angel-diligence

Category: Browser automation Risk: Unknown Mihir-Bhargav/OmniSkill NOASSERTION

name: angel-diligence
description: "Run a structured 30-minute angel diligence session that catches the red flags gut feeling reliably misses."

/angel-diligence

Vibes-based angel investing has a 90% loss rate because the same traits that make founders compelling in a pitch — conviction, charisma, narrative control — are exactly the traits that mask the signals that predict failure. This skill doesn't ask you to be less excited. It asks you to stress-test the three things that actually determine outcomes: founder judgment under uncertainty, evidence vs assertion in market claims, and whether the team actually understands the levers of their own business.

Founder Judgment Signals — ask these, not "tell me about yourself":

  • "What's the most important decision you've made in the last 6 months that you'd make differently?" (tests self-awareness and learning rate)
  • "What do your best advisors think you're wrong about — and what do you think about their view?" (tests intellectual honesty)
  • "What has to be true about customer behavior for your retention model to work?" (tests whether they've thought past acquisition)
  • "What's the earliest warning signal that your core assumption is wrong — and how would you know?" (tests whether they're paying attention to the right metrics)
  • "Why are you the person to build this, specifically? Not the market — you." (tests founder-market fit beyond narrative)

Market Validation — evidence vs assertion:

  • What is the source of the TAM number? Is it a research report or bottoms-up math from real customers?
  • How many non-friends-and-family paying customers exist today? What did it take to close them?
  • What did the last 3 churned customers say? (If they can't name 3 churned customers, ask why)
  • What's the evidence that this is a pull market — customers seeking the solution — not a push market?

Traction Signals That Predict, Not Describe:

  • Net Revenue Retention (NRR): tells you if existing customers are expanding. Below 100% is a retention problem disguised as a growth story.
  • Time-to-value: how fast does a new customer get their first win? Long TTVs predict churn.
  • Referral rate: what percentage of new customers came from existing customer referrals? High referral = product-market fit signal.
  • Sales cycle length trend: is it compressing or extending? Extending means the ICP is wrong.

The 3 Business Levers — and whether the team understands them:

  • What is the single metric that, if improved by 2x, changes the entire business trajectory? Do they answer immediately and correctly?
  • What is the biggest cost that doesn't show up on the P&L yet? (support burden, integration complexity, regulatory risk)
  • What breaks first at 10x revenue — and have they designed for it?

Your Own Bias Check:

  • Am I excited because the founder is like me? (affinity bias)
  • Am I excited because others I respect are in? (social proof bias)
  • Have I made one strong positive observation and stopped looking? (confirmation bias)
  • What would I have to believe about the market that I don't currently believe?

Rules

  1. No investment thesis without a specific falsifying condition — "what would prove me wrong?"
  2. Revenue traction beats everything except NRR. High revenue with declining NRR is a burning building.
  3. Market size claims require bottoms-up validation. TAM from a research report is a prior's assumption, not evidence.
  4. If you can't articulate the 3 business levers yourself before the meeting, you're not ready to invest.
  5. Document your biases before the meeting, not after.

The output of this skill is a one-page go/no-go memo: the thesis, the 3 risks ranked by severity, and the one question that, if answered differently, would change the decision.