startup-metrics-framework

Overview of startup-metrics-framework skill

What startup-metrics-framework does

startup-metrics-framework is a metrics-planning and calculation skill for early-stage companies that need a usable KPI framework, not just a loose list of startup numbers. It is designed for SaaS, marketplace, consumer, and B2B startups from seed through Series A, with emphasis on revenue, unit economics, growth efficiency, and cash management.

Who should use this skill

Best-fit users are founders, operators, analysts, finance leads, and investor-prep teams who need to:

• choose the right startup metrics by business model and stage
• calculate core KPIs consistently
• turn raw business data into a board, fundraising, or operating view
• spot whether growth is healthy or just expensive

The real job-to-be-done

Most users are not looking for formulas alone. They need a repeatable way to answer practical questions like:

• Which metrics matter for my startup model right now?
• How should I calculate CAC, LTV, burn multiple, or payback?
• What benchmark should I compare against?
• What should go on an investor or leadership dashboard?

startup-metrics-framework is most useful when you want the agent to structure this thinking quickly and keep the output grounded in standard startup finance language.

What makes startup-metrics-framework different

The main differentiator is scope discipline. Instead of giving generic data-analysis advice, the skill organizes startup metrics around business health and fundraising relevance. It covers:

• universal startup metrics
• revenue and growth metrics
• unit economics
• efficiency and cash metrics
• stage-aware expectations and benchmark framing

That makes it more decision-useful than a normal “analyze my business” prompt.

When this skill is a good fit

Use the startup-metrics-framework skill when you already have at least rough business inputs and need a framework for interpretation. It is especially helpful for:

• metric definitions before building a dashboard
• investor update preparation
• board metric reviews
• startup KPI audits
• identifying missing inputs for unit-economics analysis

When it is not a good fit

This skill is not a substitute for:

• audited financial modeling
• custom BI implementation
• SQL pipeline design
• advanced cohort modeling from raw event logs
• industry-specific metrics outside typical startup operating models

If your main need is data engineering, accounting compliance, or forecasting with detailed assumptions, you will need more than this skill alone.

How to Use startup-metrics-framework skill

Install context for startup-metrics-framework

The repository evidence shows this skill lives at:

plugins/startup-business-analyst/skills/startup-metrics-framework

A common install pattern for this repo is:

npx skills add https://github.com/wshobson/agents --skill startup-metrics-framework

If your setup uses a different skill loader, use the GitHub path above to locate the source and register it in your agent environment.

Read this file first

Start with:

• SKILL.md

This repo slice does not expose extra helper files, scripts, or reference folders for this skill, so most of the value is in understanding the metric definitions, formulas, and benchmark framing inside that single file.

What input startup-metrics-framework needs

The startup-metrics-framework usage quality depends heavily on the numbers you provide. Strong inputs usually include:

• business model: SaaS, marketplace, consumer subscription, B2B services, hybrid
• company stage: pre-seed, seed, Series A
• pricing model
• monthly revenue or bookings data
• customer counts
• churn or retention data
• sales and marketing spend
• gross margin
• cash balance, burn, runway
• acquisition channels if CAC is being analyzed

Without these, the agent can still provide a framework, but not a reliable metric assessment.

Turn a rough goal into a strong prompt

Weak prompt:

• “Analyze my startup metrics.”

Stronger prompt:

• “Use startup-metrics-framework for Data Analysis on a seed-stage B2B SaaS company. We have $120k MRR, 8% monthly logo churn, 78% gross margin, $45k monthly sales and marketing spend, 30 new customers last month, $1.2M cash, and $95k net burn. Calculate CAC, LTV, CAC payback, burn multiple, and identify the top 5 issues to fix before fundraising.”

The stronger version works better because it gives:

• business model context
• stage context
• enough data for calculation
• a clear output target

Best workflow for first use

A practical workflow for startup-metrics-framework install and usage is:

1. Install or register the skill in your agent setup.
2. Read SKILL.md once to understand its metric categories.
3. Gather your latest monthly operating numbers.
4. Ask the agent to calculate only the metrics supported by the data you actually have.
5. Then ask for interpretation, benchmark comparison, and next-step recommendations.

This reduces hallucinated assumptions and makes missing-data gaps visible early.

Recommended prompt structure

A reliable prompt template is:

• company type and stage
• timeframe
• source metrics you already trust
• formulas you want applied
• benchmark or decision context
• desired output format

Example:

• “Apply startup-metrics-framework to a Series A marketplace startup using the last 6 months of data. Compute revenue growth, CAC, LTV, take rate, burn multiple, and runway. Flag any metric that is directionally weak and separate calculation assumptions from conclusions.”

What the skill covers well

From the source, this skill is strongest at:

• MRR and ARR framing
• growth-rate interpretation
• CAC and LTV basics
• churn-linked unit economics
• burn and runway thinking
• benchmark-oriented analysis for early-stage companies

This is enough to support KPI reviews, investor materials, and operating dashboards at a planning level.

Where you still need your own judgment

The skill gives formulas and benchmark logic, but you still need to decide:

• whether to use logo churn or revenue churn
• whether CAC should include partial overhead
• whether ARPU should be monthly or annualized
• whether a blended metric hides major segment differences

These choices can materially change output. Ask the agent to state assumptions explicitly.

Repository reading path

Because the skill is concentrated in one file, a smart reading path is:

1. SKILL.md overview
2. universal metrics section
3. unit economics section
4. cash and efficiency sections
5. benchmark references tied to stage

Read it in that order if you want to understand both formulas and why those formulas matter operationally.

Practical usage tips that improve output quality

To get better startup-metrics-framework usage results:

• provide a single time basis, usually monthly
• label whether customer counts are logos, accounts, or active payers
• separate gross revenue from net revenue
• state if churn is monthly or annual
• give both burn and current cash if you want runway analysis
• ask the agent to show formulas before interpreting results

This prevents the most common metric-definition mixups.

startup-metrics-framework skill FAQ

Is startup-metrics-framework good for beginners?

Yes, if you already know the basic shape of your business data. The skill is accessible because it uses standard startup metrics, but beginners should still verify definitions like CAC, ARPU, churn, and gross margin before acting on results.

Is startup-metrics-framework only for SaaS?

No. The source explicitly targets SaaS, marketplace, consumer, and B2B startups. The fit is strongest where recurring revenue, acquisition cost, retention, and burn matter. It is less helpful for businesses with highly irregular project revenue or complex capital structures.

What is the main advantage over a normal prompt?

A normal prompt often produces a generic KPI list. startup-metrics-framework gives a more structured startup-finance lens: formulas, benchmark context, and a narrower set of metrics that matter for stage and business model. That usually means less prompt back-and-forth.

Can I use startup-metrics-framework for investor reporting?

Yes. This is one of the best use cases. The skill is well aligned with investor-update and board-report needs, especially for growth, unit economics, and cash efficiency. Just make sure the source numbers are already cleaned and internally consistent.

Does startup-metrics-framework do deep financial modeling?

No. It is a framework and analysis aid, not a full operating model builder. It helps define and calculate important startup metrics, but it does not replace spreadsheet-based planning, scenario modeling, or finance-team review.

When should I not install startup-metrics-framework?

Skip it if your main need is:

• SQL or dashboard implementation
• accounting-grade reporting
• advanced cohort analytics from event data
• industry-specific operational metrics outside early-stage startup finance

In those cases, a BI, analytics engineering, or FP&A-focused skill will be a better match.

How to Improve startup-metrics-framework skill

Give cleaner metric definitions up front

The fastest way to improve startup-metrics-framework outputs is to define each ambiguous number before asking for conclusions. For example:

• “CAC includes salaries, paid media, and software, but excludes founder time.”
• “Churn is monthly logo churn.”
• “ARPU is monthly subscription revenue per paying account.”

This avoids invalid comparisons and bad payback calculations.

Ask for assumptions separately from analysis

A strong prompt pattern is:

1. “List assumptions needed.”
2. “Show formulas.”
3. “Compute metrics.”
4. “Interpret results.”
5. “Recommend actions.”

That sequence makes the skill easier to audit and improves trust in the final analysis.

Provide segmented data when blended metrics hide the story

If you have multiple customer types, do not only provide blended averages. Better input:

• SMB vs enterprise
• paid vs organic acquisition
• self-serve vs sales-led
• geography or product line splits

This materially improves CAC, LTV, and growth-efficiency interpretation.

Watch for common failure modes

The most common issues with startup-metrics-framework guide style outputs are:

• mixing monthly and annual values
• using revenue churn and logo churn interchangeably
• calculating LTV from unstable early churn data
• ignoring gross margin in LTV
• treating all acquisition channels as equally efficient

If the first answer feels too neat, ask the agent to check these specific failure modes.

Improve startup-metrics-framework for Data Analysis prompts

For stronger startup-metrics-framework for Data Analysis results, ask for:

• a calculation table
• explicit formulas used
• missing-data flags
• benchmark comparison
• action ranking by likely impact

Example:

• “Use startup-metrics-framework to compute the metrics below in a table, note any assumptions, compare to seed-stage benchmarks, and rank the top 3 operational fixes by likely effect on burn multiple and CAC payback.”

Iterate after the first output

The best second-pass prompts are not “redo this.” They are targeted:

• “Recalculate CAC excluding brand spend.”
• “Show the impact of reducing churn from 8% to 5%.”
• “Separate logo churn from revenue churn.”
• “Reframe this for a board deck.”

This turns the skill from a formula explainer into a decision-support tool.

Improve output format for stakeholders

If your end use is a board update or fundraising memo, ask for output in sections:

• current metric snapshot
• benchmark comparison
• risks
• actions
• data gaps

That makes the startup-metrics-framework skill more useful in real workflows than a raw list of formulas.

Validate before operationalizing

Before you put the results into a dashboard or investor document, verify:

• source-of-truth system for each metric
• time windows
• inclusion and exclusion rules
• consistency between finance and growth teams

The skill is strongest when used to structure analysis, then validated against your internal metric definitions.