startup-context

• ICP definition — Who is the ideal customer (company size, industry, stage, geography, use case)
• Lead sources — Where inbound leads come from (website, events, content, referrals)
• CRM and tooling — Current stack for managing leads and deals
• Current customers — Who are the best existing customers and why
• Pipeline data — Existing deals, active customers, prior contacts
• Sales capacity — Who handles leads and what is their bandwidth

1. Load ICP and configuration — Read startup-context if available. Establish the qualification criteria across company attributes, person attributes, and use case fit.
2. Parse the lead data — Accept leads in any format (CSV, list, CRM export, single name). Identify data gaps and flag what needs enrichment.
3. Check pipeline overlap — Before scoring, check for existing customers (route to upsell), active deals (flag for sales coordination), and prior contacts (note history). Pipeline overlaps are routing flags, not disqualifiers.
4. Score company fit — Evaluate against company size, industry, stage, geography, and use case alignment. Weight each dimension based on what predicts closed-won deals.
5. Score person fit — Evaluate title, seniority, department, and decision-making authority. A perfect company with the wrong contact still needs routing, not rejection.
6. Score use case alignment — Connect the lead's inferred intent to specific product capabilities. Inbound signals (demo requests, pricing page visits) tip borderline cases toward qualification.
7. Generate composite score and verdict — Produce a 0-100 composite score and assign a routing recommendation.
8. Export structured output — Deliver results in a table or CSV with all qualification data, scores, and routing.

1. Scored lead report — Each lead with composite score (0-100), sub-scores by dimension, verdict category, and routing recommendation
2. ICP definition — Firmographic and demographic criteria with priority tiers
3. Scoring model — Complete point-value table for company, person, and use case dimensions with threshold definitions
4. Pipeline routing rules — How each verdict category gets handled

• Existing customer — Route to account management for upsell/expansion conversation
• Active deal in pipeline — Flag for the assigned sales rep to coordinate, do not create a duplicate
• Prior contact with no deal — Note history and score normally, but include context for the sales rep
• Competitor employee — Auto-disqualify and log for competitive intelligence

• Minimum fit score: 30 points (must have basic ICP match)
• Minimum engagement score: 20 points (must show some intent)
• Combined minimum: 60 points

• Recalibrate quarterly. Pull closed-won data and check if the model correctly predicted winners.
• Watch for score inflation. If 80% of leads become MQLs, the threshold is too low.
• Track MQL-to-SQL acceptance rate. If sales rejects more than 30% of MQLs, adjust the model.
• Start simple. Score the first 50-100 leads by hand before automating.
• Speed-to-lead is critical. Contact within 5 minutes is 21x more likely to qualify.

• cold-outreach

  — Use the ICP and scoring to prioritize who to reach out to first

• sales-script

  — Use pipeline stage definitions to prepare the right script for each stage

Lead Qualification Report (Sample)

Lead	Company Score	Person Score	Use Case Score	Composite	Verdict
Jane Smith, VP Eng @ Acme (200 emp, SaaS)	88	85	90	88	Qualified — Hot
Bob Lee, Developer @ TinyCo (15 emp, Agency)	35	40	50	40	Near Miss
Unknown Title @ MegaCorp (10K emp, Finance)	60	30 (unknown)	45	47	Near Miss — Enrich

Routing: Jane gets immediate sales outreach (AE assigned within 1 hour). Bob enters nurture sequence. MegaCorp lead flagged for enrichment — title and use case data needed before routing.