pmf-assessment

Pack Availability Guard

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PMF Assessment — Product-Market Fit Measurement

Report-First Approval Gate

Default to report-only: present findings, evidence coverage, assumptions, recommended artifact path, and proposed file changes in a pre-approval alignment page plus a concise conversation summary for user approval before creating or updating canonical research, spec, or task files.

Do not write or overwrite synthesized deliverables until the user explicitly approves, unless the user invoked an explicit write/update/fix mode or clearly asked to write files upfront. Raw evidence capture may be persisted before analysis when reproducibility requires it; report those raw paths separately and still gate synthesized research/report writes.

When stopping for approval, build and attempt to open the alignment preview page first, then ask the user to review it and approve, question, or request adjustments. Do not include Recommended next skill, Recommended next command, or downstream routing language. The approval request itself is the next action. Only emit next-skill routing after the approved artifact has been written or updated.

Designs a Sean Ellis PMF survey and qualitative signal analysis framework for measuring product-market fit post-launch. Combines the quantitative "very disappointed" threshold (40%) with qualitative signal analysis from customer feedback, usage patterns, and market indicators. This is a post-launch measurement skill — it should run after the product has real users.

Evidence And Feedback Handling

Treat user feedback as input to evaluate, not as automatic ground truth.

• For factual, evidentiary, technical, or source-backed claims: verify against available evidence. If the user appears to misunderstand the evidence or states something factually incorrect, push back clearly and cite the evidence. Do not rewrite findings merely to agree.
• For taste, brand, positioning preference, risk appetite, prioritization, or other subjective judgment calls: weigh user feedback heavily and adapt the recommendation unless it conflicts with verified evidence.
• When feedback mixes facts and preference, separate them explicitly: correct the factual part, then incorporate the preference where it is a legitimate judgment call.
• When uncertain, say what is known, what is inferred, and what would change the conclusion.

Prerequisites

• Hard: research/metrics.md (or research/{slug}/metrics.md) must exist. If not, tell the user to run /metrics first and stop.
• Soft: Read these if they exist:
  • research/icp.md — target customer segments, pain points, jobs-to-be-done
  • research/journey-map.md — critical moments, aha moment, drop-off points
  • research/customer-feedback.md — real user language, complaints, praise, churn signals

Process

0a. Product Path Manifest

Read research/.progress.yaml when present. Normalize active_path (singular legacy) to active_paths (plural list) when reading; treat legacy abandoned as archived and exclude archived/deferred/revisit/promoted paths plus research/_archive/ scopes from active target selection. Scope the PMF assessment to the active product path by default. When PMF signals suggest a deferred product path has stronger product-market fit than the active path, add a ## Product Path Implications section recommending /product-line activate.

0. Product-Path Scope Resolution

Resolve research scope by product path before using code or app structure as a hint:

1. If $ARGUMENTS names a non-archived research/{slug}/ directory or a product-path ID whose scope_path points there, use that path. Treat {slug} as the product/app name, not the ICP, audience, or segment label.
2. If $ARGUMENTS names only research/_archive/{slug}/ or a manifest entry with status: archived or legacy status: abandoned, stop and warn that the path is archived; do not write or update scoped outputs there.
3. Read research/.progress.yaml when present. Normalize legacy active_path to active_paths on read and write back active_paths on manifest updates. Treat legacy abandoned as archived; exclude archived, abandoned, deferred, revisit_candidate, promoted, and any scope_path under research/_archive/ from active target selection.
4. If active product paths exist in the manifest, use those paths. If multiple active paths exist, ask which one to target unless this skill explicitly supports cross-path output.
5. If no active manifest target exists, list non-archived product directories under research/, excluding research/_archive/ and dot directories. Auto-select only when exactly one exists; ask when multiple exist.
6. If no product directories exist, use flat research/ single-product mode.
7. Detect monorepo/app/package structure only as a secondary hint. Suggest creating a missing research/{slug}/ product path when code clearly exposes an app, but do not require code or monorepo detection before using research/{slug}/.

When product path {slug} is active, read and write research under research/{slug}/, specs under specs/{slug}/, and treat top-level research/*.md files as flat-mode documents or cross-path summaries.

1. Load Context

• Read research/metrics.md — success targets, north star metric, current performance data
• Read research/icp.md if it exists — target segments, pain points, jobs-to-be-done
• Read research/journey-map.md if it exists — critical moments, aha moment, engagement patterns
• Read research/customer-feedback.md if it exists — real user language, sentiment, churn signals
• Read CLAUDE.md, README, and key source files for product context

2. Launch Check

Detect whether the product has live users. Look for evidence:

• research/customer-feedback.md exists with real user quotes
• Analytics references in metrics or other research docs
• Deployed URLs in README or CLAUDE.md
• User counts, revenue figures, or usage data in any research file

If no evidence of live product:

Use AskUserQuestion:

• "I don't see evidence of a live product with real users. PMF assessment requires actual user data. Would you like to design the survey framework now for future use, or should we focus on /spec-interview or /roadmap to get to launch first?"

If the user wants to proceed anyway, continue but mark all outputs as "pre-launch framework — requires live data to score."

3. Research Sean Ellis Methodology

Use WebSearch with 4-6 targeted queries:

1. Core methodology — "Sean Ellis PMF survey methodology", "very disappointed question PMF"
2. Domain-specific signals — "product market fit signals [category]", "PMF measurement [domain]"
3. Advanced frameworks — "superhuman PMF engine", "Rahul Vohra PMF framework"
4. Qualitative indicators — "qualitative PMF indicators", "product market fit signs [industry]"
5. Threshold context — "40% very disappointed threshold context", "PMF score benchmarks [category]"

4. Design PMF Survey

Core question: "How would you feel if you could no longer use [product]?"

• Very disappointed
• Somewhat disappointed
• Not disappointed
• N/A — I no longer use [product]

Follow-up questions by response:

For "Very disappointed" respondents:

• What is the main benefit you get from [product]?
• What would you likely use as an alternative?
• How would you describe [product] to a friend?

For "Somewhat disappointed" respondents:

• What would you likely use instead?
• What could we improve to make [product] indispensable?

For "Not disappointed" respondents:

• Why not? What's missing for you?
• What were you hoping [product] would do that it doesn't?

Product-specific follow-ups (3-5 additional questions based on product context):

• Tailored to ICP jobs-to-be-done and journey critical moments
• Focused on understanding why users feel the way they do, not just that they do

Use AskUserQuestion:

• "Here's the PMF survey design. Are the follow-up questions tailored enough to your product? Any segments you want surveyed separately?"

5. Qualitative Signal Analysis

If research/customer-feedback.md exists, analyze for PMF signals:

Signal Category	What to Look For	PMF Indicator
Organic word-of-mouth	Unprompted referrals, social mentions, "told my friend about"	Strong positive
Usage frequency	Daily/weekly active patterns, session depth	Engagement strength
Emotional language	"love", "can't live without", "game-changer" vs. "it's fine", "okay"	Sentiment intensity
Feature requests vs. complaints	Ratio of "add X" to "fix Y" — wanting more vs. wanting less broken	Product maturity
Churn reasons	Why people leave — alternatives, missing features, price, no need	Fit gaps
Expansion signals	Upgrades, team invites, increased usage, API adoption	Growth readiness

If no feedback file exists, design the signal checklist for future analysis and note which signals to start tracking immediately.

6. Scoring Framework

Quantitative threshold (Sean Ellis):

• Strong PMF: >40% "very disappointed" responses
• Moderate PMF: 25-40% "very disappointed" responses
• Weak PMF: <25% "very disappointed" responses

Qualitative signals checklist:

1. Organic word-of-mouth happening without prompting
2. Usage frequency increasing or stable (not declining)
3. Emotional language in feedback (strong positive sentiment)
4. Feature requests outnumber complaints
5. Low voluntary churn / high retention
6. Expansion behavior (upgrades, invites, deeper usage)

Combined scoring:

• Strong PMF: >40% "very disappointed" AND 3+ qualitative signals present
• Moderate PMF: 25-40% "very disappointed" OR 2 qualitative signals present
• Weak PMF: <25% "very disappointed" AND fewer than 2 qualitative signals

Use AskUserQuestion:

• "Does this scoring framework match your context? Any signals more/less important for your market?"

7. Populate Next Steps

Include 3-5 applicable items with "Pick one:" framing, conditional on PMF strength:

Weak PMF:

• /customer-feedback [top user complaint] — Deep-dive on the weakest area to understand what's broken
• /icp — Re-examine customer fit; you may be selling to the wrong segment
• /journey-map — Map where users drop off to find the biggest gap

Moderate PMF:

• /growth-model — Model growth levers to strengthen engagement before scaling
• /hook-model — Design habit loops around what's already working
• /customer-feedback [most-requested feature] — Deep-dive on what "somewhat disappointed" users want more of

Strong PMF:

• /growth-model — Model growth channels to scale what's working
• /roadmap — Plan the scaling phase with confidence
• /gtm — Build go-to-market plan grounded in proven fit

8. Write Output

Only after the user confirms, write the output files.

9. Downstream Impact Check

After writing, check for downstream research documents that may be affected.

Downstream documents to check (use {slug}/ prefix when product-path scope is active):

• research/metrics.md
• research/growth-model.md

For each existing downstream document:

1. Read it — focus on PMF-related sections, success thresholds, growth assumptions
2. Identify conflicts where assumptions don't align with the PMF assessment findings
3. Note each conflict: file, section, stale claim, what it should now say

Classify the impact:

• None: No downstream docs exist, or no conflicts. Skip display.
• Minor (1-2 small conflicts): Display inline.
• Major (3+ conflicts OR PMF level changes growth assumptions, success targets need revision, segment focus shifted): Display and recommend /reconcile-research.

Output

research/pmf-assessment.md (or research/{slug}/pmf-assessment.md)

# PMF Assessment

> Based on: research/metrics.md[, research/icp.md, research/journey-map.md, research/customer-feedback.md]
> Date: [current date]
> Methodology: Sean Ellis PMF Survey + Qualitative Signal Analysis
> Launch Status: Live with users | Pre-launch framework

## Summary
[2-3 sentences: the PMF thesis — current fit level, key evidence, primary recommendation]

## Launch Status Assessment

**Status**: [Live with N users / Pre-launch / Early access]
**Evidence**: [what signals indicate launch status]
**Data availability**: [what data exists vs. what's needed for full scoring]

## PMF Survey Design

### Core Question

"How would you feel if you could no longer use [product]?"

| Response | Purpose |
|----------|---------|
| Very disappointed | PMF signal — these are your core users |
| Somewhat disappointed | Potential — understand what's missing |
| Not disappointed | Fit gap — understand why not |
| N/A — no longer use | Churn signal — understand what happened |

### Follow-Up Questions

#### For "Very Disappointed" Respondents
1. [Question] — *Rationale: [why this question matters for this product]*
2. [Question] — *Rationale: [why]*
3. [Question] — *Rationale: [why]*

#### For "Somewhat Disappointed" Respondents
1. [Question] — *Rationale: [why]*
2. [Question] — *Rationale: [why]*

#### For "Not Disappointed" Respondents
1. [Question] — *Rationale: [why]*
2. [Question] — *Rationale: [why]*

#### Product-Specific Follow-Ups (All Respondents)
1. [Question] — *Rationale: [why]*
2. [Question] — *Rationale: [why]*

### Recommended Sample Size
- **Minimum viable**: 40 responses (for directional signal)
- **Statistically meaningful**: 100+ responses
- **Ideal**: 200+ responses with segment breakdown

### Segment Breakdown

Survey these segments separately for segment-level PMF signals:

| Segment | Why Separate | Expected Difference |
|---------|-------------|-------------------|
| [segment] | [reason] | [hypothesis] |

## Qualitative Signal Analysis

| Signal | Current Evidence | Status |
|--------|-----------------|--------|
| Organic word-of-mouth | [evidence or "awaiting data"] | Present / Absent / Awaiting data |
| Usage frequency patterns | [evidence or "awaiting data"] | Present / Absent / Awaiting data |
| Emotional language | [evidence or "awaiting data"] | Present / Absent / Awaiting data |
| Feature requests vs. complaints | [evidence or "awaiting data"] | Present / Absent / Awaiting data |
| Churn reasons | [evidence or "awaiting data"] | Present / Absent / Awaiting data |
| Expansion signals | [evidence or "awaiting data"] | Present / Absent / Awaiting data |

**Qualitative signals present**: [count] / 6

## PMF Scoring Framework

### Quantitative Threshold (Sean Ellis)

| Level | "Very Disappointed" % | Interpretation |
|-------|----------------------|----------------|
| Strong | >40% | Clear product-market fit — scale with confidence |
| Moderate | 25-40% | Promising but not locked in — improve before scaling |
| Weak | <25% | Fit not established — iterate on product or segment |

### Signal Weights

[Note any domain-specific factors that shift the threshold. The 40% number is a guideline, not gospel — context matters.]

### Combined Assessment

| PMF Level | Quantitative | Qualitative (3+ of 6) | Action Mode |
|-----------|-------------|----------------------|-------------|
| Strong PMF | >40% | AND 3+ signals | Scale |
| Moderate PMF | 25-40% | OR 2 signals | Strengthen |
| Weak PMF | <25% | AND <2 signals | Iterate |

## Current PMF Estimate

[If data available:]
**Quantitative score**: [X]% "very disappointed" — [Strong/Moderate/Weak]
**Qualitative signals**: [N]/6 present
**Combined assessment**: [Strong/Moderate/Weak] PMF
**Confidence**: [High/Medium/Low] — based on [sample size, data quality, recency]

[If pre-launch:]
> **Pre-launch framework** — no PMF score can be calculated without live user data. Use this framework once you have 40+ active users to survey. Focus areas before measuring: [specific recommendations].

## Recommended Actions by PMF Level

| PMF Level | Primary Action | Secondary Action | Avoid |
|-----------|---------------|-----------------|-------|
| Weak | Iterate on core value prop or re-examine segment fit | Interview churned users, test with adjacent segment | Scaling spend, feature bloat |
| Moderate | Double down on what "very disappointed" users love | Improve for "somewhat disappointed" segment | Premature scaling, pivoting away from what works |
| Strong | Scale acquisition channels | Expand to adjacent segments | Over-iterating on core (it works), ignoring ops scaling |

## Strategic Implications

### What This Means for Product
[How PMF level should influence product decisions]

### What This Means for Growth
[How PMF level should influence growth investment]

### What This Means for Fundraising
[How PMF level affects investor conversations and timing]

<!-- Only include when downstream impact is Minor or Major -->
## Downstream Impact

> Checked: [list of downstream docs checked]
> Impact: Minor | Major

### Conflicts Found

1. **research/[file].md** — [Section Name]
   - **Stale**: "[exact quote]"
   - **Now**: [what PMF assessment says instead]

[For Major only:]
> **Recommended action**: Run `/reconcile-research` to audit and fix all affected downstream documents.

## Next Steps

Pick one:
- [conditional items from step 7, based on PMF level]

research/pmf-assessment-search-log.md (or research/{slug}/pmf-assessment-search-log.md)

Raw research log — queries, findings, evidence for each PMF assessment decision.

Create the research/ directory if it doesn't exist.

Task Classification

When this skill produces follow-up work, file it by execution semantics:

• Immediately actionable implementation or documentation work goes in tasks/todo.md.
• Human-only external actions tied to automated steps go in tasks/manual-todo.md with _(blocks: Step N.X)_ or _(after: Step N.X)_; repo edits, SDK wiring, generated assets, local commands, tests, audits, and authenticated CLI/API work stay in tasks/todo.md.
• One-time condition-gated records, baselines, or future measurements go in tasks/record-todo.md with source, condition, non-blocking reason, evidence, and promotion rule.
• Cadence-based reviews, playtests, adoption checks, investor updates, retros, or docs-health checks go in tasks/recurring-todo.md with cadence, owner/agent, next due, evidence path, and escalation conditions.
• Do not put non-blocking records or recurring obligations in tasks/todo.md unless they have been explicitly promoted into current execution work.

Constraints

• Requires metrics. PMF measurement without success targets is directionless. If research/metrics.md doesn't exist, stop and run /metrics first.
• Honest about data availability. If no live users, say so clearly — do not fabricate PMF scores.
• The 40% threshold is a guideline, not gospel. Context matters — note when domain-specific factors change the threshold (e.g., enterprise B2B with few customers, two-sided marketplaces, regulated industries).
• Present before writing. Never write output files until the assessment has been presented and validated.
• Do not overwrite existing research/pmf-assessment.md without asking the user first.
• PMF is a spectrum, not binary. Frame results as a continuum with actionable next steps at each level. Avoid "you have PMF" / "you don't have PMF" absolutes.

Alignment Page

When this skill produces durable deliverables (research, specs, plans, reports, prototypes, or any document output), build a full-depth HTML alignment page following ALIGNMENT-PAGE.md in this skill's directory. Output: alignment/pmf-assessment-{topic}.html.

Default Shipping Contract

Follow the shared shipping contract convention in CLAUDE.md.