Launchaudit logo

Launchaudit

Organization
buildfastwithai
launchaudit

Audit a startup, SaaS, app, developer tool, landing page, or product before launch from a live URL, localhost page, repository, screenshots, or supplied copy. Use when Codex needs to determine what a startup appears to do, test whether its public experience is launch-ready, inspect positioning, calls to action, product proof, trust, UX, accessibility, responsive behavior, broken paths, technical surface, or launch operations, produce a Ready to launch / Launch after critical fixes / Not ready yet verdict, prioritize exact improvements, or create a polished standalone HTML launch-readiness report.

Overview

Publisherbuildfastwithai
Repositorygen-ai-experiments
Skill namelaunchaudit
Stars
785
Forks
225
Bundled files
5
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.

  • 5 bundled files

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

  • Open source

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

Installation

Install the Launchaudit 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/buildfastwithai/gen-ai-experiments/tree/main/skills/launchaudit/launchaudit
  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/buildfastwithai/gen-ai-experiments.git /tmp/gen-ai-experiments
mkdir -p .claude/skills
cp -r /tmp/gen-ai-experiments/skills/launchaudit/launchaudit .claude/skills/launchaudit
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Launchaudit 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 Launchaudit 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 Launchaudit 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.

LaunchAudit

Audit the startup as a first-time visitor and launch reviewer. Infer the product before asking questions, inspect the public experience, distinguish evidence from inference, and deliver exact fixes in a standalone report.

Language

  • Match the user's language in conversation.
  • Write the report in the user's language unless they request another language.
  • Preserve product names, URLs, interface labels, and source quotations when accuracy requires it.

Read the references

Select the mode

  • standard — Inspect the primary public journey and create the full report. Use by default.
  • quick — Inspect the main page, primary CTA, and most important trust path. Return the verdict and top five fixes.
  • deep — Inspect relevant product, pricing, documentation, trust, install, and conversion paths on desktop and mobile.
  • technical — Emphasize broken paths, metadata, responsive behavior, accessibility signals, console errors, and visible performance risks.
  • before-after — Compare two versions with the same criteria and clearly attribute improvements or regressions.

Do not imply that a public-page audit can verify private analytics, billing, production monitoring, legal compliance, or authenticated product behavior.

Workflow

1. Inspect before asking

  • Open the supplied URL or artifact and determine what the startup appears to do, who it serves, the promised outcome, the mechanism, the primary action, and the likely launch stage.
  • Prefer the integrated browser for live sites and localhost apps. Inspect visible page state before interacting.
  • Follow relevant public navigation and CTA destinations. Do not submit forms, create accounts, begin trials, install software, make purchases, or transmit user data unless the user explicitly authorizes that action.
  • Use screenshots when visual hierarchy, responsive behavior, clipping, overlap, or interaction state matters.
  • Inspect a supplied repository only when it is in scope. Treat repository findings as product evidence, not proof that production uses the same version.
  • Ask only about missing information that would materially change the verdict. The default request should work with only a URL.

2. Reconstruct the startup

State:

  • what the product appears to be
  • primary audience
  • urgent problem or trigger
  • promised outcome
  • mechanism
  • primary CTA
  • current alternative

Label each important statement as Observed, Inferred, or Unknown. If the startup cannot be explained accurately after the first screen and one supporting section, treat that as a launch finding rather than asking the founder to explain it.

3. Trace the public journey

Follow the smallest realistic path from first visit to the primary conversion:

  1. Arrival
  2. Comprehension
  3. Evidence
  4. Risk reduction
  5. Action
  6. Confirmation or next-step expectation

Record the exact page, element, or interaction that supports each finding. Do not manufacture analytics or user behavior.

4. Evaluate readiness

Use the weighted system in references/evaluation-framework.md.

  • Score only what the inspected evidence supports.
  • Mark inaccessible or private criteria as unknown.
  • Report both the normalized readiness score and evidence coverage.
  • Identify critical blockers before calculating the verdict.
  • Never award “Ready to launch” when a primary conversion path is broken, the product cannot be understood, or evidence coverage is too low.

5. Prescribe fixes

For every material issue include:

  • diagnosis
  • observed evidence
  • user or business consequence
  • severity
  • effort
  • confidence
  • exact recommended change
  • validation method

Provide replacement copy for copy problems. Describe the target component and desired behavior for UI problems. Keep fixes within the inspected evidence; do not prescribe a complete rebrand or rebuild when a focused correction is enough.

Rank:

  1. Launch blockers
  2. High-impact fixes
  3. Quick wins
  4. Later improvements

6. Build the seven-day plan

Turn the recommendations into a realistic sequence:

  • Day 1: clarify and unblock
  • Days 2–3: repair the primary journey
  • Days 4–5: add proof and trust
  • Day 6: verify responsive and technical surfaces
  • Day 7: retest and launch

Adapt the plan when the product needs less or more work. Do not pretend that legal, security, or infrastructure risks can always be fixed in seven days.

7. Create the report

  • Serialize the full analysis to outputs/<startup-slug>-launchaudit.json.
  • Generate outputs/<startup-slug>-launchaudit.html with:
bash
node scripts/generate_report.mjs <input.json> <output.html>
  • Validate the JSON and generated report.
  • Inspect the report for missing sections, escaped text, score consistency, and readable colors.
  • Return clickable absolute links to both files.
  • Keep the JSON beside the HTML so a later audit can compare versions without starting over.

Verdict rules

  • Ready to launch — score at least 80, evidence coverage at least 70%, no critical blocker, and the primary path works.
  • Launch after critical fixes — score 55–79, or score 80+ with a material blocker that can be corrected without changing the core product.
  • Not ready yet — score below 55, the startup remains unclear, the primary path is broken, or the experience lacks enough product reality to support a responsible launch.

Use the more cautious verdict when score and blocker evidence disagree. Explain the override.

Quality bar

  • Make the report useful without founder narration.
  • Separate product quality from presentation quality.
  • Prefer concrete evidence over taste.
  • Do not invent customers, traction, analytics, performance measurements, security controls, legal compliance, or production behavior.
  • Do not penalize a startup for lacking enterprise features when its audience does not need them.
  • Treat accessibility, privacy, security, and legal observations as preliminary unless a qualified audit establishes them.
  • Include strengths worth preserving, not only problems.
  • End with the next retest and the smallest measurable launch experiment.

Default output

Deliver:

  1. Reconstructed startup
  2. Launch verdict, readiness score, and evidence coverage
  3. Weighted diagnostic scorecard
  4. Primary journey map
  5. Critical blockers
  6. Prioritized improvements with exact fixes
  7. Copy or interface corrections where useful
  8. Seven-day action plan
  9. Retest checklist and launch experiment
  10. Standalone HTML report and editable JSON source

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 Launchaudit AI skill do?

Audit a startup, SaaS, app, developer tool, landing page, or product before launch from a live URL, localhost page, repository, screenshots, or supplied copy. Use when Codex needs to determine what a startup appears to do, test whether its public experience is launch-ready, inspect positioning, calls to action, product proof, trust, UX, accessibility, responsive behavior, broken paths, technical surface, or launch operations, produce a Ready to launch / Launch after critical fixes / Not ready yet verdict, prioritize exact improvements, or create a polished standalone HTML launch-readiness r...

Why use Launchaudit on TypingMind?

Because you install it once and use it with any model. Launchaudit 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 Launchaudit in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/buildfastwithai/gen-ai-experiments/tree/main/skills/launchaudit/launchaudit. 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 Launchaudit?

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 Launchaudit?

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

Is the Launchaudit AI skill free?

Yes. It is published on GitHub by buildfastwithai 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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