Qualification Skill

Turn a campaign plan's qualify/disqualify rules into the variables an AI qualifier runs on every prospect: an audience description, disqualification criteria, and (rarely) qualification criteria. They go in qualification.md at the root of the campaign workspace (./cold-email/{slug}/), ready for any qualification step: an AI column in Clay or a spreadsheet, or any LLM.

Inputs

• The plan's broad ICP and its qualify/disqualify rules.
• The search filters already applied (search-strategy.md in the campaign workspace). They tell you what NOT to restate.
• The research (research.md and its company context) for competitors and ICP nuances, and the company knowledge folder (./knowledge/) when it exists: its competitors/ files and who the ideal customer profile says is not a fit.
• A sample of sourced leads, when available, to see real edge cases.

Use what the conversation already holds. Read only what is missing: plan.md, search-strategy.md and research.md in the campaign workspace (default ./cold-email/{slug}/ under the current directory).

What the qualifier can see

The qualifier sees ONLY:

• LinkedIn profile: title, company, location, previous roles, education, skills, summary.
• Company data: industry, headcount, HQ, description.
• Website metadata: page title and meta description only.

It does NOT see full site content, tech stack, news, funding, social posts, press, events, or any time-sensitive signal. Every criterion must be checkable against the data above. Rewrite or drop any rule that depends on something else.

Core Principles

• Write short, trust the AI - Give it a short, plain-language brief and let it reason. A few high-signal lines beat an exhaustive rulebook. No long lists of titles, industries, or product names.
• Never repeat the search filters - The filters already bound the list (headcount, geography, titles, seniority, industries). Do not restate any of it, not in the audience description and not as a criterion. Qualification only catches what the filters can't see.
• Don't over-qualify - Qualification is a safety net, not a precision filter. When data is incomplete or ambiguous, qualify.
• Disqualification over qualification - Prefer audience description + a few disqualifiers. Add qualification criteria only for a real positive signal worth confirming.
• Always exclude competitors - The one disqualifier that is always worth including.

Details, examples, and a disqualifier menu: references/qualification-guide.md.

Write the Variables

Audience description (required): one plain sentence on who we target, 5-20 words. No headcount, geography, title lists, or industries.

Disqualification criteria (strongly preferred): binary deal-breakers the filters can't see, such as competitor or wrong business model. Usually 2-4 lines, never more than 6. Always include competitor exclusion. Don't pad.

Qualification criteria (optional, usually empty): only a genuine positive signal visible in the data above.

At least the audience description and the disqualification criteria must be non-empty. Fewer than two substantive sections is a hard stop: revise first.

Running it

The three sections make one yes/no question per prospect, asked with the company context. Run it where the list lives: an AI column in Clay or a spreadsheet, or any LLM with the prospect's data pasted in. A starting prompt:

You judge whether a prospect fits a cold email campaign. Answer yes or no, then one short reason.
Company: {the company context from research.md}
Audience: {audience description}
Qualifies when: {qualification criteria, or "no extra conditions"}
Disqualified when: {disqualification criteria}
When the data is incomplete or ambiguous, answer yes.
Prospect: {LinkedIn profile, company data, website title and description}

Check 10 to 20 answers by hand before you trust it on the whole list. When you change the criteria, run the list again: earlier answers no longer hold.

Save

Show the three sections to the user and get approval. Then write qualification.md at the root of the campaign workspace:

# Qualification Prompt

## Audience Description
[One plain sentence on who we target. No headcount, geography, titles, or industries.]

## Disqualification Criteria
- [Competitor exclusion]
- [One or two more deal-breakers the filters can't see]

## Qualification Criteria
[Usually empty. Only a real positive signal worth confirming.]

• Exactly these three H2 headings and no others: a tool that reads the file looks for them.
• Keep one qualification file per campaign: when it exists, update its sections, never write a second.
• With no campaign workspace (a one-off request), give the three sections in the chat instead.

Confirm: "Qualification saved to ./cold-email/{slug}/qualification.md."

Recruiting campaigns

A recruiting campaign fills a role: the plan or the user gives the role (title, level, place or remote policy, must-haves). The leads are candidates, so the prompt decides who could take this job, not who would buy.

• Audience description: the role in one or two sentences (title, level, place or remote policy) and the kind of person who could do it.
• Qualification criteria: the role's must-haves, each written as something a profile shows (a current or past title, years of experience, a skill, a degree). Leave out what a profile never shows, such as notice period or salary expectations.
• Disqualification criteria: people who already work at the hiring company, people far more senior or junior than the role, and people whose profile shows none of the must-haves. A nice-to-have never disqualifies.
• Location: for an onsite or hybrid role, people outside the role's area do not qualify unless the role allows relocation. A remote role disqualifies no one on location.
• Never qualify or disqualify on age, gender, race, religion, disability, family status or nationality, or on a guess at any of them (a name, a photo, a graduation year). A language the job needs is a skill, and fine.

Save it exactly as above.