SKIN OS — Knowledge Base

SKIN OS · Knowledge Base

Six
Dimensions

A proprietary diagnostic framework that evaluates early-stage startups against the fundamentals that determine execution, decision-making, and long-term survival. Each dimension captures a distinct vector of company health — together they produce the SKIN Score.

Framework v1.1

6 dimensions · 43 signals

6 external sources

Dimension 01

Team

Founder–market fit, execution history, domain depth, and cohesion under pressure.

6 signals · 3 failure modes HIGH

Dimension 02

Product

Problem–solution clarity, evidence of value delivery, and technical defensibility.

7 signals · 4 failure modes MID

Dimension 03

Brand

Narrative clarity, positioning differentiation, and founder-to-market credibility.

5 signals · 3 failure modes MID

Dimension 04

Market

Structural tailwinds, timing signals, competitive density, and true market size.

6 signals · 3 failure modes HIGH

Dimension 05

Go-To-Market

Channel clarity, ICP definition, sales motion repeatability, and CAC awareness.

7 signals · 5 failure modes LOW

Dimension 06

Financials

Burn awareness, unit economics, runway clarity, capital efficiency, milestone definition, and cap table discipline.

6 signals · 5 failure modes LOW

Team

The most predictive single variable. Not résumés — execution history, founder–market alignment, and how the team makes decisions under ambiguity.

High-weight dimension

6 signals

Weight: 20% of SKIN Score

Green signals — strength indicators POSITIVE

+Founder has lived the problem they're solving — personal or professional domain immersionFMF
+At least one co-founder has shipped something at scale (product, team, revenue) beforeTRACK
+Clear role separation — no overlap in ownership, minimal ego friction in decision zonesSTRUCT
+Team can articulate what they disagree on — healthy conflict is surfaced, not suppressedCOHESION
+Demonstrated ability to recruit — early hires are noticeably stronger than expected at stageMAGNET
+Founders have unfair access — networks, data, channels competitors can't easily replicateACCESS

Red signals — risk indicators CAUTION

–Founder cannot clearly explain why they specifically are the right person to build this nowWHY YOU
–All founders come from the same background — no complementary tension in the founding setHOMOG.
–Vesting not set up or misaligned — signals lack of real commitment or early conflictVEST
–Team pivots narrative under light pressure — conviction untested, identity not anchoredCONVICTION

Diagnostic questions AI PROMPT INPUTS

Q1Why are you uniquely positioned to solve this problem — not in general terms, but specifically?
Q2Describe a decision you made in the last 60 days that was wrong. How did you find out, and what changed?
Q3What is your co-founder's biggest blind spot? What is yours?
Q4What's the hardest thing you've shipped — and what did it take to get it done?
Q5Who is the best person you've recruited so far? What made them say yes?

Common failure modes

Opportunity founder syndrome

Founder identified the market opportunity from outside, not the problem from inside. Lacks the conviction to survive when the opportunity contracts.

Undeclared CEO problem

Two co-founders with overlapping decision rights and no agreed tiebreaker. Creates paralysis at exactly the moment speed matters most.

Credential substitution

Impressive backgrounds used as a substitute for demonstrated execution. Pedigree is a weak signal; what matters is what they've actually built.

↳ SOURCE: YC Startup School · Startups.RIP analysis

Resilience deficit

YC data shows resilience — not intelligence or pedigree — is the #1 predictor of founder success. Founders who pivot narrative under light pressure almost universally fail to survive the first serious setback. Pattern visible across 1,700+ YC postmortems: team cohesion under stress is a leading indicator of survival.

Score sub-components

Founder–market fit

Execution track record

Team structure & roles

Talent magnet signal

Unfair access / network

Product

Not the features — the evidence of value. Whether the thing actually works, who it works for, and whether they'd be devastated if it disappeared.

Mid-weight dimension

7 signals

Weight: 18% of SKIN Score

Green signalsPOSITIVE

+Users self-describe the value in emotional terms — saves, protects, enables, transformsVALUE
+40%+ of surveyed users would be "very disappointed" if the product disappeared (Sean Ellis benchmark)PMF SIG.
+Core use case is narrow, clear, and non-negotiable — product does one thing extremely wellFOCUS
+Technical architecture is defensible — not just a wrapper, has real IP or data moat formingMOAT
+Users are organically expanding use — not from prompts or sales calls, from own discoveryEXPAN.

Red signalsCAUTION

–Product description changes significantly depending on who's asking — no stable core narrativeDRIFT
–Users engage but don't pay — value perceived but not quantified or mission-criticalWTP GAP
–Roadmap driven by customer requests rather than founder conviction on what users actually needREQ-DRIVEN
–Churn explained as "education problem" — almost always a value delivery problemCHURN

Diagnostic questionsAI PROMPT INPUTS

Q1What is the single most important thing your product does, in one sentence, for one specific person?
Q2Which of your current users would miss this most if it disappeared tomorrow? What would they do instead?
Q3What's the part of your product your best customers use that surprises you?
Q4Walk me through the last feature you killed. Why?
Q5What makes your product structurally hard to replicate in 12 months?

Common failure modes

Feature accumulation without core

Product grows in breadth before depth is achieved. No single use case is excellent — many are mediocre.

Demo-reality gap

Product sells well in demo mode but delivery falls short. Inflates early signals until churn data arrives.

Solution in search of a problem

Technical excellence applied to a pain that doesn't justify switching costs. Elegant product, weak demand.

↳ SOURCE: Failory Cemetery · 400+ startup failures analyzed

No market need — the #1 killer

Failory's analysis of 400+ failed startups identifies "no market need" as the leading failure cause — present in 64+ documented cases. The product existed; the pain didn't. Distinct from poor product: the product worked, but no one needed it badly enough to pay, switch, or return. SKIN signal: users engage but don't pay, or pay once and don't return.

Score sub-components

Problem–solution fit clarity

PMF signal strength

Technical defensibility

User retention quality

Roadmap conviction

Go-To-Market

The most commonly underdeveloped dimension at early stage. Channel clarity, ICP precision, and whether the sales motion can be repeated without the founder in the room.

Low score — high priority

7 signals

Weight: 16% of SKIN Score

Green signalsPOSITIVE

+ICP defined by behavior and buying context, not just firmographics — includes a "not for" definitionICP
+Sales cycle length is known and predictable — founder can state average days from first contact to closeCYCLE
+A non-founder has closed at least one deal — removes founder-dependency as sole sales vectorREPEAT
+Channel is validated — not hypothetical. Real conversion data exists from at least one channelCHANNEL
+CAC is known or estimated with real data — not a spreadsheet assumptionUNIT ECON

Red signalsCAUTION

–"We'll use LinkedIn, events, and content" — no channel prioritized, no conversion data, no sequencingSPRAY
–Every customer came from a personal relationship — warm network is not a repeatable GTM motionNETWORK
–Sales process lives in the founder's head — no playbook, no scripts, no handoff materialsFOUNDER DEP.
–ICP described as an industry vertical — "mid-market SaaS companies" is an audience, not a buyerICP WEAK

Diagnostic questionsAI PROMPT INPUTS

Q1Describe your ideal customer in terms of the specific situation they're in, not just their job title or company size.
Q2Walk me through your last three deals — who initiated, how long did they take, who signed?
Q3Which single channel would you double down on if you could only choose one for the next 90 days?
Q4Has anyone other than a founder closed a deal? If not, what's the plan to change that?
Q5What's your current CAC? What data is that based on?

Common failure modes

Spray-and-pray channel strategy

No channel prioritized, no conversion data, no sequencing. Energy spread across LinkedIn, events, content, and outbound simultaneously — mastering none.

Warm network mistaken for GTM

All customers came through the founder's personal relationships. Not a sales motion — social capital spending. Depletes faster than it compounds and cannot be handed to a hire.

Founder-dependency trap

Sales process lives in the founder's head. No playbook, no handoff, no repeatability. Company cannot scale past the founder's calendar.

↳ SOURCE: Wefunder · Failory "bad marketing" database (38+ cases)

Narrative-market disconnect

Wefunder data: campaigns that connect personally — relatable story, transparent risk, clear use of funds — consistently outperform technically superior competitors. Failory's 38+ "bad marketing" cases share one pattern: product story is founder-language, not customer-language. Buyers don't recognize themselves in it. SKIN signal: conversion below 20% on qualified leads despite a strong product.

Score sub-components

ICP definition quality

Channel validation

Sales motion repeatability

Unit economics awareness

Founder-independance signal

Brand

Not logo or colors — the clarity of the company's narrative, the sharpness of its positioning, and whether the founder's presence in the market builds or dilutes credibility.

Mid-weight dimension

5 signals

Weight: 10% of SKIN Score

Green signals — strength indicators POSITIVE

+Company can be described in one sentence that a customer would actually use — not founder languageCLARITY
+Positioning has a clear "not for" — the brand knows who it's not trying to serve and says soEDGE
+Founder is a credible voice in the space — content, network, or track record gives them standing to claim the narrativeAUTHORITY
+Brand language is consistent across all touchpoints — deck, website, sales calls, and social tell the same storyCOHERENCE
+Customers use the same words to describe the company that the company uses to describe itself — proof the narrative is landingRESONANCE

Red signals — risk indicators CAUTION

–Pitch deck and website tell slightly different stories — signals narrative is still being worked out, not decidedDRIFT
–Category language used without definition — "AI-powered," "next-gen," "end-to-end" with no specificity beneathJARGON
–Founder adjusts the company description to match what they think the audience wants to hearSHAPESHIFTING
–Brand tries to appeal to everyone — no point of view, no enemy, no clear stance on what's broken in the status quoGENERIC

Diagnostic questions AI PROMPT INPUTS

Q1Describe your company in one sentence — the way a happy customer would describe it to a colleague.
Q2What does your brand stand against? What's the status quo you're explicitly rejecting?
Q3Who is your brand not for? Name the buyer you actively don't want.
Q4What gives you the right to own this narrative — why should the market listen to you specifically?
Q5Read me the first line of your website. Is that the same thing you'd say if someone asked what you do at a dinner party?

Common failure modes

Positioning by committee

Brand language softened until it offends no one — and resonates with no one. Clarity sacrificed for inclusivity. The result is a company that's hard to remember.

Category confusion

Company creates a new category name without the brand authority to make it stick. Buyers can't place you — and can't remember you. Creating a category requires already owning a conversation.

Founder-brand dependency

Brand equity lives entirely in the founder's personal profile. The company has no independent gravity. Dangerous when the founder leaves the room — the brand dissolves with them.

Score sub-components

Narrative clarity

Positioning sharpness

Founder authority signal

Cross-channel coherence

Customer language alignment

Market

Structural tailwinds, timing precision, and whether the competitive landscape is genuinely navigable — or a story the founder tells themselves to justify the bet.

High-weight dimension

6 signals

Weight: 18% of SKIN Score

Green signals — strength indicators POSITIVE

+Structural forces — regulatory shift, technology unlock, cost collapse — are pulling the market toward this solution now, not eventuallyTAILWIND
+Founder can name 3+ recent events that made this problem more urgent this year than it was two years agoTIMING
+Competitive map has a clear white space — incumbents are serving a different customer, moving slowly, or structurally unable to respondGAP
+Market size is built bottom-up from real buyer data — not a top-down TAM slide from an analyst reportSIZING
+Beachhead market is small enough to dominate, adjacent to a much larger opportunity the startup can grow intoBEACH-HEAD
+Buyers are already spending money on this problem — budget exists, just allocated to inferior solutionsBUDGET

Red signals — risk indicators CAUTION

–Market size cited as a top-down TAM number — "the market is $50B" with no path to what % is actually addressableTAM FICTION
–Competitive slide lists only startups — ignores incumbents, internal tools, or "do nothing" as the dominant competitorBLIND SPOT
–Timing claim is "the world is going digital / AI / remote" — macro trend doesn't explain why this specific problem is solvable nowVAGUE TIMING
–No buyer currently has budget for this — founder will have to create the category and the budget simultaneouslyNO BUDGET

Diagnostic questions AI PROMPT INPUTS

Q1Why is now the right moment for this? Name something that changed in the last 18 months that makes this timing different.
Q2Who is your most dangerous competitor — and why haven't they already built what you're building?
Q3How did you arrive at your market size number? Walk me through the math from a real buyer up, not from a research report down.
Q4What is the one market where you intend to be undeniably dominant in 18 months? What does winning there look like?
Q5What are buyers currently doing to solve this problem — and what budget line does that come from?

Common failure modes

Too-early market entry

Problem is real but buyers aren't in enough pain yet. Company spends its runway educating a market that isn't ready to buy. Being right too early is economically equivalent to being wrong.

Beachhead too broad

First target market is defined at the level of an industry, not a specific buyer in a specific situation. Company tries to be everything to everyone and dominates nothing.

Incumbent awakening

Market validation attracts a well-resourced incumbent who can replicate the product in 12 months. Competitive moat was distribution and brand, not technology — and the startup has neither yet.

↳ SOURCE: Dealroom Taxonomy · 2.6M+ companies tracked globally

Category misclassification

Founder describes their market in a category that investors and buyers don't use. Dealroom's taxonomy of 50+ primary sectors with hundreds of sub-categories shows that how a startup is categorized directly affects which investors find it, which competitors it's benchmarked against, and which pricing comps apply. Founders who create their own category names without the authority to make them stick — "AI-native CRM for the future of work" — are invisible to the market infrastructure that would otherwise route capital and customers to them.

Score sub-components

Structural tailwind clarity

Timing precision

Competitive white space

Market size validity

Beachhead definition

Budget existence signal

Financials

Not about accounting — about whether the founding team has a disciplined, honest relationship with capital. Burn awareness, unit economics, revenue clarity, and disciplined deployment of every dollar.

Low score — high priority

6 signals

Weight: 16% of SKIN Score

Green signals — strength indicators POSITIVE

+Founder knows burn rate to the dollar, calculates runway from realistic assumptions with a buffer, and matches spend to actual evidence — not aspirational stageBURN & RUNWAY
+Unit economics are understood at the cohort level — knows which customer segments are profitable and which are not, with the data to prove itUNIT ECONOMICS
+Revenue model is simple, tested with paying customers, and the operational path to sustainability is clear — not "when we raise"REVENUE & PATH
+Every dollar is tied to a specific proof point, outcome, or milestone — not "team and growth." Capital allocation has visible logicCAPITAL DISCIPLINE
+Equity ownership is clean and documented — founder splits resolved, employee grants formalized, no undocumented promises or verbal advisor dealsEQUITY DISCIPLINE
+Founder knows their numbers cold, surfaces problems early, and has no hidden surprises — full transparency on financial healthTRANSPARENCY

Red signals — risk indicators CAUTION

–Founder cannot state monthly burn without checking a spreadsheet — financial illiteracy at the leadership levelAWARENESS
–Unit economics unknown or estimated, not measured — growth without knowing if customers are profitableUNIT BLINDNESS
–Revenue model has never been tested with a paying customer — pricing is hypothetical until someone pays full price unpromptedUNTESTED MODEL
–Capital deployed without milestone framework — "we needed to grow" instead of "we needed to prove X by Y date"NO MILESTONE
–Equity is messy — undocumented founder splits, verbal advisor grants, contractor work paid in promisesEQUITY MESS

Diagnostic questions AI PROMPT INPUTS

Q1What is your current monthly burn? What is the single largest line item driving it?
Q2How many months of runway do you have at current burn — and what revenue assumption is that based on?
Q3Walk me through the unit economics of your best customer. What did it cost to acquire them, and what do they generate annually?
Q4What's the path to sustainability that doesn't require another raise — and at what revenue level does that happen?
Q5If you had $500K to deploy in the next 6 months, what specific milestone would you spend it to hit?
Q6Walk me through your cap table — including any unissued equity, vesting status, and outstanding promises.

Common failure modes

Optimism accounting

Financial model built on best-case assumptions with no downside scenario. First time the founder runs a realistic model is when an investor forces them to — by then it's too late to adapt.

CAC blindness

Company is growing but doesn't know if customers are profitable. LTV/CAC ratio is either unknown or calculated on assumptions rather than cohort data. Growth without unit economics is just burning capital faster.

Revenue recognition confusion

Founder conflates bookings, revenue, and cash. Reports a "record month" in bookings while cash is declining. Creates a false sense of momentum that delays necessary corrective action.

Milestone-free spending

Capital deployed without proof-point structure. Cash goes to "team and growth" rather than to a specific outcome. Even profitable companies suffer from this — money spent is money that didn't build optionality.

Equity time-bombs

Undocumented founder splits, verbal advisor grants, or contractor work paid in promised equity. Surfaces at the worst possible moment — fundraise, hire, or acquisition — and destroys deal value.

Score sub-components

Burn & runway discipline

Unit economics clarity

Revenue model & path

Capital allocation discipline

Equity & ownership discipline

Financial transparency

Prompt Logic

The system prompts and diagnostic templates that power the AI reasoning layer. These translate raw founder inputs into scored dimension assessments.

AI layer

System prompts

System prompt — dimension scoringTEMPLATE

You are a SKIN Pulse analyst — an experienced startup operator evaluating early-stage companies. CONTEXT: You are reviewing a startup against the SKIN framework. Dimension being evaluated: {{dimension_name}} Weight in SKIN Score: {{dimension_weight}}% INPUTS: Founder answers: {{founder_responses}} Supporting documents: {{deck_summary}}, {{team_bios}} TASK: 1. Score this dimension from 0–100 based on the signal rubric 2. Identify the top 2 strengths (cite specific evidence) 3. Identify the top 2 risks (cite specific evidence) 4. Output 3 prioritized actions for this dimension FORMAT: Return structured JSON matching the SKIN report schema. Do not infer beyond what the evidence supports. Flag low-confidence scores explicitly.

Intake prompt — founder intake formTEMPLATE

Based on the uploaded pitch deck and founder profiles, extract and structure the following for each of the 7 SKIN dimensions:

FOR EACH DIMENSION:
— Primary evidence present in submitted materials
— Gaps (what's missing that we need to ask)
— Preliminary signal classification: STRONG / MIXED / WEAK / ABSENT

OUTPUT:
A structured intake brief per dimension.
Flag any red signals that appear without context.
Generate 3–5 follow-up questions per dimension where evidence is missing.

TONE:
Operator-grade. No hedging. No praise inflation.
This brief will be reviewed by human analysts before delivery.

Sources

The external databases, research platforms, and real-world datasets that validate and calibrate SKIN's signal library. Each source is rated for signal quality and mapped to the dimensions it informs.

6 sources integrated

v1.1 — March 2026

Source registryEXTERNAL VALIDATION

Startups.RIP

1,737+ YC startup postmortems from 2005 to present. Covers all batches. Curated failure and acquisition patterns across every industry. Highest signal quality for Team and Product dimensions — shows which founder profiles survive vs. collapse, and which product categories have structural death rates.

HIGH signal Team · Product · Market
Failory Cemetery

120+ structured startup postmortems categorized by root cause: no market need (64+ cases), bad marketing (38+), competition (39+), lack of focus, legal challenges, poor product, no pivoting, mismanagement of funds. Each case includes founder interview. Best source for failure mode classification and red signal calibration.

HIGH signal Product · GTM · Market
YC Startup School

15 years of YC playbook distilled from 5,000+ companies. Key calibrations: 70% of top 100 YC companies found ideas organically (not forced); resilience is the #1 founder trait (above intelligence, pedigree, or confidence); execution speed is the top predictor of success in the AI era. Canonical benchmark for Team and Product signals.

HIGH signal — canonical Team · Product · Financials
Dealroom Taxonomy

2.6M+ companies tracked globally. 50+ primary sectors with hundreds of sub-categories. Defines a startup as "a company designed to grow fast." Used by SKIN for: standardizing sector classification at intake, mapping a company's beachhead to an investable category, and benchmarking against stage/sector peers. Key insight: how you're classified determines who finds you — investors, acquirers, and buyers all navigate by category.

HIGH signal — classification Market · Financials
Wefunder

$617M raised across 2,833 founders since 2012. 1.5M+ retail investor base. Key calibrations: campaigns that connect personally (narrative, transparent risk, clear use of funds) outperform technical pitches; hockey-stick projections without drivers are the most common valuation red flag; revenue multiples of 165x+ at early stage signal disconnect from reality. Strongest source for Financials discipline and GTM narrative signals — feeds the Fundable Score for raise-specific assessment.

MEDIUM signal GTM · Brand · Financials
Cerdeira Notion DB

Curated Notion database of startup resources, frameworks, and tools. Broad but less opinionated than primary failure databases. Useful for discovering emerging frameworks and tools across the startup ecosystem. Currently inaccessible (link may have moved — check cerdeira.notion.site for updated path).

PENDING — link broken General reference

Failure
Benchmarks

Aggregate failure pattern data from Failory and Startups.RIP. These are the base rates — what kills startups, how often, and at what stage. Every SKIN red signal maps to at least one of these root causes.

400+ failures analyzed

11 root cause categories

Root cause taxonomy — Failory (400+ cases)FREQUENCY RANKED

No market need

Bad marketing / GTM

Competition

Lack of focus (ICP drift)

Lack of funds / runway

Poor product

No pivoting (failed to adapt)

Mismanagement of funds

Legal challenges

Depending on others

#10

Lack of experience

#11

YC postmortem patterns — Startups.RIP1,737+ COMPANIES

→Highest failure sectors: Community, Social Media, Marketplace, Education — all high-competition, network-effect-dependent categories where timing and distribution matter more than product qualityHIGH RISK
→Most acquired (survived) sectors: Developer Tools, Fintech, B2B SaaS, HealthTech — defensible IP or sticky enterprise contracts make these acquirable before they failACQUIRABLE
→The W12/S12 cohort shows disproportionate failure — post-2008 funding surge attracted opportunity founders who entered markets without lived experience, and hit reality hard when growth slowedPATTERN
→Hardware, Consumer, and Deep Tech show the lowest acquisition rates and highest "inactive" classification — cash intensity and long cycles outrun most founding teamsCAPITAL TRAP

Wefunder red flags — valuation & narrative$617M DATA

!Valuations above 30x revenue at early stage without a clear growth driver are routinely rejected by sophisticated investors — 165x multiple cases (e.g. GigaStar) illustrate the ceiling of what retail markets will absorbVALUATION
!Hockey-stick projections without an identified inflection driver are the single most common credibility destroyer in a pitch — across both institutional and retail investor contextsPROJECTIONS
+Campaigns/pitches that nail personal narrative + transparent risk + specific use of funds consistently outperform technically stronger competitors — across Wefunder, YC, and institutional contexts alikeNARRATIVE
+Community round momentum (early investor clustering) is a leading indicator of eventual institutional follow-on — 2,833 Wefunder companies have gone on to raise $5B in follow-on financingMOMENTUM

Dimension → failure root cause mapping

Team

Lack of experience · Depending on others · Founder conflict

Product

No market need · Poor product · No pivoting

Brand

Bad marketing · Lack of focus · No market need (positioning)

Market

Competition · No market need (timing) · Legal challenges

Go-To-Market

Bad marketing · Lack of focus · No pivoting

Financials

Lack of funds · Burn discipline failure · Mismanagement

Financials

Mismanagement of funds · Lack of funds · No pivoting (burn)