- name
- creator-analysis
- description
- Analyze creator profiles, content patterns, audience fit, and collaboration quality to support smarter creator decisions. Use when evaluating creators for partnerships, comparing shortlist options, diagnosing weak creator fit, or turning creator signals into a clear prioritize / test / skip recommendation.
Creator Analysis
Analyze creators with a short, decision-ready workflow.
This skill is not just for describing a creator.
Its real job is to help answer:
- Is this creator actually a fit for the product or offer?
- Is the creator strong because of real fit, or just because the content looks polished?
- Should the team prioritize, test, monitor, or skip this creator?
- What kind of collaboration is this creator best suited for?
- What risks are easy to miss if we only look at vanity metrics?
Solves
Creator selection often breaks down in predictable ways:
- teams confuse reach with relevance;
- polished content gets mistaken for conversion potential;
- shortlists are built on vague gut feel;
- nobody separates audience fit from operational risk;
- the same creator looks “good” until product, category, and offer constraints are applied;
- outreach and testing happen before the fit is clear.
Goal: Turn creator data into a clear fit assessment and next-step recommendation.
Use when
Use when the user needs a creator decision, not just a profile summary.
Typical cases:
- evaluating creators for partnerships, seeding, UGC, affiliate, or paid-whitelisting work;
- comparing two or more creators for the same product or brief;
- diagnosing why a creator is or is not a good fit;
- summarizing creator strengths / weaknesses before outreach;
- reviewing content patterns to estimate trust, fit, and conversion relevance;
- building a shortlist with clear prioritize / test / skip decisions.
Do not use when
Do not use this skill when:
- the user only wants follower counts or a surface profile scrape;
- there is too little creator evidence to assess fit;
- the task is direct outreach drafting rather than fit analysis;
- the task is campaign attribution or post-campaign performance reporting;
- the user wants legal/contract review instead of partnership evaluation.
Inputs
Ask for the minimum useful decision context:
- creator handle / profile link
- niche and platform
- product / category / offer
- target customer
- goal of the collaboration
- content samples, if available
- audience clues, comments, or metrics, if available
- any constraints (budget, country, language, brand safety, creator type)
Workflow
1. Define the decision
Clarify what the team is trying to decide:
- shortlist creators
- compare creators
- diagnose weak fit
- summarize a creator before outreach
- decide whether to test or skip
2. Normalize the inputs
Restate the context clearly:
- creator identity
- niche / category
- target customer
- product and offer constraints
- available evidence
3. Score the creator on five useful dimensions
Evaluate:
- audience relevance
- content clarity
- proof / trust signals
- conversion potential
- operational risk
Optional sub-questions:
- Does the audience match the buyer?
- Does the creator naturally present products in a believable way?
- Is the content style usable for direct response, UGC, creator seeding, or affiliate?
- Is there any risk in tone, consistency, professionalism, or category mismatch?
4. Write the recommendation in plain language
Answer clearly:
- what this creator is good for
- where the fit is weak
- whether to prioritize, test, monitor, or skip
- what type of brief or collaboration would suit them best
Output format
Return a concise decision package:
- One-line verdict
- prioritize / test / monitor / skip
- Strengths
- what this creator is genuinely good at
- Risks / gaps
- what could reduce fit or performance
- Recommended use case
- seeding / UGC / affiliate / paid ads / awareness / trust-building / not recommended
- Next action
- outreach now / test later / compare with others / skip
- Confidence note
- if metrics or evidence are incomplete, say so directly
Quality bar
A strong analysis should:
- prefer observable evidence over hype words;
- separate reach from fit;
- separate polished content from conversion relevance;
- flag uncertainty when evidence is thin;
- explain the recommendation in business language;
- help the user make a creator decision faster.
What “better” looks like
Good output should make it obvious:
- whether the creator is actually worth time or budget;
- what this creator is best used for;
- why the fit is strong or weak;
- what risk is easy to overlook;
- what the team should do next.
Resources
Read references/output-template.md when you need a ready response shape.