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Multi-agentProduct Development & LaunchAdvancedFree

Product-Market Fit Assessment System.

4 agents, 4 deliverables.

Take a product idea from strategy to user experience, technical planning, build phases, and launch readiness.

WORKFLOW META
Agents4
Total tokens (avg)~500
Run time12 min
AI toolChatGPT · Claude · Gemini
Variables4
DifficultyAdvanced
CategoryProduct Development & Launch
SEQUENCE MAP · CLICK TO JUMP
· 01 ·
PMF Signal Analyzer
· 02 ·
PMF Gap Diagnostician
· 03 ·
PMF Improvement Strategist
· 04 ·
PMF Tracking System Builder
USE CASE INPUTS

Set the workflow's inputs once.

These variables feed into every agent prompt below. Fill them once, then copy each agent in order.

{{product_name_type}}
Product name type
Product name type
Mega Prompt Library with 3000+ prompts
{{months_since_launch}}
Months since launch
Months since launch
Product-Market Fit Assessment System example context
{{current_active_user_count}}
Current active user count
Current active user count
beginner AI users
{{current_retention_rate}}
Current retention rate
Current retention rate
Product-Market Fit Assessment System example context
THE AGENTS

The 4-step sequence.

01
AGENT · PMF SIGNAL

PMF Signal Analyzer

GOAL OF THIS STEP

Assess product-market fit signals across 5 dimensions: (1) Retention (Day 7, Day 30, Day 90 benchmarks by category), (2) NPS score and 'very disappointed' metric (Sean Ellis test — target 40%+), (3) Organic growth (what % of new users come from word-of-mouth?), (4) Engagement depth (are users discovering core features?), (5) Revenue signals (expansion revenue, low churn). Score each dimension 1–5 and give an overall PMF verdict: No PMF / Weak PMF / Strong PMF.

EXPECTED OUTPUT

PMF assessment: 5 dimensions scored + evidence for each + overall PMF verdict

agent-01-pmf-signal-analyzer.md
### Input
Product, months since launch, users, retention

### Task
Assess product-market fit signals across 5 dimensions: (1) Retention (Day 7, Day 30, Day 90 benchmarks by category), (2) NPS score and 'very disappointed' metric (Sean Ellis test — target 40%+), (3) Organic growth (what % of new users come from word-of-mouth?), (4) Engagement depth (are users discovering core features?), (5) Revenue signals (expansion revenue, low churn). Score each dimension 1–5 and give an overall PMF verdict: No PMF / Weak PMF / Strong PMF.

### Output
PMF assessment: 5 dimensions scored + evidence for each + overall PMF verdict
02
AGENT · PMF GAP

PMF Gap Diagnostician

03
AGENT · PMF IMPROVEMENT

PMF Improvement Strategist

04
AGENT · PMF TRACKING

PMF Tracking System Builder

HOW TO RUN

Three steps. 12 min.

STEP 01

Fill in the variables at the top. Copy them into a note or your tool's context window — every agent below uses them.

STEP 02

In your AI tool, paste Agent 1 and run it. Copy the output. Paste Agent 2 with the output appended. Repeat in order for all 4 agents.

STEP 03

At the final agent, review and refine. It outputs your finished deliverable, ready to publish or hand off.

WHAT YOU GET

The final output, end-to-end.

PMF Signal Analyzer

PMF assessment: 5 dimensions scored + evidence for each + overall PMF verdict

PMF Gap Diagnostician

Root cause diagnosis per low-scoring dimension + priority ranking of gaps by PMF impact

PMF Improvement Strategist

2 PMF improvement experiments: hypothesis + change + segment + timeline + metric + kill switch + 90-day projection

PMF Tracking System Builder

7-metric PMF dashboard + data sources + monthly review process + PMF milestone definitions + M3/M6 targets

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