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Startup Pressure Test

Community
Kappaemme-git
startup-pressure-test

Brutally evaluate and refine startup ideas with practical early-stage startup frameworks. Use when Codex is asked to pressure-test a startup idea, validate whether the problem is real, map competitors and current customer behavior, find the first 10 customers, define an MVP, create a 2-week launch plan, assess founder-market fit, or give a direct strong/weak/pivot verdict.

Overview

PublisherKappaemme-git
Repositorycodex-startup-pressure-test-skill
Skill namestartup-pressure-test
Stars
990
Forks
89
Bundled files
2
LicenseMIT
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.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by Kappaemme-git on GitHub. Read the source before you install it.

Installation

Install the Startup Pressure Test 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/Kappaemme-git/codex-startup-pressure-test-skill/tree/main/startup-pressure-test
  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/Kappaemme-git/codex-startup-pressure-test-skill.git /tmp/codex-startup-pressure-test-skill
mkdir -p .claude/skills
cp -r /tmp/codex-startup-pressure-test-skill/startup-pressure-test .claude/skills/startup-pressure-test
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Startup Pressure Test 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 Startup Pressure Test 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 Startup Pressure Test 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.

Startup Pressure Test

Overview

Use this skill to pressure-test a startup idea before the user wastes time building the wrong thing. Be direct, specific, and practical. Use a Paul Graham-style early startup lens: real users, painful problems, current behavior, manual traction, and the smallest test that proves or kills the idea.

Language

Match the user's language. If the user writes in Italian, answer in Italian. If the user writes in English, answer in English. Keep common startup terms in English when clearer: ICP, MVP, PMF, early adopter, switching cost, wedge.

First Move

If the idea is missing, ask for it first. Keep the question short:

text
Send me the startup idea, target customer, and what you want them to do/pay for.

If the idea is already provided, start immediately.

Modes

Choose the mode from the user request. If unclear, use full.

  • pressure-test: find fatal flaws and give a verdict.
  • problem-validation: test whether the pain is real and urgent.
  • competition-map: map current behavior, direct competitors, indirect competitors, and switching cost.
  • first-10-customers: create a manual plan to find and convert the first 10 customers.
  • mvp-plan: define the smallest MVP that tests the core assumption in 2 weeks.
  • full: run a compact version of all modes.

Read references/playbooks.md for the detailed prompt templates and criteria for each mode.

Default Output

Default to compact output. Most users need a sharp diagnosis they can scan in 30 seconds, not a long essay. Include the score table by default.

Use this shape:

markdown
**Verdict**
Strong / Weak / Pivot required

2-3 direct sentences.

**Scorecard**
| Area | Score | Read |
|---|---:|---|
| Pain intensity | 3/5 | ... |
| Buyer clarity | 2/5 | ... |
| Urgency | 3/5 | ... |
| Differentiation | 2/5 | ... |
| Speed to validate | 4/5 | ... |
| Founder advantage | 3/5 | ... |

**Core Assumption**
One sentence.

**Fatal Flaws**
| Risk | Severity | Why It Matters | Fast Test |
|---|---|---|---|
| ... | High | ... | ... |

**Problem Reality**
- Pain: ...
- Early adopter: ...
- Vitamin or painkiller: ...

**Competition**
- Current behavior: ...
- Real enemy: ...
- Differentiation needed: ...

**First 10 Customers**
1. ...
2. ...
3. ...

**MVP**
- Build:
- Cut:
- 2-week test:

Default limits:

  • Scorecard: always include 6 rows.
  • Verdict: max 3 sentences.
  • Fatal flaws: max 3 rows.
  • Problem reality: max 3 bullets.
  • Competition: max 3 bullets.
  • First 10 customers: max 3 actions.
  • MVP: max 3 bullets.
  • Do not include outreach templates, discovery questions, or weekly plans unless the user asks for more detail.

Rules

  • Be specific to the idea; never give generic startup advice.
  • Rank dangerous flaws first.
  • Identify the core assumption that must be true for the business to work.
  • Treat current behavior as competition.
  • Treat "we have no competition" as false by default.
  • Test real behavior, not compliments or hypothetical intent.
  • Discovery questions must ask about past behavior.
  • Prefer manual founder-led validation before automation.
  • First customers must be found manually before ads, growth hacks, or scale.
  • Cut features that do not test the riskiest assumption.
  • MVP must test the single riskiest assumption, not become a mini-product.
  • If the idea is weak, say so directly and explain the pivot path.
  • Do not invent fake market data. If market facts matter and are current/uncertain, browse or state what must be verified.

Scoring

Use scores only when useful:

AreaScore
Pain intensity1-5
Buyer clarity1-5
Urgency1-5
Differentiation1-5
Speed to validate1-5
Founder advantage1-5

Scores must be tied to evidence from the idea, not vibes.

Deep Mode

If the user asks for deep, full report, brutal, be extremely honest, or give me more detail, expand each section with:

  • assumptions to validate
  • disconfirming evidence to look for
  • customer discovery questions
  • outreach messages
  • 2-week milestones
  • pivot options

Still keep the writing direct and structured.

Resources

  • references/playbooks.md: Mode-specific checklists and output details.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Startup Pressure Test AI skill do?

Brutally evaluate and refine startup ideas with practical early-stage startup frameworks. Use when Codex is asked to pressure-test a startup idea, validate whether the problem is real, map competitors and current customer behavior, find the first 10 customers, define an MVP, create a 2-week launch plan, assess founder-market fit, or give a direct strong/weak/pivot verdict.

Why use Startup Pressure Test on TypingMind?

Because you install it once and use it with any model. Startup Pressure Test 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 Startup Pressure Test in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Kappaemme-git/codex-startup-pressure-test-skill/tree/main/startup-pressure-test. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Startup Pressure Test?

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 Startup Pressure Test?

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

Is the Startup Pressure Test AI skill free?

Yes. It is published on GitHub by Kappaemme-git under the MIT license. 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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