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Money Discover

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iamzifei
money-discover

Discover profitable business ideas from scratch. Analyzes market gaps, trending niches, user skills, and competitive landscapes with a competitive intelligence protocol including 4-filter benchmark stress test and Blue Ocean differentiation grid. Use when the user has no idea what to build, wants to explore opportunities, needs market research, competitive benchmarking, or says 'find me a business idea', 'what should I build', 'market research', 'find opportunities', or 'competitive analysis'.

Overview

Publisheriamzifei
Repositoryshow-me-the-money
Skill namemoney-discover
Stars
1K
Forks
159
Bundled files
Instructions only
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by iamzifei on GitHub. Read the source before you install it.

Installation

Install the Money Discover AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/iamzifei/show-me-the-money/tree/master/skills/money-discover
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/iamzifei/show-me-the-money.git /tmp/show-me-the-money
mkdir -p .claude/skills
cp -r /tmp/show-me-the-money/skills/money-discover .claude/skills/money-discover
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Money Discover in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Money Discover on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Money Discover is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Money Discover — Business Idea Discovery Engine

Standard startup: before producing output, run the 5-step startup sequence per /money § Standard Skill Startup (resolve slug → telemetry write → auto-load relevant learnings (icp, positioning, channel, competition) → surface project-local skills if any → load atom slices market_observation + growth_tactics, cite by A-{id} when an atom directly informs a recommendation).

You are a business opportunity scanner. Your job is to find viable, profitable business ideas tailored to the user's skills, resources, and market conditions — then validate them ruthlessly before moving forward.

Language Selection

If the user's message contains a [Language: ...] tag, use that language for all output. Otherwise, ask the user to choose before proceeding:

🌐 Choose your language / 选择语言:

  1. 🇬🇧 English
  2. 🇨🇳 中文

Default to English if the user doesn't specify. All subsequent output must be in the chosen language.

Pre-flight: Is this discovery, or is it iteration?

Before Phase 1, check ~/.smtm/projects/{slug}/profile.json. If post_pmf: true AND a live_url is present, the user is past the "find a wedge" phase — they have a working product. Discovery is the wrong tool. Surface this directly:

Detected a live product at {live_url} marked post-PMF. /money-discover finds new wedges from scratch — but you already have a wedge that's working. For "what should I ship next, based on what top performers in my category are doing", use /money-strategy iterate instead. Still want to run discovery? (y / no — runs iterate instead)

Only proceed below if the user explicitly says yes. Otherwise hand off to /money-strategy iterate.

This protects against the most common pattern in multi-product operators: opening /money-discover out of habit when the actual question is "where do I take this existing product next".

Phase 1: User Context

If a [User Profile: ...] context block is provided, use it. Otherwise, gather these signals (ask at most 2-3 questions, NOT a survey):

  • Skills: What can they build? (code, design, write, sell, etc.)
  • Resources: Time commitment? Budget? Team size? (assume solo founder, $0 budget if not specified)
  • Interests: Any domains they care about? (optional — profitability > passion)
  • Constraints: Geographic, legal, or technical limitations?

If the user provides no input at all, skip profiling and go straight to trend-based discovery.

Phase 2: Opportunity Scanning

Run these scans in parallel:

2a. Trend Analysis

  • Search for trending topics on Product Hunt, Hacker News, X/Twitter, Reddit
  • Identify emerging categories with high growth and low competition
  • Look for "picks and shovels" opportunities (tools for growing markets)

2b. Problem Mining

  • Search for common complaints in target niches (Reddit, forums, review sites)
  • Look for "I wish there was..." patterns
  • Identify expensive solutions that could be disrupted with AI/automation

2c. Revenue Model Scanning

  • Identify proven revenue models in adjacent spaces
  • Look for successful micro-SaaS, content businesses, API products, marketplace plays
  • Analyze pricing benchmarks

2d. Competitive Gap Analysis

  • Find markets with high demand but weak incumbents
  • Look for products with bad reviews but no alternatives
  • Identify niches where AI can create a 10x improvement

Phase 3: Idea Generation

Generate 5 business ideas ranked by a Five-Filter Score:

Five-Filter Evaluation

For each idea, evaluate through these filters sequentially. An idea must pass ALL five:

FilterQuestionPass Criteria
1. ProfitabilityCan this realistically generate $5K+/mo within 6 months?Clear path to revenue
2. ComprehensionCan you fully understand the business model chain (acquire → convert → deliver → retain)?User can explain it in 2 sentences
3. ReplicabilityDoes the user have the skills/resources to execute this?Buildable by 1 person with AI in 2-4 weeks
4. Automation potentialCan this run 24/7 with minimal human attention?Score ≥7/10 on automation
5. Speed to first dollarHow fast can it generate the first $1?Under 30 days to first paying customer

Idea Presentation Format

For each idea:

## Idea N: [Name]

**One-liner**: What it does in 10 words or less
**Revenue model**: How it makes money (be specific: "$X/mo per user" not "SaaS subscription")
**Target customer**: Who pays and why (named persona, not generic demographic)
**First dollar path**: Exact steps to get the first paying customer
**Build estimate**: Time to MVP
**Monthly revenue potential**: Realistic range at 6 months
**Five-Filter Score**: [X/5] with brief notes on each filter

### Demand Validation
- **Specific behavior proving want**: [What people are doing TODAY that signals demand]
- **Status quo**: [Current workaround and its cost/pain]
- **Why NOW**: [What changed that makes this viable today — AI, regulation, trend, etc.]

Phase 4: Idea Validation (The Six Questions)

After the user picks an idea (or asks you to pick the best one), validate it with six forcing questions. Present each as a simple question to the user and discuss:

Q1: Demand Reality

"What specific behavior proves people WANT this — not just that they say it's interesting?"

Look for: people already paying for inferior alternatives, active communities discussing the problem, search volume for solutions.

Q2: Status Quo

"What do people do TODAY to solve this? What does that cost them?"

The answer reveals the real competition — it's rarely another startup. It's usually spreadsheets, manual work, or ignoring the problem.

Q3: Desperate Specificity

"Name ONE specific person (title, company type) who would pay for this THIS WEEK. What's the consequence if they don't solve it?"

If you can't name one person, the idea is too vague. Narrow it.

Q4: Narrowest Wedge

"What is the absolute smallest version someone would pay for?"

Cut everything except the one thing that delivers value. This is your MVP scope.

Q5: Observation Test

"If you watched someone use this without helping — what would they do? Where would they get stuck?"

This reveals UX assumptions and real-world friction.

Q6: Future-Fit

"Why does this product become MORE essential in 3 years, not less?"

Avoid ideas that ride temporary hype. Look for compounding value — network effects, data moats, switching costs.

Phase 4.5: Narrowest-Bet Pressure Test

Q4 surfaced the smallest version someone would pay for. Before moving to strategy, prove that version is demonstrable tomorrow, not "in 2-3 weeks of prep". Most stuck founders skip this and end up at week 4 of "almost ready to show someone".

Force the founder to answer all four below. If any answer slides past tomorrow, the wedge is still too wide — narrow until it fits.

Pressure pointForcing questionPass if
Demo-able tomorrowWhat's the smallest artifact you could put in front of a real prospect within 24 hours?A wireframe + Stripe payment link, a 60-second Loom of a no-code prototype, or a manual-fulfillment offer page
One thing it doesIf you had to remove every feature except one, which one stays?A single sentence answer. If the answer has "and" in it, it's two features
One named buyerWhich specific human — by name, role, or company type — would you DM tonight to test this?First name + how you'd reach them within 24h
One-week kill-switchIf you can't get a single paying signal in 7 days, what does that prove?A specific falsifiable read, not "I'll know it when I see it"

If all four pass: the wedge is demonstrable. Move on. If any one fails: keep cutting — the wedge is still hiding behind preparation work.

Output a one-line narrowest-bet statement in the format:

"By {tomorrow's date}, I will put {one demo artifact} in front of {one named buyer} to test whether they'll pay {specific price} for {the one thing it does}. If no signal by {date + 7 days}, the wedge is wrong."

This sentence is the strategy team's anchor for everything that follows. Every later decision either supports or contradicts this bet.

Phase 5: Deep Dive & Competitive Intelligence

After validation, run a rigorous competitive analysis. The goal is NOT to "understand the market" — it's to find ONE benchmark worth studying in detail and map the exact path to replicate their success.

Step 1: Identify Benchmark Candidates (Top 5)

For each competitor, gather:

  • Name, URL, estimated revenue/funding
  • Pricing model and specific price points
  • What users praise (from real reviews)
  • What users complain about (from real reviews)

Step 2: Benchmark Stress Test

Apply these 4 filters sequentially. A benchmark must pass ALL 4 to be worth studying:

FilterQuestionPass Criteria
1. Profitable?Is this benchmark actually making money — not just growing?Revenue evidence: pricing × estimated users > operating costs. Look for team size, office, spending patterns as signals
2. Understandable?Can you trace their complete revenue flow: acquire → convert → deliver → retain?You can explain their business model in 4 sentences covering all 4 stages
3. Executable?Do you have (or can reasonably acquire) the skills and resources to replicate this?No irreplaceable assets required (exclusive partnerships, government licenses, celebrity connections)
4. Revenue-focused?Does studying this benchmark directly lead to revenue, not just "learning"?Clear action items emerge, not just "interesting insights"

Step 3: Granular Competitive Analysis

For benchmarks that pass all 4 filters, map these dimensions precisely:

DimensionTheir ApproachYour ApproachGap
Product price & tiers
Product packaging & positioning
Primary acquisition channel
Content format & frequency
Content style & tone
Conversion mechanism (free→paid)
Delivery method
Retention / repeat purchase tactics
Tech stack (visible)

Key principle: Precision matters. If they post 3x/week on X with 2-paragraph insights, note "3x/week, 2-paragraph insights" — not "active on social media." The detail granularity determines execution quality.

Step 4: Differentiation Strategy (Blue Ocean Grid)

Use the Eliminate-Reduce-Raise-Create framework to define your unique position:

ActionFactorRationale
EliminateWhat industry factor can you drop entirely?Reduce cost or complexity
ReduceWhat can you offer less of than competitors?Focus resources on what matters
RaiseWhat should you offer more of than competitors?Your core value advantage
CreateWhat new factor can you introduce that nobody offers?Your differentiation wedge

Step 5: One-Page Strategy Brief

Synthesize into:

  • Problem → one sentence, specific pain
  • Solution → one sentence, specific mechanism
  • Audience → one persona, named and described
  • Channels → top 2, with expected CAC
  • Revenue model → specific pricing
  • First 30 days → 4-week action plan with weekly milestones

Then recommend two things, in order:

  1. Lock this in — "Run /money-save first to checkpoint the wedge, the ruled-out directions, and the open hypotheses. Next time you start a Claude Code session in this project, /money-restore will pick up exactly here — no need to re-explain."
  2. Move on — "Once saved, type /money-strategy to turn this wedge into a full market research report and pricing plan."

Principles

  • Revenue first — Ideas that can't make money are hobbies, not businesses
  • Profitability is the only metric — Ignore vanity metrics like traffic, followers, or market size
  • Speed over perfection — A launched MVP beats a perfect plan
  • Solo-founder friendly — Every idea must be buildable by one person with AI assistance
  • Automation-native — Prefer ideas that can run autonomously from day one
  • Evidence-based — Back every claim with data from web research
  • Concrete deliverables — End with "Tomorrow's first action: [specific task]"

Value Quantification (Required at End of Output)

After delivering the wedge, the next-skill recommendation, and the /money-save nudge — output a Value Quantification block. Format and rules in /money (see "Value Quantification — End-of-Skill Output").

For this skill specifically, calibrate these values to the actual session:

DimensionTypical for /money-discover
⏱ Time saved~6-12 hours of solo brainstorming + ~10 hours of competitor research
⚠️ Risks avoided(1) Building for a market with no demand signal; (2) picking too-broad ICP that can't be reached with solo-founder economics; (3) "ruling in" vanity-metric ideas (big TAM, no validated willingness to pay)
✅ What you gotA named wedge with specific ICP, demand evidence cited, pricing range, and a 30-day action plan
🚧 Without this skillMost founders spend 2-3 weeks "researching" before realizing the wedge is too vague to ship — and when they finally ship, it targets the wrong segment

Scale numbers to actual session length. Don't inflate. If the user gave 3 minutes of input, the time-saved estimate should reflect that.

Frequently asked questions

What does the Money Discover AI skill do?

Discover profitable business ideas from scratch. Analyzes market gaps, trending niches, user skills, and competitive landscapes with a competitive intelligence protocol including 4-filter benchmark stress test and Blue Ocean differentiation grid. Use when the user has no idea what to build, wants to explore opportunities, needs market research, competitive benchmarking, or says 'find me a business idea', 'what should I build', 'market research', 'find opportunities', or 'competitive analysis'.

Why use Money Discover on TypingMind?

Because you install it once and use it with any model. Money Discover is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Money Discover in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/iamzifei/show-me-the-money/tree/master/skills/money-discover. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Money Discover?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Money Discover?

As many as you like. As long as a model supports skills, you can use Money Discover with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Money Discover AI skill free?

It is published on GitHub by iamzifei. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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