Marketing Hiring Scorecard
Defines what actually matters for one open marketing role, then scores anonymized candidates against it - never a criterion that proxies for something it shouldn't.
Get This Skill
Run this command in your terminal, or paste it into Claude—or your favorite AI assistant—and ask it to install the skill for you.
What This Skill Does
Criteria built from the actual job
Derives what to evaluate from what the role needs to accomplish, not a generic checklist.
Keeps names out of the scorecard
Every candidate is labeled Candidate A/B/etc.; no name or contact detail ever appears in the output.
Refuses the criteria that shouldn't be there
Catches and rejects anything that proxies for a protected characteristic, whether it's a proposed criterion or a stray note about a candidate.
Shows exactly what's missing
Marks any criterion you can't yet speak to as "insufficient information," not a guess.
Never estimates pay
States plainly that no verifiable compensation figure exists rather than naming one.
Consistent across candidates
Every person for the same role gets scored against the identical criteria and weights.
Best Used For
- Defining what to actually evaluate candidates on before interviews start
- Comparing two or more finalists for the same marketing role on a level basis
- Making sure a "culture fit" impression gets tied to an actual, job-relevant behavior
- Keeping a hiring decision's basis defensible and consistent if it's ever questioned later
How It Works
Without a defined scorecard, marketing hires tend to get decided on gut feel - who interviewed best, who felt like the strongest "fit" in the vaguest sense. That's exactly how criteria that have nothing to do with the job, and sometimes ones that are legally risky, quietly end up shaping the decision.
This skill starts from what the role actually needs to accomplish and derives a set of job-relevant criteria from that, weighted by what matters most for this specific hire. Every proposed criterion - and every fact volunteered about a candidate - gets checked against a list of common proxies for protected characteristics; anything that fails gets set aside with an explanation, not silently scored. Candidates are never referred to by name in the process; every one is labeled Candidate A, B, and so on, so no personal detail ends up in the finished artifact.
You walk away with a ranked, job-relevant scorecard for every candidate, a follow-up question for anything you didn't have enough information to score, and a consistent basis for the decision - not a guess at compensation, and not a guarantee of how anyone will actually perform on the job.
Get This Skill
Run this command in your terminal, or paste it into Claude—or your favorite AI assistant—and ask it to install the skill for you.
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Common questions
No - describe candidates by their role-relevant background instead. Even if a name is mentioned, it never appears in the finished scorecard; every candidate is labeled Candidate A/B/etc.
It's checked against a list of common proxies for protected characteristics - things like age, family status, or unrelated alma mater prestige. If a criterion or a candidate detail falls into that list, it's set aside and you're asked for a job-relevant substitute instead.
No - there's no verifiable general figure for what a role should pay, and this skill says so rather than guessing. Compensation benchmarking needs a real, current data source.
No - it's a structured, job-relevant read built from what you supplied, not a guarantee of future job performance.
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