Pmf strategy
Skill ulpi-io/plugin-marketplace/plugins/kostja94/skills/pmf-strategy
When the user wants to validate product-market fit, measure PMF, or plan before scaling. Also use when the user mentions "PMF," "product-market fit," "product market fit," "Sean Ellis test," "very disappointed," "vitamin vs painkiller," "PMF validation," "premature scaling," or "validate before scale."From its SKILL.md
npx -y skills add ulpi-io/plugin-marketplace --skill pmf-strategyAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
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SKILL.md
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Strategies: Product-Market Fit
Guides product-market fit (PMF) validation and measurement. PMF occurs when a product precisely meets market needs, creating widespread demand. ~99% of startups fail primarily due to PMF issues (vitamin problems, premature scaling). Use this skill when validating before scaling, measuring PMF, or diagnosing traction problems.
When invoking: On first use, if helpful, open with 1–2 sentences on what this skill covers and why it matters, then provide the main output. On subsequent use or when the user asks to skip, go directly to the main output.
Definition
Product-market fit (Marc Andreessen): "Being in a good market with a product that can satisfy that market."
Signals: Customers buying rapidly; usage growing; word-of-mouth spreading organically; high retention, low churn.
Sean Ellis 40% Test
Question: "How would you feel if you could no longer use [product]?"
| Response | Meaning |
|---|---|
| Very disappointed | Strong PMF signal |
| Somewhat disappointed | — |
| Not disappointed | — |
| N/A – I no longer use | — |
Threshold: 40%+ answering "very disappointed" = PMF achieved. Below 40% = iterate.
| Score | Action |
|---|---|
| Below 25% | Significant changes needed |
| 25–39% | Close to PMF; iterate and improve |
| 40%+ | PMF achieved |
Best practice: Survey 40–50 active users (used product 2+ times in last 14 days). Segment by user type—some segments may have PMF while others don't.
Limitation: Combine with retention curves, engagement, organic growth; avoid false positives in early stages.
Vitamin vs Painkiller
| Type | Definition | Adoption |
|---|---|---|
| Painkiller | Solves urgent, acute problems; users actively seek solutions | Fast adoption; high retention; willing to pay |
| Vitamin | Nice-to-have; incremental benefit; users can live without | Slow adoption; expensive marketing to succeed |
~99% of failures: Solving vitamin problems instead of real pain. Validate: Would users be genuinely inconvenienced if your product vanished?
Validation: Talk to users; listen for frustration; run pre-sells; check frequency and time-sensitivity of the problem.
Key Indicators
| Indicator | Strong PMF |
|---|---|
| Retention | High; low churn |
| CAC vs CLTV | CAC decreasing relative to CLTV |
| Activation | Strong conversion to paying customers |
| Growth | Organic; word-of-mouth |
| NPS | High; enthusiastic advocacy |
Common Failures
| Failure | Avoid |
|---|---|
| Vitamin problems | Solve urgent pain, not nice-to-have |
| Vanity feedback | Use retention data, not polite opinions |
| Premature scaling | Validate PMF before scaling acquisition |
| Misalignment | Customer-problem fit before product-build |
Product Research & SaaS Context
| Area | Notes |
|---|---|
| Product positioning | Target audience; core value; competitive differentiation |
| Market research | Competitor analysis; surveys; interviews to validate assumptions |
| SaaS form | Cloud delivery; subscription; ease of use; dependency on industry standardization |
| Enterprise / ACV | Customization; data security/private deployment; procurement cycles; buy vs SaaS trade-offs |
Use: When discussing PMF for SaaS or enterprise—factor in product research rigor and ACV-specific challenges. See gtm-strategy for enterprise GTM.
PMF as Continuous Process
PMF is increasingly a continuous validation—markets evolve; re-measure as you expand. Target "PMF for a niche" first (40%+ in one segment) before broadening.
Output Format
- PMF assessment (current signals, Sean Ellis score if available)
- Vitamin vs Painkiller diagnosis
- Validation approach (interviews, pre-sells, metrics)
- Next steps (iterate vs scale)
Related Skills
- cold-start-strategy: First users; avoid large-scale paid before PMF
- indie-hacker-strategy: Indie hacker PMF; monetize day one; Ramen profitability
- paid-ads-strategy: PMF testing (small budget) vs conversion-driven (post-PMF)
- google-ads: PMF testing with landing page + $47–500
- gtm-strategy: GTM framework; PMF validation before scaling GTM
- product-launch: Launch execution; validate PMF before scaling
- retention-strategy: Retention as PMF signal; churn as anti-signal
What ships with it
Read from the repository
Just SKILL.md. No reference files, no scripts.
Gives 0 of the 12 instructions most roadmap strategy skills give in ~1.0k tokens
Counted across 614 of the 719 authors here whose files we hold, read 2026-09-06
- Check for product marketing context firstin 39 of 614, across 16 files
- Use compact with a custom summaryin 32 of 614, across 16 files
- Compact after planningin 30 of 614, across 16 files
- Do not compact mid-implementationin 30 of 614, across 17 files
- Structure launch marketing across three channel typesin 21 of 614, across 5 files
- Read the compaction suggestionin 20 of 614, across 10 files
- Write before compactingin 17 of 614, across 11 files
- Write important context to files before compactingin 16 of 614, across 5 files
- Recruit early users one-on-onein 16 of 614, across 3 files
- Identify 3-5 core content pillarsin 15 of 614, across 9 files
- Target a specific keyword or questionin 14 of 614, across 4 files
- Stagger announcements to maintain momentumin 14 of 614, across 4 files
Said here and by no other author read
- Measure product-market fit using the Sean Ellis test
- Survey active users to calculate the PMF score
- Target 40 percent very disappointed responses for PMF
- Segment survey responses by user type
- Solve urgent pain rather than vitamin problems
- Validate product-market fit before scaling acquisition
Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.