Startup Analysis
Alex YangCommunityCreated by Alex Yang
- Skill
- Research and analysis
- Business and productivity
- MIT
- No issues found
AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
Startup Analysis provides a structured, multi-lens evaluation of private tech companies. By synthesizing data from public sources, it generates a report that examines a company's health and potential through three distinct viewpoints: the VC investor (focusing on market and economics), the job applicant (focusing on career risk and equity), and the CEO (focusing on operational efficiency and strategy). This approach ensures users understand not just the company's viability, but how it aligns with their specific goals, whether they are looking to invest, join, or lead.
What it does
- Gathers and synthesizes public data on startups including funding, traction, and team.
- Evaluates companies from a VC investor perspective to assess market opportunity and defensibility.
- Analyzes companies from a job applicant perspective to assess equity value and career risk.
- Assesses companies from a CEO perspective to identify operational strengths and strategic gaps.
- Provides a cross-perspective synthesis to highlight where different lenses agree or diverge.
- Delivers a structured report with clear verdicts for each perspective.
When to use it
- When performing due diligence on a potential investment target.
- When weighing a job offer from a startup to understand the risks and potential upside.
- When a founder needs an objective, third-party style assessment of their own company's health.
- When conducting general research on a private tech company's competitive position.
Advantages
- Provides a balanced view by separating investor, employee, and founder interests.
- Helps identify hidden risks like high burn rates or poor equity structures.
- Standardizes the evaluation process using clear metrics and frameworks.
- Encourages critical thinking by forcing the user to consider multiple stakeholder viewpoints.
Usage examples
- “What do you think of Acme Corp as a place to work?”
- The agent will research Acme Corp and provide a report focusing on the job applicant lens, including equity risks, culture, and runway, while providing context from the investor and CEO perspectives.
- “Research the startup 'Nebula AI' for me.”
- The agent will conduct a comprehensive 360-degree analysis of Nebula AI, covering market, traction, team, and providing verdicts for investment, employment, and operational health.
Install
Download the skill as a ZIP:
https://autogineer.com/agent-skills/himself65/startup-analysis/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip startup-analysis.zip -d .agents/skills
Documentation by Alex Yang
Startup Analysis
Produces a multi-perspective analysis of a startup, examining it through three lenses that each reveal different aspects of company health and potential:
VC Investor Lens — Is this a good investment? Market size, unit economics, growth trajectory, team quality, defensibility
Job Applicant Lens — Should I work here? Equity value, runway risk, culture signals, career growth, compensation fairness
CEO/Founder Lens — How healthy is this company? Product-market fit, burn efficiency, competitive moat, organizational health
Each perspective surfaces insights the others miss. A company can be a great investment but a terrible place to work (or vice versa). The goal is to give the user a 360-degree view so they can make informed decisions.
Step 1: Gather Information
Before analyzing, collect as much public information as possible about the startup. Use web search, the company's website, Crunchbase data, press coverage, and any other available sources.
Key data to gather:
| Category | What to find |
|---|---|
| Basics | Founded year, HQ location, employee count, what the product does |
| Funding | Total raised, last round (size, date, valuation if known), key investors |
| Product | What they sell, who buys it, pricing model, key competitors |
| Traction | Users, revenue (if public), growth signals, notable customers |
| Team | Founders' backgrounds, key hires, LinkedIn headcount trends |
| Market | Industry, market size estimates, tailwinds/headwinds |
| News | Recent press, product launches, partnerships, layoffs, pivots |
If certain data isn't publicly available (e.g., revenue for private companies), note the gap and infer what you can from indirect signals (hiring pace, customer logos, web traffic proxies, job postings).
When information is insufficient
Many startups — especially early-stage or niche ones — have limited public presence. If web search does not return enough information to produce a meaningful analysis (e.g., you can't determine what the company does, who founded it, or how it's funded), ask the user to provide the company's website URL before proceeding. The company website is often the single most information-dense source, and reading it directly (about page, pricing page, team page, blog) can fill most gaps.
You can also ask the user for:
The company's website or landing page URL
A Crunchbase, LinkedIn, or PitchBook link
Any pitch deck, job listing, or press article they have
Specific context they already know (e.g., "they just raised a Series A from Sequoia")
It is better to ask for a URL and produce an accurate analysis than to guess and produce a misleading one.
Step 2: Determine Which Perspectives to Cover
By default, produce all three perspectives. If the user specifies a particular angle (e.g., "I'm considering joining them" or "should I invest"), emphasize that perspective but still include the others as context — they often reveal relevant information.
| User's situation | Primary perspective | Still include |
|---|---|---|
| Considering investing | VC Investor | Job Applicant (talent signal), CEO (operational health) |
| Considering a job offer | Job Applicant | VC Investor (funding runway), CEO (strategic direction) |
| Running the company / advisory | CEO/Founder | VC Investor (how investors see you), Job Applicant (talent attractiveness) |
| General curiosity / research | All equally | — |
Step 3: Analyze from Each Perspective
Read the relevant reference files for the detailed framework for each perspective. These contain the specific criteria, metrics, and red/green flags to evaluate.
VC Investor Analysis
Read references/vc-framework.md for the full evaluation framework.
Core areas to assess:
Market opportunity — TAM/SAM/SOM, market timing, secular trends
Product & traction — Product-market fit signals, growth metrics, retention
Unit economics — CAC, LTV, margins, burn multiple, path to profitability
Team — Founder-market fit, technical depth, hiring ability
Defensibility — Moats (network effects, switching costs, data, brand, regulatory)
Deal terms context — Stage-appropriate valuation, comparable exits
Produce a clear Investment Thesis (bull case) and Key Risks (bear case). End with a verdict: Strong Pass / Lean Pass / Lean Invest / Strong Invest, with reasoning.
Job Applicant Analysis
Read references/job-applicant-framework.md for the full evaluation framework.
Core areas to assess:
Financial stability — Runway, burn rate, funding trajectory, revenue health
Equity value — Option/equity package analysis, dilution risk, liquidation preferences, realistic exit scenarios
Career growth — Role scope, learning opportunity, resume value, mentorship
Culture & work-life — Glassdoor signals, employee tenure data, leadership style
Product & market risk — Is PMF real? What happens if the startup fails?
Red flags — High turnover, constant pivots, vague metrics, founders cashing out
Produce a clear Why Join (pros) and Watch Out For (risks). End with a verdict: Strong Pass / Lean Pass / Lean Join / Strong Join, with reasoning.
CEO/Founder Analysis
Read references/ceo-framework.md for the full evaluation framework.
Core areas to assess:
Product-market fit — Retention curves, organic growth, Sean Ellis test proxy
Growth efficiency — Burn multiple, CAC payback, magic number
Competitive position — Moat strength, competitive dynamics, market share trajectory
Organizational health — Hiring pipeline, attrition, team capability gaps
Fundraising readiness — Metrics vs. benchmarks for next round, investor narrative
Strategic risks — Platform dependency, customer concentration, regulatory exposure
Produce a clear Strengths to Double Down On and Urgent Areas to Address. End with a health grade: Critical / Struggling / Stable / Strong / Exceptional, with reasoning.
Step 4: Synthesize Cross-Perspective Insights
After the three analyses, add a synthesis section that highlights:
Where perspectives agree — If all three lenses flag the same strength or weakness, it's probably real
Where perspectives diverge — A company can be VC-attractive (huge market) but employee-risky (high burn, low runway). Call these out.
The bottom line — One paragraph summary: what kind of company is this, what's its most likely trajectory, and what should the user do based on their stated (or implied) situation
Step 5: Present the Report
Write it as a scannable report titled [Company Name] — Startup Analysis:
Summary — a short overview that states the three verdicts up front.
One section per perspective, in the order that fits the user's situation from Step 2. Each covers the core areas from Step 3 with the evidence behind every judgment, and ends with its verdict on the scale defined there — Investment Verdict (Strong Pass / Lean Pass / Lean Invest / Strong Invest), Employment Verdict (Strong Pass / Lean Pass / Lean Join / Strong Join), or Health Grade (Critical / Struggling / Stable / Strong / Exceptional) — followed by the reasoning.
Cross-perspective synthesis — where the lenses agree, where they diverge, and the bottom line.
Scale each section to the evidence: where financials or other areas are opaque, say so in a line and spend the space on what's observable, rather than filling a heading for its own sake. Don't fabricate metrics; make informed inferences and state your confidence level.
Reference Files
references/vc-framework.md— VC due diligence checklist with metrics, benchmarks, and red/green flagsreferences/job-applicant-framework.md— Job seeker evaluation framework with equity analysis and culture assessmentreferences/ceo-framework.md— CEO self-assessment framework with operational metrics and strategic analysis
Read these when you need the detailed criteria and benchmarks for each perspective.
Files
- ATTRIBUTION.mdAdded by AutoGineer891 B
- LICENSELicence file added by AutoGineer1.0 KiB
- README.md2.0 KiB
- SKILL.md8.6 KiB
- references/ceo-framework.md7.8 KiB
- references/job-applicant-framework.md7.4 KiB
- references/vc-framework.md6.4 KiB
references/job-applicant-framework.md
Job Applicant Startup Evaluation Framework
Detailed framework for evaluating whether to join a startup as an employee. The core question: is the risk/reward tradeoff worth it compared to a safer, better-paying job at an established company?
1. Financial Stability Assessment
Runway & Funding
| Signal | Green | Yellow | Red |
|---|---|---|---|
| Last funding round | <12 months ago, healthy amount | 12-18 months ago | >18 months ago with no revenue growth |
| Runway | 18+ months | 12-18 months | <12 months |
| Investor quality | Top-tier VCs (a16z, Sequoia, etc.) | Mid-tier or strategic investors | Unknown angels, no institutional backing |
| Revenue trend | Growing >50% YoY | Growing but slowing | Flat or declining |
| Burn trajectory | Decreasing burn multiple | Stable | Increasing burn, no revenue growth |
How to research
Crunchbase / PitchBook — Funding history, investors, valuation
LinkedIn headcount — Is the team growing, flat, or shrinking?
Job postings — Lots of openings = growth; few = maintenance mode; mass closings = trouble
News — Recent layoffs, pivots, leadership changes
Glassdoor — Employee reviews, especially recent ones mentioning "runway" or "funding"
Questions to Ask in Interviews
"What's your current runway?" (they should answer openly; evasion is a red flag)
"When do you plan to raise next, and how's that process going?"
"What's your revenue trajectory looking like?"
"Has there been any restructuring or layoffs in the past year?"
2. Equity & Compensation Analysis
Understanding Your Equity
| Term | What it means for you |
|---|---|
| Stock options (ISO/NSO) | Right to buy shares at a set price (strike price). Worthless if company value < strike + preferences |
| RSUs | Actual shares granted. More valuable than options but rare at early-stage startups |
| Strike price / 409A | The "buy" price for options. Lower = more potential upside |
| Vesting schedule | Typically 4 years with 1-year cliff. You own nothing until the cliff |
| Preference stack | Investors get paid first in an exit. If they have 2x preferences and the company sells for 2x invested capital, common shareholders (you) get $0 |
| Dilution | Your % shrinks with each funding round. Expect 15-25% dilution per round |
| Exercise window | How long after leaving you can buy vested options. 90 days is standard but brutal — you may have to pay $50K+ to exercise |
Equity Valuation Reality Check
To estimate what your equity might actually be worth:
Start with the last 409A valuation (ask for it)
Estimate realistic exit scenarios — Most startups don't exit at unicorn valuations. Model: acquisition at 2-5x last round, IPO at 5-10x, and failure (0)
Apply the preference stack — Subtract total investor preferences before calculating common share value
Apply dilution — Assume 2-3 more rounds of 20% dilution each
Probability-weight — ~70-80% of VC-backed startups fail. Even "good" ones often exit below the preference stack
Compensation Benchmarking
| Factor | How to think about it |
|---|---|
| Cash below market | Expect 10-30% below big-tech base salary; more than that is a red flag |
| Equity as gap-filler | Equity should more than compensate for the cash gap in an expected-value sense |
| Total comp comparison | Compare total expected comp (cash + equity expected value) against FAANG/big-tech offers |
| Startup risk premium | You should expect meaningfully higher total comp potential to justify the risk, illiquidity, and extra work |
3. Career Growth Assessment
Signals of Good Growth Potential
| Signal | What to look for |
|---|---|
| Role scope | Will you own significant areas, or be a cog? Early employees get outsized scope |
| Learning velocity | Are you working with people better than you in key areas? |
| Resume value | Is this company/brand recognizable? Will it open doors? |
| Title trajectory | Startups often offer faster title progression, but titles mean less |
| Mentorship | Is there someone senior in your function? Or are you building from scratch? |
| Network | Will you meet investors, operators, and experts you wouldn't otherwise? |
When Startup Experience Is Most Valuable
Early in career (first 5-7 years): maximum learning, acceptable risk
When switching functions: startups let you wear many hats
When building founder skills: closest thing to founding without the risk
When the startup's domain aligns with your long-term career direction
When It's Less Valuable
Deep specialization needed: big companies have more depth
Financial obligations (mortgage, family): startup risk may not be appropriate
Late career with established reputation: incremental resume value is lower
4. Culture & Work-Life Signals
Positive Signals
Founders are transparent about challenges, not just hype
Employee tenure is reasonable (2+ years for early employees)
Clear values that show up in decision-making, not just a poster
Engineers/ICs have voice in product direction
Reasonable on-call and work hours expectations
Red Flags
Glassdoor reviews consistently mention burnout, toxicity, or chaos
"We're a family" language combined with 60+ hour expectations
High turnover in leadership positions
Founders talk about "crushing it" but can't articulate product strategy
No clear onboarding process or role definition
"We work hard and play hard" as a substitute for compensation
Questions to Ask
"What does a typical week look like for someone in this role?"
"Tell me about someone who was recently promoted — what did they do?"
"What's the biggest challenge the team is facing right now?"
"How does the company handle disagreements between founders/leadership?"
"What's the on-call rotation like?" (for engineering)
5. Product & Market Risk
Assessing from the Outside
| Signal | How to check |
|---|---|
| Product quality | Try the product yourself. Is it good? Would you use it? |
| Customer sentiment | Check G2, Capterra, Product Hunt, Twitter/X, Reddit |
| Competitor landscape | Who else does this? Is the market crowded or greenfield? |
| Platform dependency | Does the product depend on a platform that could cut them off or compete? |
| Technical risk | Is the product technically hard (moat) or could it be replicated quickly? |
What Happens If It Fails?
Think about your personal downside:
How long would it take to find a new job in your function/market?
Have you burned cash on exercising options that are now worthless?
Have you maintained your skills and network for a smooth transition?
Is the experience itself valuable on your resume regardless of outcome?
6. Verdict Framework
Scoring
Rate each area 1-5:
| Area | Weight |
|---|---|
| Financial stability | 25% |
| Equity upside potential | 20% |
| Career growth | 25% |
| Culture & work-life | 15% |
| Product & market risk | 15% |
Verdict Scale
| Verdict | Meaning |
|---|---|
| Strong Join | Compelling across most dimensions — take this job |
| Lean Join | Good opportunity with manageable risks, worth considering |
| Lean Pass | Meaningful concerns; only join if you have a specific reason (learning, network, passion for the problem) |
| Strong Pass | Significant financial risk, poor equity setup, or cultural red flags — look elsewhere |
Requirements and compatibility
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
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Attribution and licence
- Created by
- Alex Yang
- Publisher
- Alex Yang (Community)
- Source
- himself65/finance-skills · plugins/startup-tools/skills/startup-analysis · commit a085078
- Licence
- MIT License (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
AutoGineer is not affiliated with or endorsed by Alex Yang or Alex Yang.
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