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startup-metrics-framework

Track, calculate, and optimize key performance metrics for SaaS, marketplace, consumer, and B2B startups from seed through Series A, including unit economics, growth efficiency, and cash management.

Install / Use

npx skills add wshobson/agents --skill startup-metrics-framework

Installs into whichever agent you are using.

About this skill
📄

SKILL.md

Installable skill definition

Quality Score

98/100

Supported Platforms

Universal

Tags

Our assessment of startup-metrics-framework

startup-metrics-framework scores 98/100 on our quality scale, 63rd of 1,753 Development & Engineering skills we index (top 4%).

Its SKILL.md is 9.8 KB long, well organised into 34 sections with 27 code examples: a thorough specification that gives an agent plenty to work with.

With 39,920 GitHub stars, it is one of the more widely adopted skills in the catalogue.

Substance
29/30
Structure
20/20
Description
15/15
Adoption
20/20
Freshness
15/15

Maintenance, license and trust

  • The repository was last updated 4 days ago, so startup-metrics-framework is actively maintained.
  • It is released under the MIT license, a permissive license that allows use, modification and commercial use with attribution.
  • Its trust signals score 100/100, with no cautions. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.

Safety scan

No issues found

Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands.

Automated pattern scan on 2026-09-25. It catches known dangerous patterns, not every risk — read a skill before letting an agent act on it.

startup-metrics-framework compared with similar skills

All 4 of these similar skills score higher than startup-metrics-framework; compare them before choosing.

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Frequently asked questions

How do I install startup-metrics-framework?
Run npx skills add wshobson/agents --skill startup-metrics-framework. The install tabs above show the steps for each supported agent.
Which AI agents does startup-metrics-framework work with?
It is written for Universal, as a SKILL.md file. Other agents that read the same format can often use it too.
Is startup-metrics-framework safe to use?
Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. It is MIT-licensed and scores 100/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
Is startup-metrics-framework still maintained?
The repository was last updated 4 days ago, so startup-metrics-framework is actively maintained.

name: startup-metrics-framework description: Track, calculate, and optimize key performance metrics for SaaS, marketplace, consumer, and B2B startups from seed through Series A, including unit economics, growth efficiency, and cash management. Use this skill when defining a metrics framework, calculating CAC/LTV/burn multiple, benchmarking business health, or preparing metrics dashboards for investors or board reporting. version: 1.0.0

Startup Metrics Framework

Comprehensive guide to tracking, calculating, and optimizing key performance metrics for different startup business models from seed through Series A.

Overview

Track the right metrics at the right stage. Focus on unit economics, growth efficiency, and cash management metrics that matter for fundraising and operational excellence.

Universal Startup Metrics

Revenue Metrics

MRR (Monthly Recurring Revenue)

MRR = Σ (Active Subscriptions × Monthly Price)

ARR (Annual Recurring Revenue)

ARR = MRR × 12

Growth Rate

MoM Growth = (This Month MRR - Last Month MRR) / Last Month MRR
YoY Growth = (This Year ARR - Last Year ARR) / Last Year ARR

Target Benchmarks:

  • Seed stage: 15-20% MoM growth
  • Series A: 10-15% MoM growth, 3-5x YoY
  • Series B+: 100%+ YoY (Rule of 40)

Unit Economics

CAC (Customer Acquisition Cost)

CAC = Total S&M Spend / New Customers Acquired

Include: Sales salaries, marketing spend, tools, overhead

LTV (Lifetime Value)

LTV = ARPU × Gross Margin% × (1 / Churn Rate)

Simplified:

LTV = ARPU × Average Customer Lifetime × Gross Margin%

LTV:CAC Ratio

LTV:CAC = LTV / CAC

Benchmarks:

  • LTV:CAC > 3.0 = Healthy
  • LTV:CAC 1.0-3.0 = Needs improvement
  • LTV:CAC < 1.0 = Unsustainable

CAC Payback Period

CAC Payback = CAC / (ARPU × Gross Margin%)

Benchmarks:

  • < 12 months = Excellent
  • 12-18 months = Good
  • 24 months = Concerning

Cash Efficiency Metrics

Burn Rate

Monthly Burn = Monthly Revenue - Monthly Expenses

Negative burn = losing money (typical early-stage)

Runway

Runway (months) = Cash Balance / Monthly Burn Rate

Target: Always maintain 12-18 months runway

Burn Multiple

Burn Multiple = Net Burn / Net New ARR

Benchmarks:

  • < 1.0 = Exceptional efficiency
  • 1.0-1.5 = Good
  • 1.5-2.0 = Acceptable
  • 2.0 = Inefficient

Lower is better (spending less to generate ARR)

SaaS Metrics

Revenue Composition

New MRR New customers × ARPU

Expansion MRR Upsells and cross-sells from existing customers

Contraction MRR Downgrades from existing customers

Churned MRR Lost customers

Net New MRR Formula:

Net New MRR = New MRR + Expansion MRR - Contraction MRR - Churned MRR

Retention Metrics

Logo Retention

Logo Retention = (Customers End - New Customers) / Customers Start

Dollar Retention (NDR - Net Dollar Retention)

NDR = (ARR Start + Expansion - Contraction - Churn) / ARR Start

Benchmarks:

  • NDR > 120% = Best-in-class
  • NDR 100-120% = Good
  • NDR < 100% = Needs work

Gross Retention

Gross Retention = (ARR Start - Churn - Contraction) / ARR Start

Benchmarks:

  • 90% = Excellent

  • 85-90% = Good
  • < 85% = Concerning

SaaS-Specific Metrics

Magic Number

Magic Number = Net New ARR (quarter) / S&M Spend (prior quarter)

Benchmarks:

  • 0.75 = Efficient, ready to scale

  • 0.5-0.75 = Moderate efficiency
  • < 0.5 = Inefficient, don't scale yet

Rule of 40

Rule of 40 = Revenue Growth Rate% + Profit Margin%

Benchmarks:

  • 40% = Excellent

  • 20-40% = Acceptable
  • < 20% = Needs improvement

Example: 50% growth + (10%) margin = 40% ✓

Quick Ratio

Quick Ratio = (New MRR + Expansion MRR) / (Churned MRR + Contraction MRR)

Benchmarks:

  • 4.0 = Healthy growth

  • 2.0-4.0 = Moderate
  • < 2.0 = Churn problem

Marketplace Metrics

GMV (Gross Merchandise Value)

Total Transaction Volume:

GMV = Σ (Transaction Value)

Growth Rate:

GMV Growth Rate = (Current Period GMV - Prior Period GMV) / Prior Period GMV

Target: 20%+ MoM early-stage

Take Rate

Take Rate = Net Revenue / GMV

Typical Ranges:

  • Payment processors: 2-3%
  • E-commerce marketplaces: 10-20%
  • Service marketplaces: 15-25%
  • High-value B2B: 5-15%

Marketplace Liquidity

Time to Transaction How long from listing to sale/match?

Fill Rate % of requests that result in transaction

Repeat Rate % of users who transact multiple times

Benchmarks:

  • Fill rate > 80% = Strong liquidity
  • Repeat rate > 60% = Strong retention

Marketplace Balance

Supply/Demand Ratio: Track relative growth of supply and demand sides.

Warning Signs:

  • Too much supply: Low fill rates, frustrated suppliers
  • Too much demand: Long wait times, frustrated customers

Goal: Balanced growth (1:1 ratio ideal, but varies by model)

Consumer/Mobile Metrics

Engagement Metrics

DAU (Daily Active Users) Unique users active each day

MAU (Monthly Active Users) Unique users active each month

DAU/MAU Ratio

DAU/MAU = DAU / MAU

Benchmarks:

  • 50% = Exceptional (daily habit)

  • 20-50% = Good
  • < 20% = Weak engagement

Session Frequency Average sessions per user per day/week

Session Duration Average time spent per session

Retention Curves

Day 1 Retention: % users who return next day Day 7 Retention: % users active 7 days after signup Day 30 Retention: % users active 30 days after signup

Benchmarks (Day 30):

  • 40% = Excellent

  • 25-40% = Good
  • < 25% = Weak

Retention Curve Shape:

  • Flattening curve = good (users becoming habitual)
  • Steep decline = poor product-market fit

Viral Coefficient (K-Factor)

K-Factor = Invites per User × Invite Conversion Rate

Example: 10 invites/user × 20% conversion = 2.0 K-factor

Benchmarks:

  • K > 1.0 = Viral growth
  • K = 0.5-1.0 = Strong referrals
  • K < 0.5 = Weak virality

B2B Metrics

Sales Efficiency

Win Rate

Win Rate = Deals Won / Total Opportunities

Target: 20-30% for new sales team, 30-40% mature

Sales Cycle Length Average days from opportunity to close

Shorter is better:

  • SMB: 30-60 days
  • Mid-market: 60-120 days
  • Enterprise: 120-270 days

Average Contract Value (ACV)

ACV = Total Contract Value / Contract Length (years)

Pipeline Metrics

Pipeline Coverage

Pipeline Coverage = Total Pipeline Value / Quota

Target: 3-5x coverage (3-5x pipeline needed to hit quota)

Conversion Rates by Stage:

  • Lead → Opportunity: 10-20%
  • Opportunity → Demo: 50-70%
  • Demo → Proposal: 30-50%
  • Proposal → Close: 20-40%

Metrics by Stage

Pre-Seed (Product-Market Fit)

Focus Metrics:

  1. Active users growth
  2. User retention (Day 7, Day 30)
  3. Core engagement (sessions, features used)
  4. Qualitative feedback (NPS, interviews)

Don't worry about:

  • Revenue (may be zero)
  • CAC (not optimizing yet)
  • Unit economics

Seed ($500K-$2M ARR)

Focus Metrics:

  1. MRR growth rate (15-20% MoM)
  2. CAC and LTV (establish baseline)
  3. Gross retention (> 85%)
  4. Core product engagement

Start tracking:

  • Sales efficiency
  • Burn rate and runway

Series A ($2M-$10M ARR)

Focus Metrics:

  1. ARR growth (3-5x YoY)
  2. Unit economics (LTV:CAC > 3, payback < 18 months)
  3. Net dollar retention (> 100%)
  4. Burn multiple (< 2.0)
  5. Magic number (> 0.5)

Mature tracking:

  • Rule of 40
  • Sales efficiency
  • Pipeline coverage

Metric Tracking Best Practices

Data Infrastructure

Requirements:

  • Single source of truth (analytics platform)
  • Real-time or daily updates
  • Automated calculations
  • Historical tracking

Tools:

  • Mixpanel, Amplitude (product analytics)
  • ChartMogul, Baremetrics (SaaS metrics)
  • Looker, Tableau (BI dashboards)

Reporting Cadence

Daily:

  • MRR, active users
  • Sign-ups, conversions

Weekly:

  • Growth rates
  • Retention cohorts
  • Sales pipeline

Monthly:

  • Full metric suite
  • Board reporting
  • Investor updates

Quarterly:

  • Trend analysis
  • Benchmarking
  • Strategy review

Common Mistakes

Mistake 1: Vanity Metrics Don't focus on:

  • Total users (without retention)
  • Page views (without engagement)
  • Downloads (without activation)

Focus on actionable metrics tied to value.

Mistake 2: Too Many Metrics Track 5-7 core metrics intensely, not 50 loosely.

Mistake 3: Ignoring Unit Economics CAC and LTV are critical even at seed stage.

Mistake 4: Not Segmenting Break down metrics by customer segment, channel, cohort.

Mistake 5: Gaming Metrics Optimize for real business outcomes, not dashboard numbers.

Investor Metrics

What VCs Want to See

Seed Round:

  • MRR growth rate
  • User retention
  • Early unit economics
  • Product engagement

Series A:

  • ARR and growth rate
  • CAC payback < 18 months
  • LTV:CAC > 3.0
  • Net dollar retention > 100%
  • Burn multiple < 2.0

Series B+:

  • Rule of 40 > 40%
  • Efficient growth (magic number)
  • Path to profitability
  • Market leadership metrics

Metric Presentation

Dashboard Format:

Current MRR: $250K (↑ 18% MoM)
ARR: $3.0M (↑ 280% YoY)
CAC: $1,200 | LTV: $4,800 | LTV:CAC = 4.0x
NDR: 112% | Logo Retention: 92%
Burn: $180K/mo | Runway: 18 months

Include:

  • Current value
  • Growth rate or trend
  • Context (target, benchmark)

Quick Start

To implement startup metrics framework:

  1. Identify business model - SaaS, marketplace, consumer, B2B
  2. Choose 5-7 core metrics - Based on stage and model
  3. Establish tracking - Set up analytics and dashboards
  4. Calculate unit economics - CAC, LTV, payback
  5. Set targets - Use benchmarks for goals
  6. Review regularly - Weekly for core metrics
  7. Share with team - Align on goals and progress
  8. Update investors - Monthly/quarterly reporting

Related Skills

View on GitHub
GitHub Stars39.9k
CategoryDevelopment
Updated4d ago
Forks4.3k

Languages

Python

Trust signals

100/100

From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.

No cautions