Operating logic
Maven Expert Acquisition Rubric
A screening model for identifying commercially-ready experts before the broader market notices them.
This rubric separates famous names from recruitable experts by evaluating practical career relevance, operator credibility, teaching signal, Maven dependency, recruiting wedge, course potential, and evidence confidence.
Section 1 — Scoring Criteria
Seven questions every candidate is judged against before they advance.
Practical career relevance
Does this expert solve a high-stakes work problem for ambitious professionals?
Why it matters: Maven courses work best when the learner can apply the skill directly to career advancement, business execution, or a pressing operational problem.
Operator credibility
Has this expert done the work, not just commented on it?
Why it matters: The strongest Maven experts teach from lived operating experience, not generic commentary.
Teaching signal
Does this expert already explain ideas clearly through posts, talks, templates, workshops, guides, podcasts, or public frameworks?
Why it matters: Visible teaching signal reduces launch risk because the expert has already shown they can translate expertise into learning.
Maven dependency
Does Maven solve a real packaging, launch, discovery, lead capture, community, or operating problem for this expert?
Why it matters: The best recruiting targets should have a reason to care about Maven now, not just a reason Maven would like to recruit them.
Recruiting wedge
What specific reason would this expert have to care about Maven now?
Why it matters: The wedge determines the outreach strategy. Maven should not pitch every expert the same way.
Course potential
Can this expertise become a specific Maven course, workshop, corporate program, coaching funnel, or expert business pathway?
Why it matters: A good target is not just credible. Their expertise must package into a concrete learning offer.
Evidence confidence
Is the score supported by visible public evidence, or does it still require verification?
Why it matters: No target should move to outreach until the reasoning can be traced to evidence.
Section 2 — Target Archetypes
The three shapes of expert this command center is built to recruit.
Multi-Channel Teacher
Already teaching elsewhere and could benefit from cohort format, marketplace reach, packaging support, and a stronger expert business system.
AI micro-niche
A high-demand specialist in an urgent, practical skill gap where the market needs credible instruction before the category becomes crowded.
Underleveraged advisor
Already advising, consulting, or publishing strong content, but has not fully packaged that expertise into a scalable learning product.
Section 3 — Recruiting Wedges
The four reasons an expert would care about Maven now. The wedge determines outreach strategy.
Audience Expansion
Turn public trust into a larger owned audience and lead list.
Best for: Experts with visible public content, strong audience pull, or newsletter, podcast, social, or community momentum.
Offer Expansion
Turn advising, consulting, or informal teaching into scalable courses, workshops, and services.
Best for: Experts who already help clients or followers but have not fully productized their expertise.
Category Ownership
Become the go-to teacher in a growing niche before the category is crowded.
Best for: Experts in emerging fields where the market is still forming and learners need practical guidance.
Enterprise Leverage
Package expertise for teams, companies, or executive buyers.
Best for: Experts whose knowledge can support team training, leadership alignment, enterprise adoption, or strategic decision-making.
Section 4 — Readiness Rules
Hard rules that gate progression from screened to outreach.
- Presentation evidence is not the same as verified public evidence.
- Source URLs are required before outreach.
- No expert should be marked Outreach Ready until public evidence supports the core claims.
- No expert should be marked Recruit Now unless the profile contains verified evidence for practical relevance, credibility, teaching signal, Maven dependency, recruiting wedge, and course potential.
- Missing evidence should remain visible as a gap, not be filled in by AI.
- Public-screened sample; no target is presented as a warm lead.
- Verify conflicts, offers, willingness, and Maven fit before pursuing.
Section 5 — Current App Status
Snapshot of where the approved 10-person sample sits today.