chris Wong (Marketing Manager, Gamma LLC) โ€” Score: 52 ยท Nurture sequence 4. ๐ŸŸข Taylor Reed (Developer, Delta Co) โ€” Score: 44 ยท Technical nurture track 5. โŒ Anonymous โ€” Score: 28 ยท Enrich before contacting Top priority: Jordan โ€” trial activity + decision-maker title = highest close probability this week. ``` ## Boundaries - Lead scoring models are probabilistic guides, not predictions โ€” always frame scores as directional signals that require sales judgment. - Do NOT use protected characteristics (gender, race, age, nationality, religion) as scoring signals โ€” ever. - Be transparent about the scoring model: share weights and criteria so sales teams can understand and calibrate. - If enrichment data is missing, reduce confidence in the score and flag it rather than inflating the score with assumed data. - Do NOT auto-send outreach on behalf of the user โ€” surface recommendations and let the sales team execute. - Treat all lead contact data as PII โ€” do not log or expose it beyond the immediate task.