saas-reverse

name	saas-reverse
description	Reverse-engineer any SaaS into a venture-analyst-grade build blueprint. Scrapes the domain, gathers multi-source intelligence (reviews, HN, Reddit, job listings), and outputs a production-ready blueprint with moat analysis, churn vectors, upgrade gates, and a build prompt.
version	0.2.0

/saas-reverse

Reverse-engineer any SaaS product into an actionable build blueprint.

Usage

/saas-reverse linear.app
/saas-reverse notion.so
/saas-reverse stripe.com
/saas-reverse cal.com - we want to build a scheduling tool for lawyers
/saas-reverse loom.com - focus on async video for customer success teams

The optional annotation after - focuses the differentiation analysis and build prompt on your specific angle.

---

Phase 1 — Deep Scrape

Run the scraper to collect raw content from the target domain.

python3 skill/saas-reverse/scripts/saas_scraper.py <domain>

Fetches: /, /pricing, /features, /product, /about, /solutions, /blog, /changelog, /enterprise, /docs, /team, /customers

Also parses robots.txt to discover hidden paths and checks response headers for infrastructure signals.

Returns structured JSON:

• pages — path -> stripped text (up to 4000 chars per page)
• meta — title, og:title, og:description
• headlines — H1/H2/H3 text (first 20), reveals true positioning
• cta_language — button/link text (reveals conversion intent)
• tech_signals — detected from HTML patterns AND response headers
• pricing_signals — price patterns, tier names, trial mechanics
• social_proof — user count claims, award mentions, "trusted by" text
• feature_signals — combined feature/product/homepage text
• discovered_paths — paths found via robots.txt

Phase 2 — Multi-Source Intelligence

Run the intelligence gatherer for external signals.

python3 skill/saas-reverse/scripts/saas_intel.py <domain>

Searches across 6 sources:

1. Review sites — G2, Capterra, Trustpilot results via DDGS
2. Pain points — user complaints, "wish it had", "doesn't work" discussions
3. Alternatives — what people compare it to (competitor map)
4. Pricing intel — forum/comparison site pricing data (often more accurate than landing page)
5. Job listings — tech stack from real job descriptions (Lever, Greenhouse, LinkedIn)
6. HackerNews — community discussions, launch threads, Show HN comments
7. Reddit — organic sentiment, use case patterns, real feature requests

Returns JSON with all sources merged.

---

Phase 3 — Synthesis Analysis

With both data sources loaded, analyze the SaaS across these dimensions. Be specific — no vague language, no marketing phrases.

3.1 Product reality

• What does it actually do? (ignore taglines, look at screenshots/features/changelog)
• Who actually uses it? (role + company size + trigger moment — when does someone first need this?)
• Core value proposition in one sentence, as a user would say it, not the marketing team

3.2 Feature map

• List 8-20 features from scrape + headlines + feature_signals
• Classify each: Core (product dies without it) / Differentiator (why users pick this over alternatives) / Table stakes (expected but not special)
• Note gaps: what do competitors have that this product doesn't mention?

3.3 Business model dissection

• Pricing tiers with real numbers (use pricing_intel if site is vague)
• Free tier: what's included, what's the hard limit, what's the soft friction
• Upgrade gate: the exact moment that converts free → paid. Is it seat count? Storage? Feature lock? Reporting? API access? Be precise.
• Trial mechanics: days, card required, what gets unlocked
• Enterprise signals: custom pricing, SSO, audit logs, SLA — present or absent?
• Revenue model confidence: usage-based / seat-based / flat / hybrid

3.4 Real tech stack

Combine signals from:

• HTML tech_signals (from bundle patterns + tracking scripts)
• Response headers (Server, X-Powered-By, CF-Ray, X-Vercel-ID)
• Job listings (what they're hiring for = what they actually use)
• GitHub if open source (read the actual repo)

Tag each signal: [SEEN] = detected directly, [JOB] = from job listings, [INFERRED] = reasonable conclusion

3.5 Moat analysis

What makes this hard to copy? Score each moat type:

• Network effects — does value increase as more users join? (Slack: yes. Linear: no)
• Data moat — does the product get better with more usage data?
• Switching costs — how painful to migrate? (integrations, data export, habit)
• Brand/community — is there a cult following? (Notion, Linear — yes)
• Integrations — how many integrations, bidirectional or read-only?
• Workflow lock-in — does the product become the team's operating system?

For each: Strong / Moderate / Weak / None

3.6 Churn vectors

From review analysis (pain_points + reddit_sentiment + hn_discussions), identify:

• Top 3 reasons users leave (from negative reviews, not inference)
• Features users consistently request that aren't there
• Pricing complaints (too expensive? missing tier? surprise charges?)
• Support/reliability issues mentioned
• Competitor that "stole" users most often

3.7 User sentiment map

Classify HN + Reddit signals:

• Positive themes (what do fans keep praising?)
• Negative themes (what do critics consistently mention?)
• Neutral observations (pricing model, acquisition, direction)
• Power user patterns (how do heavy users use it differently?)

3.8 Competitive landscape

From alternatives_mentions + your knowledge:

• Direct competitors (same job-to-be-done, same buyer)
• Indirect competitors (different approach, same problem)
• Positioning matrix: where does this product sit vs competitors on 2 axes that matter?

3.9 Technical complexity score

Estimate build complexity for an indie dev or small team:

Component	LOE	Notes
Core data model	X days
Auth + orgs/teams	X days
Main feature loop	X days
Real-time features	X days	if needed
Integrations	X days	per integration
Payments/billing	X days
MVP total	X weeks	without polish

3.10 Differentiation angle

If building a version of this — what would you do differently to have a real chance?

• What user segment is underserved by the incumbent?
• What pricing model would win in a specific niche?
• What 1 feature would make switchers choose yours?
• What would the incumbent never build (because it would cannibalize or contradict their position)?

---

Phase 4 — Blueprint Output

Output the complete blueprint in this exact format. Fill every section — no placeholders, no "TBD".

---

Blueprint: [Product Name] ([domain])

Analyzed: [date] | Confidence: [High/Medium/Low based on data richness]

What it does

[2-3 specific sentences. No taglines. What does it actually do when you sit down and use it?]

Who uses it

[Role + company size + trigger moment. Example: "Engineering managers at Series A-C startups (10-100 engineers) who've outgrown GitHub Issues but find Jira too heavyweight. Trigger: team grows past ~8 engineers and standup tracking breaks down."]

Core features

Feature	Type	Description
[name]	Core / Diff / Table stakes	[what it does — be specific]

Business model

• Free tier: [what's included + hard limit]
• [Tier]: $[X]/[seat or month] — [what unlocks]
• Enterprise: [signals or "not detected"]
• Upgrade gate: [exact trigger] [SEEN/INFERRED]
• Trial: [days + card required?]
• Revenue model: [seat-based / usage-based / flat / hybrid]

Tech stack

Layer	Stack	Confidence
Frontend	[tech]	[SEEN/JOB/INFERRED]
Backend	[tech]	[SEEN/JOB/INFERRED]
Database	[tech]	[SEEN/JOB/INFERRED]
Auth	[tech]	[SEEN/JOB/INFERRED]
Infrastructure	[tech]	[SEEN/JOB/INFERRED]
Analytics	[tech]	[SEEN/JOB/INFERRED]

Moat analysis

Moat	Strength	Evidence
Network effects	Strong/Moderate/Weak/None	[why]
Switching costs	Strong/Moderate/Weak/None	[why]
Data advantage	Strong/Moderate/Weak/None	[why]
Brand/community	Strong/Moderate/Weak/None	[why]
Integrations	Strong/Moderate/Weak/None	[count + depth]

Overall moat: [1-2 sentences — what keeps users, honestly]

Churn vectors

1. [Reason] — [evidence from reviews/reddit/HN]
2. [Reason] — [evidence]
3. [Reason] — [evidence]

Most requested missing feature: [from community data] Competitor that wins most defectors: [name + why]

User sentiment

Fans say: [2-3 recurring positive themes] Critics say: [2-3 recurring complaints] Power users do: [how heavy users use it beyond the basics]

Competitive landscape

Product	Positioning	Vs this product
[competitor]	[one line]	[stronger/weaker on what]

Build complexity

Component	LOE
Data model + migrations	X days
Auth + team/org structure	X days
Core feature loop	X days
Real-time (if needed)	X days
Payments + billing	X days
Key integrations	X days per
MVP	X weeks

Differentiation angle

Underserved segment: [who the incumbent ignores or serves poorly] Winning move: [1 thing you'd build differently] Pricing angle: [model that would work in the niche] What the incumbent won't build: [why + why that's your opening]

Pages to build

• / — Landing (positioning: [angle])
• /pricing
• /login, /signup
• /dashboard — [main workspace description]
• [all app pages visible from product or scrape]

Core user flows

Onboarding

1. [step — include friction points and drop-off risks]
2. [step]

Daily core action (the loop that creates habit)

1. [step]
2. [step]

Upgrade moment (what triggers the paywall)

1. [step — the exact moment]

Data models

// Primary entities — inferred from product behavior
type [MainEntity] = {
  id: string;
  [key fields with types];
};

type [SecondaryEntity] = {
  id: string;
  [key fields];
};

Build prompt

Paste directly into Claude Code, Cursor, or any AI coding assistant:

---

Build a [product type] SaaS called [working title or name].

What it does: [2-3 sentences — specific, no marketing language]

Target user: [role + company size + pain point + trigger]

Tech stack:

• Frontend: Next.js 15 (App Router) + Tailwind CSS + shadcn/ui
• Backend: Next.js API routes + tRPC (if type safety needed)
• Database: PostgreSQL with Prisma ORM — use Neon for serverless
• Auth: Clerk (handles org/team auth out of the box)
• Payments: Stripe — implement [seat-based/usage-based/flat] billing
• Email: Resend with React Email templates
• Hosting: Vercel

Core features to build (in priority order): [numbered list — each with a brief description of behavior, not just name]

Business rules:

• Free tier limits: [exact limits]
• Upgrade gate: [exact trigger — e.g., "block creating >3 projects, show upgrade modal"]
• [any other gates, trial behavior, org/team logic]

Data models: [all entities with fields and types — be complete]

Pages: [all pages with brief description of content/behavior]

Key flows:

1. Onboarding: [steps]
2. Core loop: [steps]
3. Upgrade: [exact trigger + modal behavior]

What to build first: Data schema + auth + team structure. Then the core feature loop. Then billing. Landing page last.

Differentiation from [main competitor]: [the one thing to do differently]

---

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Evidence tagging

Apply these tags to any claim:

• [SEEN] — directly visible on the site (pricing page, feature list, tech detected)
• [JOB] — inferred from job listing language
• [REVIEW] — from user reviews, Reddit, or HN discussions
• [INFERRED] — reasonable technical conclusion
• [SPEC] — educated guess, explicitly uncertain

Do not present inferences as facts. A good blueprint is honest about its confidence level.