Cpo Advisor
Run a structured PMF playbook—segment power users, measure retention and qualitative pull, and avoid mistaking polite feedback for fit before you scale build and launch spend.
Overview
cpo-advisor is an agent skill most often used in Validate (also Grow, Operate) that guides solo builders through finding product-market fit, measuring pull with multiple signals, and avoiding false positives before scali
Install
npx skills add https://github.com/alirezarezvani/claude-skills --skill cpo-advisorWhat is this skill?
- Defines PMF as pull (referrals, distress when down) vs vanity signals like lone NPS without growth
- Step 1: compare retained D90+ vs churned users on attributes, then validate with 10 power-user calls
- Step 2: triangulate three PMF signals (retention curves plus complementary metrics in the full playbook)
- Explicit anti-patterns: enterprise lock-in churn and 'users like it' without organic pull
- Operational steps, not theory—export lists, interview scripts, and falsifiable segment hypotheses
- 3 PMF signals measured together
- 5-10 attributes to compare retained vs churned
- 10 retained power-user calls recommended in Step 1
Adoption & trust: 525 installs on skills.sh; 17.5k GitHub stars; 2/3 security scanners passed (skills.sh audits).
What problem does it solve?
You have some users and positive comments but cannot tell whether demand is real pull or polite interest, so every roadmap bet feels like a guess.
Who is it for?
Founders with early signups or a thin MVP who need a repeatable PMF checklist and interview prompts without hiring a fractional CPO.
Skip if: Teams that only want a one-number dashboard with no cohort exports or customer calls, or products with zero users yet and no hypothesis to test.
When should I use this skill?
You have early users or a prototype and need to decide if you have real PMF before scaling build, pricing, or distribution.
What do I get? / Deliverables
You leave with a prioritized highest-PMF segment, a measurement plan across the playbook’s PMF signals, and clearer go/no-go criteria before you commit the next build cycle.
- Highest-PMF segment hypothesis
- PMF measurement plan across playbook signals
- Interview question bank and falsifiable fit criteria
Recommended Skills
Journey fit
Spans multiple journey phases - primary shelf plus alternate fits below.
PMF is the core validate question: whether the idea deserves a full build; this skill shelves here as the canonical pre-commitment proof framework. Scope and positioning decisions depend on which customer segment shows pull; the playbook starts with finding that segment before broad measurement.
Where it fits
Narrow ICP after comparing D90 retainers to churned signups on company size and first-action attributes.
Check whether willingness-to-pay aligns with the segment that shows organic pull, not just inbound demos.
Re-run retention-curve interpretation when growth stalls despite positive survey scores.
Delay broad paid spend until Sean-Ellis-style disappointment signals clear in the highest-PMF slice.
Audit whether contract renewals hide churn so you do not confuse lock-in with fit.
How it compares
Use instead of generic 'talk to users' advice when you need explicit retention, segment, and anti-vanity rules in one procedural playbook.
Common Questions / FAQ
Who is cpo-advisor for?
Solo and indie builders shipping SaaS or agent-backed products who own positioning and must decide if they have real pull before scaling build and marketing.
When should I use cpo-advisor?
During validate when scoping who the product is for; during grow when retention flattens and you need to re-segment; during operate when enterprise renewals mask underlying churn risk.
Is cpo-advisor safe to install?
It is procedural guidance only—review the Security Audits panel on this Prism page before installing any skill from the repo, and avoid pasting live customer PII into agent chats when following export steps.
SKILL.md
READMESKILL.md - Cpo Advisor
# PMF Playbook How to find product-market fit, measure it, and not lose it. Steps, not theory. --- ## What PMF Actually Is PMF is when a product pulls users in rather than pushing them. Signals: - Users find the product without you telling them about it - They're upset when it doesn't work - They bring their colleagues, their friends, their boss - They build workarounds when a feature is missing PMF is not: - Users saying they like it - A good NPS score with flat growth - Enterprise customers who are locked in but churning at contract end --- ## Step 1: Find Your Best Customers First Before measuring PMF across everyone, find the segment where PMF is strongest. **How:** 1. Export a list of all churned users and all retained users (D90+) 2. Identify 5-10 attributes to compare: company size, industry, job title, signup source, first action taken, time to first value 3. Find the attributes that are over-represented in retained vs. churned 4. That's your highest-PMF segment **This is not an analytics project.** Call 10 retained power users. Ask: - "What were you doing before you found us?" - "What would you use if we shut down tomorrow?" - "Who else in your life has this problem?" The segment where this conversation is easy and the answers are specific — that's where your PMF is. --- ## Step 2: Measure the Three PMF Signals Run all three. They measure different things. One signal without the others is misleading. ### Signal 1: Retention Curves **Method:** 1. Cohort users by week or month of first use 2. Calculate % still active at D1, D7, D14, D30, D60, D90 3. Plot the curve for each cohort **Interpretation:** | Curve Shape | What It Means | |-------------|--------------| | Drops to zero | No PMF. Product doesn't solve a recurring problem. | | Drops and keeps dropping | Weak PMF. Some people find value, but not enough to keep coming back. | | Drops then flattens above 0 | PMF signal. A core group finds ongoing value. | | Flattens higher with each newer cohort | PMF improving. You're learning. | **Benchmarks:** | Segment | D30 Retention (PMF threshold) | D90 Retention (strong PMF) | |---------|-------------------------------|---------------------------| | Consumer | > 20% | > 10% | | SMB SaaS | > 40% | > 25% | | Enterprise SaaS | > 60% | > 45% | | Marketplace (buyers) | > 30% | > 20% | | PLG (free-to-paid) | > 25% free D30, > 50% paid D30 | > 15% free D90 | **If retention is below threshold:** - Don't run more acquisition. You'll just churn faster. - Find the users who ARE retained. Understand why. Build for them. --- ### Signal 2: Sean Ellis Test Survey users with one question: "How would you feel if you could no longer use [Product]?" **Answers:** - Very disappointed - Somewhat disappointed - Not disappointed (it really isn't that useful) - N/A — I no longer use [Product] **Scoring:** - Count only "very disappointed" responses - Divide by total non-churned respondents - PMF threshold: **> 40% "very disappointed"** **Sample size requirement:** Minimum 40 responses. Under 40, the signal is noisy. **When to run it:** - When you have 100-500 active users - Quarterly for ongoing tracking - After major product changes **What to do with "somewhat disappointed":** Don't lump them with "very disappointed." The delta between "somewhat" and "very" is where your retention problem lives. Interview people in the "somewhat" group. What's missing? Why only somewhat? **When score is 20-35%:** You have a segment with PMF. Find them. Ask what they love. Run a separate survey for just that segment. **When score is < 20%:** Your core value proposition isn't working. This is not a retention tactics problem. Revisit the fundamental problem you're solving. --- ### Signal 3: Organic Growth and Referral **Metric:** % of new signups that came from existing user referral, word of mouth, or organic search — without a paid incentive. **Threshold:** > 20% of new users are coming organically without incentive programs. **How