Qualify a lead against our ICP

AI lead qualification is an Agent Skill: a written method your AI applies on demand, in Zealos or in any client that reads SKILL.md.

Qualification is a claim about evidence, not a score: a deal is qualified when every criterion has a source you can quote, and unevidenced criteria are named rather than filled in optimistically.

What is in this skill

namemeddicc
descriptionThe MEDDICC qualification framework and what each letter requires before a deal can advance. Use when assessing deal health, preparing a forecast, writing a deal review, or deciding whether a deal is real.
allowed-toolsnone — this skill adds judgement, not reach
compatibilityZealos, Claude Code, Cursor, Codex and any client that reads SKILL.md
licenseFree to use and edit

How it works

  1. 1For each criterion record one of three states — evidenced with a quotable source, assumed and labelled as such, or missing entirely.
  2. 2Demand a number in the buyer's own units for the metric. Two days per rep per week is a metric; efficiency gains is a hope.
  3. 3Test the champion by asking them to do something small. A champion who will not introduce you to the economic buyer is a contact.
  4. 4Report the single highest-value unknown and the one question that resolves it, rather than a list of everything still missing.

The skill itself

---
name: meddicc
description: The MEDDICC qualification framework and what each letter requires before a deal can advance. Use when assessing deal health, preparing a forecast, writing a deal review, or deciding whether a deal is real.
---

# MEDDICC

A deal is qualified when every letter has evidence, not a guess. Say which
letters are unevidenced rather than filling them in optimistically — a
confidently-wrong qualification is worse than an obviously incomplete one.

| | Requires |
|---|---|
| **M**etrics | A number the buyer stated, in their units. "Two days per rep per week", not "efficiency gains". |
| **E**conomic buyer | A named person who can sign without asking anyone. If we have not spoken to them, say so. |
| **D**ecision criteria | What they will compare us on, in their words and their order. |
| **D**ecision process | The actual steps: security review, legal, procurement, board. With dates. |
| **I**dentify pain | What breaks if they do nothing. If nothing breaks, there is no deal. |
| **C**hampion | Someone who sells internally when we are not in the room, and who has something to gain. Tested by asking them to do something. |
| **C**ompetition | Including "do nothing" and "build it ourselves", which win more often than any vendor. |

## How to report

For each letter: **evidenced** (quote the source — call, email, note),
**assumed** (say so), or **missing**.

Then state the single highest-value unknown and the question that resolves it.
One question, not a list.

## Red flags

- Champion who will not introduce you to the economic buyer.
- Metrics we authored rather than heard.
- A decision process with no dates.
- "Everyone loves it" with no named detractor. There is always a detractor.

Who uses it

Account executive

Deal reviews stop being a memory test. Every criterion carries the call or email it came from, so the conversation is about the gaps rather than about what you remember.

Sales manager

You can see which deals are qualified on evidence and which are qualified on optimism, which is usually the difference between the forecast and the quarter.

RevOps

Stage progression starts meaning the same thing across the team, because the exit criteria are written once rather than interpreted per rep.

Frequently asked questions

Which qualification framework should I encode?

Whichever one your team already argues in. MEDDICC, SPICED and BANT all work — the value is in writing the criteria down once, not in the acronym.

Can it disqualify a deal on its own?

It reports against the criteria; you decide. A skill that silently killed deals would be a policy engine, and policy belongs in your pipeline rules.

Does it need CRM access?

It reads better with it, but the framework itself is pure method — it tells the model what evidence to demand, wherever that evidence lives.

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