# Lead Qualification

> Run BANT/MEDDIC-inspired qualification with evidence vs unknown separation.

- Skill: `danthamanvoiss/lead-qualification` (Agent Skill)
- Install (CLI): `npx skillmds@latest add danthamanvoiss/lead-qualification`
- Raw SKILL.md: https://api.skillmd.com/api/skills/danthamanvoiss/lead-qualification/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: danthamanvoiss (https://skillmd.com/u/danthamanvoiss)
- Updated: 2026-09-22
- Page: https://skillmd.com/skills/danthamanvoiss/lead-qualification

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## Purpose
Assess lead quality using evidence-backed qualification criteria and recommend next action.

## Use when
- A new lead or referral needs prioritization.
- Pipeline review requires clear fit/confidence scoring.

## Required inputs
- Lead/account details supplied by user.
- Current offer scope from brand context.
- Any known timeline, budget, or decision process notes.

## Safety/authority
- No fabricated prospect facts.
- No legal, financial, or guaranteed-outcome claims.
- If data is missing, mark unknown instead of guessing.

## Workflow
1. Map evidence to BANT/MEDDIC-style dimensions.
2. Separate known evidence from assumptions and unknowns.
3. Identify blockers, risks, and required discovery questions.
4. Recommend qualification status and next step.

## Output format
```
- Qualification summary
- Evidence table (criterion | evidence | confidence)
- Unknowns to validate
- Recommendation: pursue | nurture | disqualify
- Next questions
```

## Quality checks
- Every claim has supporting input or is marked unknown.
- Recommendation aligns with evidence quality.

## Related skills
discovery-call-prep, proposal-scope-draft, salesportl-account-handoff