Build In Public Launcher

Community

FAIRY123456789

build-in-public-launcher

Turn a finished or nearly finished Skill, plugin, repository, micro-tool, research utility, or AI project into a credible public launch that can earn real users and feedback. Use when the work exists but nobody outside the creator has tried it, the README is weak, the launch has no demo or proof metric, or the user wants a GitHub-first build-in-public plan without fake traction or vanity metrics.

Overview

Publisher	FAIRY123456789
Repository	`human-edge-agent-skills`
Skill name	`build-in-public-launcher`
Stars	108
Forks	0
Bundled files	1
License	MIT
Links	Skill source Repository

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.
1 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by FAIRY123456789 on GitHub. Read the source before you install it.

Installation

Install the Build In Public Launcher AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/FAIRY123456789/human-edge-agent-skills/tree/main/plugins/build-in-public-launcher/skills/build-in-public-launcher
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/FAIRY123456789/human-edge-agent-skills/tree/main/plugins/build-in-public-launcher/skills/build-in-public-launcher

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

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/FAIRY123456789/human-edge-agent-skills.git /tmp/human-edge-agent-skills
mkdir -p .claude/skills
cp -r /tmp/human-edge-agent-skills/plugins/build-in-public-launcher/skills/build-in-public-launcher .claude/skills/build-in-public-launcher

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Build In Public Launcher 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 Build In Public Launcher 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 Build In Public Launcher 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.

Build in Public Launcher

Purpose

Launch for evidence, not applause. This Skill turns a finished artifact into a bounded release and feedback loop without inventing adoption.

Instructions

Define the exact user and painful moment the artifact addresses.
Audit whether the artifact is actually ready for a stranger: installation, first-run path, example input, example output, license, privacy and security notes, and failure handling.
Write a one-line value proposition that names the job, not the technology stack.
Build a launch pack using references/launch-pack.md.
Create one comparison or task-completion demo. Do not cherry-pick an impossible-to-reproduce showcase.
Choose one primary proof metric and one secondary distribution metric.
Recruit the first testers from existing communities, peers, contributors, or target-user groups. Prefer 10 relevant people over 1,000 generic impressions.
Ask testers to perform a task before explaining the solution. Capture confusion and failure.
Publish a changelog entry after each evidence-driven revision.
Promote measured adoption only after it exists. Never invent stars, users, installs, revenue, benchmarks, or testimonials.

Default launch horizon

Use a 7-day launch loop:

Day 1: packaging and smoke test
Day 2: private testers
Day 3–4: fix onboarding and failure cases
Day 5: public release
Day 6–7: collect issues, repeat use, and outcome evidence

Output

Return a release-ready checklist, README hero copy, demo plan, tester plan, proof metrics, and 7-day launch schedule.

Examples

Input: a working repository with no external users and a confusing README.
Output: a reproducible demo, a first-tester plan, one utility metric, and explicit stop or iterate criteria.

Limitations

Do not claim market demand, compatibility, or traction that has not been observed. Publishing, messaging testers, and changing external accounts require the user's explicit authorization.

Troubleshooting

If no credible metric exists, choose successful task completion or tester confusion as the first measurement. If strangers cannot install the artifact, return to packaging before writing launch copy.

Bundled files

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

references/launch-pack.md1 KB

Frequently asked questions

What does the Build In Public Launcher AI skill do?

Why use Build In Public Launcher on TypingMind?

Because you install it once and use it with any model. Build In Public Launcher 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 Build In Public Launcher in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/FAIRY123456789/human-edge-agent-skills/tree/main/plugins/build-in-public-launcher/skills/build-in-public-launcher. 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 Build In Public Launcher?

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 Build In Public Launcher?

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

Is the Build In Public Launcher AI skill free?

Yes. It is published on GitHub by FAIRY123456789 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Build in Public Launcher

Purpose

Instructions

Default launch horizon

Output

Examples

Limitations

Troubleshooting

Bundled files

Frequently asked questions

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