startup-evaluation

Startup Evaluation

Evaluate the health of a startup, not just the attractiveness of its pitch.

This skill combines:

• MIT Disciplined Entrepreneurship: beachhead, customer, value, business model, validation
• Timmons model: opportunity x team x resources dynamic fit
• VC 5T model: Team, Target Market, Tech/Product, Traction, Terms
• PMF and pretotyping: behavior evidence before build effort
• Efficiency models: define the question, use MECE, test assumptions cheaply

Usage Template

Prompt

Use startup-evaluation on this company/idea.
Assess startup health, evidence quality, red flags, fundraising readiness, and the next cheapest validation step.

Use Case

• Founder wants a health check before building, hiring, or fundraising.
• Investor wants a due diligence memo or pass/lean-in decision.
• Team wants to identify the one constraint limiting progress.

Expected Result

• A structured startup health memo with scores, evidence, risks, and validation experiments.

Output Example

• Stage: Seed. Health score: 62/100. Verdict: promising but not Series A ready. Top constraint: weak retention evidence. Next test: 20-customer paid pilot with D30 retention threshold.

Verification Case

• The output separates facts, assumptions, and opinions; every score cites evidence or marks missing evidence.

Verified Effect

• Startup enthusiasm becomes a health dashboard, risk register, and concrete validation plan.

Success Metrics

• Stage, startup type, and evaluation objective are explicit.
• Scores distinguish evidence-backed health from narrative confidence.
• The memo identifies top constraint, fatal risks, runway status, and next cheapest test.
• VC-scale companies are judged by power-law upside; bootstrapped companies are judged by cashflow and durability.

When to Use

• "Evaluate this startup."
• "Is this business healthy?"
• "Should I invest / join / keep building / fundraise?"
• "Review my pitch deck or startup idea."
• "What is the next validation experiment?"

Startup Health Model

Use this equation as the mental model:

Startup Health =
  Opportunity quality
  x Team quality
  x Evidence momentum
  x Capital discipline
  x Learning velocity
  - Fatal risks

Do not average away a fatal flaw. A brilliant market with no reachable customer, no team fit, or six weeks of runway is unhealthy.

Step 1: Classify the Case

Before scoring, classify:

| Field | Options | |---|---| | Stage | idea, pre-seed, seed, Series A, growth, mature | | Startup type | lifestyle/SME, innovation-driven, VC-scale, hard tech, AI-native | | Evaluation lens | founder health check, investor due diligence, fundraising readiness, pivot decision | | Evidence state | narrative only, interviews, behavior, payment, retention, repeatable growth |

If data is missing, continue with assumptions and mark confidence low.

Step 2: Evidence Ladder

Rank claims by evidence quality:

| Level | Evidence | Weight | |---|---|---| | 0 | Founder belief, TAM slide, friend feedback | very weak | | 1 | Customer interviews about past behavior | weak | | 2 | Landing page, waitlist, demo usage | moderate | | 3 | Paid pilot, preorder, signed LOI, repeated use | strong | | 4 | Retention, expansion, organic referral, healthy unit economics | very strong |

Quotes and intentions do not prove demand. Payment, repeated usage, retention, and referral are stronger.

Step 3: Score Startup Health

Default weights. Adjust only when stage or startup type clearly requires it.

| Dimension | Weight | Healthy signal | Red flag | |---|---:|---|---| | Customer pain and beachhead | 15 | narrow painful use case, reachable buyer, urgent workflow | vague user, nice-to-have pain | | Market and timing | 15 | large/growing market or focused profitable niche, clear timing window | TAM-only logic, market too early/late | | Value proposition and 10x | 15 | 10x better, 1/10 cost, or new capability | marginal improvement | | PMF and traction | 15 | retention, payment, pull, repeatable channel | paid growth only, high churn, weak usage | | Business model and unit economics | 10 | LTV/CAC > 3, clear pricing, gross margin path | CAC unknown, payback too long | | Team and governance | 15 | founder-market fit, complementary roles, written equity/decision rules | solo gaps, cofounder conflict, weak recruiting | | Capital and runway | 10 | 12-18 month runway, milestone-based spend, financing plan | <6 month runway, unfocused burn | | Moat and risk control | 5 | data, network, distribution, regulatory or execution moat | easily copied, platform/model dependency |

Score each dimension:

0 = absent
1 = narrative only
2 = weak signal
3 = plausible but incomplete
4 = evidence-backed
5 = strong and repeatable

Final score:

| Score | Status | Meaning | |---:|---|---| | 80-100 | Healthy | Scale or fundraise if risks are bounded. | | 65-79 | Promising | Continue, but fix the top constraint before major spend. | | 50-64 | Fragile | Narrow scope and validate before hiring/fundraising. | | 0-49 | Unhealthy | Pivot, pause, or redesign assumptions. |

Step 4: VC 5T Cross-Check

For VC-backed or investor-facing evaluations, add a 5T view:

| 5T | Question | |---|---| | Team | Why this team? What unfair insight or execution proof exists? | | Target Market | Can this become a power-law outcome, not just a good business? | | Tech/Product | Is there defensibility beyond using current tools? | | Traction | Is growth pulled by customers and retention, not only paid push? | | Terms | Does valuation, dilution, and round structure leave room for returns? |

Use the 5T view to decide whether the company is venture-scale. A healthy bootstrapped company can still be a poor VC investment.

Step 5: AI-Native and Hard-Tech Addendum

Use when relevant:

| Question | Why it matters | |---|---| | Is this Type 1, 2, or 3 AI? | Tools on existing software, replacement software, or software becoming labor have different TAM and pricing logic. | | Does the product improve 10-40% or 10-40x? | VC outcomes need step-change value, not small convenience. | | What remains if code or models get cheaper? | Moat must move to data, workflow, distribution, trust, regulation, or physical execution. | | Is there product surplus? | Products built for the next model can fail before users adopt them. | | Are hardware, supply chain, regulation, or deployment loops bottlenecks? | Hard-tech health depends on non-software execution constraints. |

Step 6: Runway and Financing Health

Use these thresholds:

| Metric | Green | Yellow | Red | |---|---|---|---| | Runway | 18+ months | 6-18 months | <6 months | | Fundraising start | before 12 months runway | 6-12 months | after crisis starts | | LTV/CAC | 3-5+ | 1-3 | <1 or unknown at scale | | CAC payback | <12 months | 12-18 months | >18 months | | Burn focus | tied to next milestone | mixed | vanity hiring/spend |

Capital should buy evidence for the next milestone. Spending that does not reduce the top uncertainty is unhealthy.

Step 7: Diagnose the Constraint

Name one primary constraint:

| Constraint | Typical fix | |---|---| | No painful problem | customer discovery and problem pivot | | Wrong beachhead | segment narrower by buyer, workflow, urgency | | Weak value proposition | quantify before/after value and 10x wedge | | No behavior evidence | pretotype, paid pilot, concierge MVP | | Weak retention | reduce scope, improve core workflow, delay scale | | Team gap | recruit missing builder/seller/operator, clarify equity and decisions | | Capital risk | cut burn, milestone fundraising, smaller experiment | | Moat risk | build data loop, distribution edge, workflow lock-in, trust layer |

Step 8: Output Format

Return:

## Startup Health Memo

Stage / type / lens:
Verdict:
Health score:
Confidence:

### Evidence Ledger
Facts:
Assumptions:
Missing evidence:

### Scorecard
| Dimension | Score | Evidence | Risk |

### Top Constraint
...

### Red Flags
...

### Fundraising Readiness
...

### Next Cheapest Test
Hypothesis:
Experiment:
Metric:
Decision rule:
Cost/time:

### 30-Day Operating Focus
...

Quality Gates

• [ ] Stage, type, and lens are explicit.
• [ ] Facts, assumptions, and missing evidence are separated.
• [ ] Health score uses the scorecard and does not hide fatal risks.
• [ ] VC-scale and bootstrapped-health judgments are not conflated.
• [ ] Customer pain, beachhead, PMF, unit economics, team, runway, and moat are covered.
• [ ] Top constraint is singular and actionable.
• [ ] Next cheapest test has a metric, threshold, cost/time box, and decision rule.
• [ ] Final verdict names residual uncertainty.

Anti-Patterns

• Treating a large TAM as proof of opportunity.
• Scoring a startup from pitch quality rather than customer behavior.
• Averaging high team quality with zero demand evidence.
• Recommending fundraising before knowing the milestone capital will prove.
• Calling an AI wrapper defensible without data, distribution, workflow, or trust moat.
• Giving many generic suggestions instead of one constraint and one next test.

Connections

• [[wiki/concepts/创业基础框架]] — Timmons opportunity x team x resources
• [[wiki/concepts/创业机会识别]] — opportunity sources, beachhead, TAM/SAM/SOM
• [[wiki/concepts/产品市场契合]] — MVP, retention, payment, referral
• [[wiki/concepts/创业融资]] — runway, LTV/CAC, fundraising stages
• [[wiki/concepts/创业团队建设]] — founder fit, complementary roles, equity rules
• [[wiki/concepts/顶级VC评估框架]] — 5T, TOPSCAN, T2D3
• [[maps/创业知识库]] — entrepreneurship system map
• [[maps/VC与一级投资知识库]] — investor evaluation map
• [[maps/效率思维与模型]] — hypothesis-driven, MECE, cheap tests