name: pmf description: > Full product-market fit cycle for one product — from initial hypothesis to post-launch metrics. 10 stages: setup → hypothesis (7 dimensions) → market research → risk synthesis → DVF validation → interview prep → field → interview synthesis → MVP → metrics (Sean Ellis + retention + Levels of PMF) → iterate. Resumes between sessions based on the project folder state. Bilingual (English + Russian) — picks the language during first-run setup. TRIGGER on ANY:

• "do PMF for [product]" / "I need product market fit for X" / "PMF [name]"
• "start PMF cycle" / "I want to go through PMF" / "help me validate [idea]"
• "continue PMF" / "continue PMF [name]"
• "check PMF" / "what stage is my PMF at" / "show my PMF projects"
• "is my product ready to launch"
• "сделай PMF для [продукта]" / "нужен product market fit для X" / "PMF [имя]"
• "запусти PMF цикл" / "хочу пройти PMF" / "помоги валидировать [идею]"
• "продолжаем PMF" / "продолжай PMF [имя]"
• "проверь PMF" / "на каком этапе у меня PMF" / "покажи мои PMF проекты"
• "готов ли мой продукт к запуску"
• User mentions a product and wants to validate it systematically

metadata: version: "1.0.0"

PMF — Product-Market Fit Engine

This skill takes a product through the full product-market fit cycle. One skill = one orchestrator, not a bundle. All 10 stages live inside it. The skill figures out which stage the project is in and proposes the next step.

A PMF cycle takes months. The skill accepts that pace: it resumes between sessions, remembers where you left off, and does not rush.

⛔ Critical rules

1. No subagents for research. Stage 2 (market research) is done sequentially in the main session via Exa/WebSearch. The user sees every search and every result and can intervene. Researching analogs is substantive work, not "file lookup."

1. The PMF projects folder is configurable. Stored in ~/.claude/skills/pmf/config.md. Default: ~/pmf-projects/. The skill writes everything for a given product into <projects_path>/<product-slug>/. Not CWD, not somewhere else.

1. Confidence can decrease. That is normal. If the confidence in narrative-v2 is lower than v1 — the data is contradicting the hypothesis, and that is a useful signal, not a reason to inflate the number.

1. Terminology: "assumption" (not "hypothesis") in Stage 4. In stages 1–3 — "hypothesis." In stage 4 with DVF — only "assumption." This is methodologically important (David Bland).

1. Tone is calm. No exclamation marks, no dramatization. The user is doing PMF not to be cheered on, but to understand what works and what does not.

1. Stages 6 (interviews) and 8 (MVP) are outside the skill. The skill prepares the guide / gives metrics instructions, but does NOT try to "conduct interviews" or "launch an MVP." This is the user's work in the real world, weeks or months.

Step 0 — Configuration (first run)

On every trigger, before doing anything else, the skill reads its config:

~/.claude/skills/pmf/config.md

Expected format:

language: en        # or "ru"
projects_path: ~/pmf-projects

If config.md does not exist OR is missing values

Ask the user, one question at a time. Each question can be skipped (the default is used).

Question 1 — Language:

"What language should we work in for this skill — English or Russian? (default: English)"

English: skill communicates in English, references loaded from references/en/.

Russian / Русский: skill communicates in Russian, references loaded from references/ru/.

Question 2 — Projects path:

"Where do you want PMF projects stored? (default: ~/pmf-projects/)"

Examples:

- ~/pmf-projects/ (default — home folder)

- ~/Documents/PMF/

- D:/Work/PMF/ (Windows)

- /Users/me/Projects/PMF/ (macOS)

The skill creates one subfolder per product inside this path.

Save the config: After both answers (or skips → defaults), write ~/.claude/skills/pmf/config.md with the chosen values. Confirm to the user: "Config saved. Default language is <lang>, projects folder is <path>. You can change this anytime by editing the file."

If config.md exists

Read it. Use those values for the rest of the session. All references must be loaded from `references/<language>/...`. All communication happens in the configured language.

Step 1 — Auto-start (what the skill does first on every trigger after config)

1a. Read the listing of the projects folder

ls <projects_path>/

• Folder does not exist → create it (mkdir)
• Folder is empty → no active projects
• Subfolders exist → each = one active PMF project (product slug)

1b. Parse the user's intent

1c. Determine the stage of the chosen project from folder contents

Check files in priority order (later stages first):

1d. Show status and ask for the next action

Output format:

📍 PMF project: <product-slug>
   Product type: <type> | Context: <org>
   Current stage: Stage N — <name>
   Ready artifacts: <list of .md files>
   Last updated: <file date>
 
Next step: Stage N+1 — <name>
   What it is: <one-line stage goal>
   Artifact: <filename>
 
Move on to Stage N+1? Or go back to stage X?

After the user agrees — move to the chosen stage.

Step 2 — Setup (Stage 0)

When it runs: auto-start found an empty folder or an explicit "new PMF for X." If 00_setup.md already exists in the folder — Stage 0 is skipped.

Goal: collect basic product and team context before working on the hypothesis. Short stage (10–20 minutes).

What is collected:

1. Product name and folder slug
2. Product type (B2C / B2B / Marketplace / DTC / Services / Internal / Other)
3. Organizational context (Zero-to-one / Established / Extension)
4. Team Pre-Flight Check (3 questions: Founder-Market Fit, Skill gaps, Conviction-flexibility) → risk flag

Artifact: 00_setup.md in the project folder.

The Pre-Flight Check happens HERE, not in Stage 1. It is about the team, not the product. Stage 1 will read the finished results from 00_setup.md and copy them into the narrative.

Detailed logic, the `00_setup.md` template, quality gates, common pitfalls: references/<lang>/stage-0-setup.md.

Pipeline overview — 10 stages

For the stage-to-stage transition map, see references/<lang>/pipeline-overview.md.

Step 3 — Hypothesis (Stage 1)

Goal: turn the product idea into a structured hypothesis across 7 PMF dimensions with honest confidence scores.

7 dimensions (detail — references/<lang>/7-dimensions.md):

1. Problem to Solve — outcome-motivation gap
2. Target Audience — 2-3 defining attributes, Now vs Future segments
3. Value Proposition — tagline + 3-5 benefits (not features)
4. Competitive Advantage — one of the 7 Powers (Helmer)
5. Growth Strategy — short-term traction (first 1K) ≠ long-term sustainable (100K+)
6. Business Model — equation, pricing, LTV, cost structure
7. Timing / Why Now — what changed, why now in particular

Before dimensions: Team Pre-Flight Check — 3 questions (references/<lang>/stage-1-hypothesis.md).

After dimensions: confidence assessment 1–10 for each + identification of the riskiest.

Artifact: narrative-v1.md from the template references/<lang>/template-narrative.md (structured) or references/<lang>/template-narrative-prose.md (prose, for stakeholders).

Detailed stage logic: references/<lang>/stage-1-hypothesis.md.

Step 4 — Market Research (Stage 2)

Goal: find analogs (successful companies validating the dimension) and antilogs (known failures on the dimension) for each of the 7 dimensions.

Method: sequentially via Exa (preferred) or WebSearch (fallback). Per dimension — 3-5 analogs and 2-3 antilogs. Total ~14-21 searches.

Adaptive threshold:

• Mature markets (SaaS, e-commerce, marketplace): analog = $10M+ revenue
• Emerging markets (AI, web3, new categories): analog = $1M+ ARR or 10K+ active users

⛔ Do NOT use the Agent tool / subagents. Search is done by direct calls to mcp__exa__web_search_exa or WebSearch in the main session. This is a hard rule.

Artifact: market-research.md from the template references/<lang>/template-market-research.md.

If context overflows: split into 2 passes (dim 1-4 in one session, dim 5-7 in the next). This is normal for a months-long cycle.

Detailed logic and search strategies per dimension: references/<lang>/stage-2-research.md.

Step 5 — Synthesis (Stage 3)

Goal: condense the research into risk-prioritization, identify the riskiest dimension, update the narrative to V2.

Risk scoring formula:

Risk Score = (10 - Evidence Score) × Failure Impact

Failure Impact defaults (can be recalibrated for the specific product):

Cross-fit analysis (mandatory):

• Channel-Model Fit — does the growth channel fit the business model? (example conflict: enterprise sales + freemium pricing)
• Model-Market Fit — does the business model fit the target audience? (example conflict: subscription for an audience that does not pay for software)

Artifacts:

• risk-prioritization.md (from the template references/<lang>/template-risk-prioritization.md)
• narrative-v2.md (an update of V1 based on research data, with an explicit version history changelog)

Decision tree after synthesis:

• Overall confidence > 7 + a riskiest dimension exists → Stage 4 (validate the riskiest) or jump to Stage 5 (interviews)
• Overall confidence 4–7 → Stage 4 is mandatory
• Overall confidence < 4 → return to Stage 1 (rethink hypothesis) or do more research

Detailed logic: references/<lang>/stage-3-synthesis.md.

Step 6 — Validate / DVF (Stage 4)

Goal: take the riskiest dimension, decompose it into 9 assumptions across DVF (Desirability × Viability × Feasibility), prioritize via a 2×2 (importance × evidence), and design an experiment for the riskiest assumption.

DVF categories (detail — references/<lang>/dvf-framework.md):

• Desirability — does the user need this? (only user needs, nothing about money or technical feasibility)
• Viability — is this profitable for the business? (everything financial — pricing, unit economics, LTV, CAC, costs)
• Feasibility — can we build it? (operational + technical + regulatory)

Assumption format: "I believe..." 9 of them (3 per category).

Regulatory sub-check: if product type = AI / fintech / healthtech → automatically add 1-2 regulatory assumptions to Feasibility.

2×2 map: importance (high/low) × evidence (strong/weak). The riskiest = high importance + weak evidence.

Experiment brief for the risk-assumption:

• Assumption verbatim
• What learning?
• Experiment type (one of the standards: Customer Interview, Smoke Test, Concierge, Survey, Prototype, Landing Page)
• How to run (3 steps)
• How to measure (success + failure signals, concrete thresholds)
• Estimated effort
• Remaining uncertainty

Artifacts:

• assumptions-map.md (9 assumptions + 2×2)
• experiment-brief.md (for the risk-assumption)

Detailed logic: references/<lang>/stage-4-validate.md.

Step 7 — Interview Prep (Stage 5)

Goal: prepare the guide for in-depth interviews on the 2-3 riskiest dimensions from risk-prioritization.

Guide structure:

1. Introduction script (greeting, purpose, consent, recording)
2. Screening questions (2-3 questions to check audience fit)
3. Thematic blocks: 5-7 open questions per risk-dimension
4. Closing (thanks, next steps, incentive)

Question rules:

• Open, not leading
• About past behavior, not hypothetical futures
• About concrete situations, not general opinions
• Coverage matrix: every question maps to a dimension and assumption

Quantity: at least 15-20 interviews, saturation usually at 12-20.

Artifacts:

• interview-guide.md
• interviews/note-template.md (template for one note for the user)

Detailed logic: references/<lang>/stage-5-interview-prep.md.

Step 8 — Field Interviews (outside the skill)

This is a waiting state. When resumed at this stage, the skill says:

📍 Stage 6 — field (interviews)
   Guide ready: interview-guide.md
   Notes collected: <count> in interviews/notes/
 
What's new? Ready to move to synthesis (Stage 7)?
- How many interviews have you done in total?
- Are there obvious patterns already?
- When are you planning to finish the field?

The skill does not try to conduct interviews. It only prepares and then processes the results.

With ≥ 1 note in interviews/notes/ — moving to Stage 7 is possible (though 15+ is optimal).

Step 9 — Interview Synthesis (Stage 7)

Goal: read all interview notes, extract patterns per dimension, update confidence scores, evolve the narrative to V3.

Process:

1. Read all notes from interviews/notes/ (not in a batch — one at a time, to avoid mixing respondents)
2. For each dimension extract: pattern (what they say) + supporting evidence (how many respondents confirm) + key quotes (2-3 verbatim) + confidence change
3. Cross-dimensional insights (patterns spanning several dimensions)
4. Surprises (findings that contradict the hypothesis)
5. Updated risk assessment table (pre vs post)
6. Recommended next steps (build MVP / more validation / pivot)

Artifacts:

• interview-synthesis.md (from the template references/<lang>/template-interview-synthesis.md)
• narrative-v3.md (an update of V2 based on field data)

Loop detection: if confidence dropped between V2 and V3 → flag + recommendation (return to Stage 4 for deeper validation, or to Stage 1 to revisit the hypothesis).

Detailed logic: references/<lang>/stage-7-interview-synthesis.md.

Step 10 — MVP Launch (outside the skill)

A waiting state. The skill says:

📍 Stage 8 — MVP launch
   narrative-v3 is ready, the hypothesis is validated.
 
When you are ready:
- Launch the MVP to a minimally viable audience
- Collect the first ~40 active users
- Come back for Stage 9 (metrics)
 
What do you want to discuss about the launch?

The skill is only useful here as a sounding board — it does not try to "launch the MVP."

Step 11 — Metrics (Stage 9)

Goal: set up post-launch PMF measurement through 3 instruments.

Sean Ellis Survey (references/<lang>/sean-ellis-survey.md):

• Question: "How would you feel if you could no longer use [product]?"
• Options: Very disappointed / Somewhat disappointed / Not disappointed / N/A
• Threshold: ≥ 40% Very disappointed = PMF
• Minimum 40 responses
• Distribute only to active users (not the newsletter list)
• The skill generates the question text + distribution instructions, does not collect data itself

Retention Cohorts:

• Cohort table (week 1, 2, 3, 4, 5...) × percentage of returning users
• PMF signal: the curve flattens, does not drop to zero
• Table template

First Round Levels of PMF (references/<lang>/levels-of-pmf.md):

• Level 1: Nascent (early signals)
• Level 2: Developing (some signals, but not stable)
• Level 3: Strong (stable retention + WOM growth)
• Level 4: Extreme (mega-signals — non-linear growth, hype)
• Each level has its own signals across satisfaction / demand / efficiency

Artifact: metrics-dashboard.md (from the template references/<lang>/template-metrics-dashboard.md)

Flow: the skill creates the template → the user collects data over weeks → comes back → the skill interprets and recommends Stage 10.

Detailed logic: references/<lang>/stage-9-metrics.md.

Step 12 — Iterate (Stage 10)

Goal: based on the metrics, make a decision and lock it in.

Decision tree:

• Sean Ellis ≥ 40% + retention flattens + Level 3+ → PMF achieved, move to scale (outside this skill's scope)
• Sean Ellis 25-40% + retention partly flattens + Level 2 → iterate (return to Stage 4 for the risk-assumption or Stage 7 for a new interview cycle)
• Sean Ellis < 25% + retention falling + Level 1 → pivot (return to Stage 1, rethink the failed dimension)

Artifact: iteration-changelog.md — what changes, why, which stage we return to.

After the changelog is written, auto-start will determine the stage again from the new artifacts.

Cross-stage rules

Narrative versioning:

• V1 (after Stage 1) — initial hypothesis
• V2 (after Stage 3) — after market research, updated based on analogs/antilogs
• V3 (after Stage 7) — after interview synthesis, updated based on field data
• Each version is its own file, does not overwrite the previous one
• Each new version has a "Version History" section with a changelog (what changed vs the previous version)

Confidence can decrease. Do not inflate. If the data contradicts the hypothesis — note it honestly.

Loop detection: if the confidence on some dimension dropped in the new version → flag. Possible actions: more validation, return to research, or pivot.

Going back. The user can say "go back to stage X" at any time — the skill switches. Old artifacts are not deleted. Example: after Stage 4 you decide more research is needed → return to Stage 2 → new market-research-v2.md (not overwrite).

Between sessions: the PMF cycle is months. On every resume the skill re-reads auto-start and does not trust "memory" of the previous session.

Quality gates (general for the pipeline)

Before moving to the next stage, verify:

• [ ] All 7 dimensions covered (no "we forgot Timing")
• [ ] Confidence scores recorded for every dimension
• [ ] Riskiest dimension explicitly identified
• [ ] Cross-fit analysis (Channel-Model + Model-Market) done in Stage 3
• [ ] DVF assumptions = 9 (3 per category) in Stage 4
• [ ] Interview guide contains no leading questions in Stage 5
• [ ] Coverage matrix covers all 2-3 risk-dimensions in Stage 5
• [ ] Sean Ellis survey collected on at least 40 responses in Stage 9
• [ ] Retention analyzed by cohorts, not by overall average, in Stage 9
• [ ] Decisions in Stage 10 are based on all three instruments at once (not just Sean Ellis)

Common pitfalls

Reference files

All references live under references/<lang>/..., where <lang> is en or ru (chosen at first run, stored in config).

Pipeline map:

• pipeline-overview.md — state machine, transitions between stages

Stage logic (read on the corresponding stage):

• stage-0-setup.md
• stage-1-hypothesis.md
• stage-2-research.md
• stage-3-synthesis.md
• stage-4-validate.md
• stage-5-interview-prep.md
• stage-7-interview-synthesis.md
• stage-9-metrics.md

Methodology (read when needed — reference manuals):

• 7-dimensions.md — full description of the 7 PMF dimensions + what good looks like
• 7-powers.md — Hamilton Helmer competitive advantage
• dvf-framework.md — David Bland Desirability/Viability/Feasibility
• sean-ellis-survey.md — 40% threshold, distribution
• levels-of-pmf.md — First Round 4 levels
• narrative-writing-guide.md — how to make problem visceral

Artifact templates (load at the relevant stage):

• template-narrative.md — structured narrative with 7 dimensions + validation table
• template-narrative-prose.md — prose for stakeholders
• template-market-research.md — market research synthesis
• template-risk-prioritization.md — risk scoring + cross-fit
• template-interview-guide.md — interview guide
• template-interview-synthesis.md — interview synthesis
• template-metrics-dashboard.md — metrics dashboard
• template-interview-note.md — one interview note

Methodology sources

• gnurio/pmf-plugin — structural pipeline pattern (10 stages)
• Marty Cagan — Empowered, product discovery
• David Bland — Testing Business Ideas (DVF framework)
• Hamilton Helmer — 7 Powers (competitive advantage)
• Sean Ellis — PMF survey (40% threshold)
• First Round Capital — Levels of PMF
• Bill Gross — TED talk on timing as the key startup-success factor