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Get Started Free →When a founder needs to qualify inbound leads, define their ICP, build a lead scoring model, set MQL criteria, or route prospects through pipeline stages. Activate when the user mentions lead scoring, ICP, MQL, SQL, lead qualification, inbound leads, or pipeline design.
.claude/skills/mkurman-lead-scoring/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-02 | ✗→✓ | ▲ Improved | 48% | 0% |
| case-03 | ✗→✓ | ▲ Improved | 27% | 0% |
| case-04 | ✗→✓ | ▲ Improved | 69% | 0% |
| case-07 | ✗→✓ | ▲ Improved | 77% | 0% |
| case-10 | ✗→✓ | ▲ Improved | 49% | 0% |
---|-------|--------| | Qualified — Hot | 85-100 | Immediate sales outreach. High urgency, strong fit. | | Qualified — Warm | 75-84 | Active pursuit within 24 hours. Good fit, moderate urgency. | | Borderline | 50-74 | Requires human review. Qualified with caveats — flag specific concerns. | | Near Miss | 30-49 | Nurture sequence or referral opportunity. Not ready for sales. | | Disqualified | 0-29 | Does not fit ICP. Includes competitor employees. Polite decline. |
Score unknown dimensions at 30 points (out of 100 for that dimension). This acknowledges data absence without automatically rejecting leads. A lead missing company size data is not the same as a lead with the wrong company size. Flag unknowns for enrichment rather than penalizing them.
Prospects who initiate contact demonstrate genuine interest. For borderline cases (scores 50-74), inbound signals should tip the scoring decision toward qualification. A borderline lead who requested a demo is a better prospect than a slightly-above-threshold lead who has never engaged.
Before scoring, check for overlaps and route accordingly:
Company evaluation — Score against: company size, industry vertical, company stage/funding, geography, and use case fit. Weight dimensions based on which most predict closed-won deals in your data.
Person assessment — Score against: job title, seniority level, department alignment, and decision-making authority. A Director of Engineering at a perfect-fit company scores higher than a junior developer at the same company.
Use case alignment — Map the lead's stated or inferred needs to specific product capabilities. Strong alignment on the core use case matters more than broad but shallow fit.
An MQL requires BOTH fit and engagement. Neither alone is sufficient.
A perfect-fit company that never engages is not an MQL. A student downloading every whitepaper is not an MQL. The dual-threshold prevents both failure modes.
cold-outreach — Use the ICP and scoring to prioritize who to reach out to firstsales-script — Use pipeline stage definitions to prepare the right script for each stageExample prompt: "We get 200 inbound leads a month from our website and events. Most go nowhere. Help me build a system to score and route them."
Good output excerpt: > ### 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.
Example prompt: "A lead from a current customer's company just filled out our demo form. What do I do?"
Good output approach: Flag the pipeline overlap — check if this is a new department/team or the same buyer. If same account, route to the existing account manager for upsell coordination. If new department, score normally but include account context. Never create a duplicate deal.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-16 | pass→pass | 13,928 | 11,906 | -15% | 1 | 1 | 0% | 2,198 | 3,015 | +37% | 0 | 0 | — |
case-01 | fail→fail | 8,306 | 15,621 | +88% | 1 | 1 | 0% | 1,324 | 3,832 | +189% | 0 | 0 | — |
case-21 | pass→pass | 15,348 | 13,174 | -14% | 1 | 1 | 0% | 2,372 | 3,098 | +31% | 0 | 0 | — |
case-02 | fail→pass | 21,127 | 22,711 | +7% | 1 | 1 | 0% | 3,576 | 5,307 | +48% | 0 | 0 | — |
case-03 | fail→pass | 13,082 | 8,538 | -35% | 1 | 1 | 0% | 1,988 | 2,525 | +27% | 0 | 0 | — |
case-04 | fail→pass | 11,735 | 12,000 | +2% | 1 | 1 | 0% | 1,650 | 2,791 | +69% | 0 | 0 | — |
case-05 | pass→pass | 10,217 | 9,911 | -3% | 1 | 1 | 0% | 1,548 | 2,593 | +68% | 0 | 0 | — |
case-06 | pass→pass | 10,052 | 7,459 | -26% | 1 | 1 | 0% | 1,499 | 2,154 | +44% | 0 | 0 | — |
case-07 | fail→pass | 11,368 | 11,371 | +0% | 1 | 1 | 0% | 1,582 | 2,802 | +77% | 0 | 0 | — |
case-08 | pass→pass | 14,884 | 10,580 | -29% | 1 | 1 | 0% | 2,124 | 2,791 | +31% | 0 | 0 | — |
case-09 | fail→fail | 11,733 | 7,496 | -36% | 1 | 1 | 0% | 1,796 | 2,255 | +26% | 0 | 0 | — |
case-10 | fail→pass | 9,308 | 6,097 | -34% | 1 | 1 | 0% | 1,391 | 2,066 | +49% | 0 | 0 | — |
case-11 | pass→pass | 11,497 | 3,952 | -66% | 1 | 1 | 0% | 1,832 | 1,868 | +2% | 0 | 0 | — |
case-12 | fail→pass | 12,769 | 4,833 | -62% | 1 | 1 | 0% | 2,098 | 1,928 | -8% | 0 | 0 | — |
case-13 | pass→pass | 12,378 | 8,850 | -29% | 1 | 1 | 0% | 1,809 | 2,423 | +34% | 0 | 0 | — |
case-14 | pass→pass | 14,390 | 8,337 | -42% | 1 | 1 | 0% | 2,069 | 2,355 | +14% | 0 | 0 | — |
case-15 | pass→pass | 13,994 | 10,822 | -23% | 1 | 1 | 0% | 2,041 | 2,759 | +35% | 0 | 0 | — |
case-17 | pass→pass | 5,419 | 3,999 | -26% | 1 | 1 | 0% | 802 | 1,774 | +121% | 0 | 0 | — |
case-18 | pass→pass | 12,432 | 10,215 | -18% | 1 | 1 | 0% | 1,921 | 2,671 | +39% | 0 | 0 | — |
case-19 | pass→pass | 14,149 | 12,597 | -11% | 1 | 1 | 0% | 1,934 | 2,968 | +53% | 0 | 0 | — |
case-20 | fail→pass | 4,885 | 1,617 | -67% | 1 | 1 | 0% | 714 | 1,337 | +87% | 0 | 0 | — |
case-22 | pass→fail | 16,308 | 20,428 | +25% | 1 | 1 | 0% | 2,680 | 4,477 | +67% | 0 | 0 | — |
case-23 | pass→pass | 12,408 | 10,812 | -13% | 1 | 1 | 0% | 1,978 | 2,747 | +39% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 23 cases were attempted. The headline lift of +26 percentage points is the difference between those two pass rates over the 23 comparable cases. 1 case got worse with the skill loaded, and it is included in that figure.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.