Github Stars Playbook logo

Github Stars Playbook

Community
Gingiris-1031
github-stars-playbook

Design and execute a 14-day GitHub repository growth sprint using README conversion, competitor-window research, Reddit, Hacker News, launch directories, community activation, and contributor loops. Use when launching an open-source repository, diagnosing weak star conversion, or building sustained GitHub discovery without buying stars.

Overview

PublisherGingiris-1031
Repositorygingiris-skills
Skill namegithub-stars-playbook
Stars
88
Forks
6
Bundled files
1
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.

  • 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 Gingiris-1031 on GitHub. Read the source before you install it.

Installation

Install the Github Stars Playbook 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/Gingiris-1031/gingiris-skills/tree/main/skills/github-stars-playbook
  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/Gingiris-1031/gingiris-skills.git /tmp/gingiris-skills
mkdir -p .claude/skills
cp -r /tmp/gingiris-skills/skills/github-stars-playbook .claude/skills/github-stars-playbook
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Github Stars Playbook 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 Github Stars Playbook 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 Github Stars Playbook 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.

GitHub Stars — 14-Day Evidence-Driven Sprint

Use stars as a proxy for qualified developer interest, never as the final business outcome. Track installation, activation, repeat use and contribution beside stars.

Intake gate

Collect before planning:

  • repository URL, license and target developer;
  • working quickstart verified on a clean machine;
  • one reproducible demo and one differentiator;
  • current views, unique cloners, stars, issues, contributors and activation event;
  • three direct competitors and their latest launch/change dates;
  • available founder, engineering and community capacity.

Do not launch if a new developer cannot understand the value and complete the quickstart in 10 minutes.

Measurement contract

Create one row per channel:

text
date | source | post URL | repo views | stars | clones | installs | activated | retained D7 | contributors

Report view → star, star → install, install → activation, and activation → D7 retained. Do not attribute organic GitHub traffic to a campaign without source evidence.

Use 2h only to catch broken links and unanswered discussion. At 24h decide continue/change/pause, at 72h select the second wave, and at 7d decide which content and channels deserve reuse. Mark unavailable native metrics as data unavailable rather than estimating them from impressions.

Keep a dated evidence label on benchmarks and cases: verified, self-reported, inferred, or unverified. Re-check platform rules and community policies during the launch week.

Competitor-window scan

Before selecting the launch date:

  1. Review the three competitors' release notes, X, Reddit, Hacker News, Product Hunt and GitHub activity from the last 30 days.
  2. Record their positioning, strongest proof asset, community response and unresolved complaints.
  3. Avoid launching into a dominant competitor announcement unless the product is a credible alternative to that exact news.
  4. Use a quiet window or a category event where the repository adds a distinct point of view.

Historical Gingiris OSS launch evidence shows that timing against the competitor/news window materially changes distribution; it is not enough to post everywhere on a fixed calendar.

README conversion surface

The first screen must contain:

  • one-line outcome for a named developer;
  • reproducible demo or result;
  • three differentiators at most;
  • quickstart that works when copied;
  • trust signals: license, security/privacy posture, maintainers and community link.

Ask five unfamiliar developers to explain the product and run the quickstart. Fix repeated confusion before distribution.

14-day sprint

Days 1–3 — proof and instrumentation

  • Validate the quickstart on macOS/Linux and the supported runtime.
  • Produce one 30–60 second demo, architecture diagram and comparison table.
  • Add source-tagged links and establish the baseline dashboard.
  • Prepare issue templates, contribution guide and response ownership.

Days 4–6 — community fit

  • Identify two relevant subreddits, one Hacker News angle and five specialist communities.
  • Participate with useful answers before sharing the repository.
  • Draft channel-native posts: problem/lesson for Reddit, technical novelty for Show HN, demo/proof for X.
  • Ask existing users for honest feedback, never coordinated stars.
  • Disclose affiliation and paid relationships. Do not buy accounts, automate fake participation, bulk-message scraped users, or coordinate/incentivize votes.

Day 7 — release

  • Publish the release, README and demo together.
  • Submit one Show HN post only when there is genuine technical novelty and the maintainers can answer questions live.
  • Publish to relevant Reddit communities only where rules permit; disclose affiliation.
  • Respond to technical objections with evidence and open issues for valid gaps.

Days 8–10 — second wave

  • Turn repeated questions into documentation and comparison pages.
  • Contact maintainers, newsletters and creators whose audience already uses the category.
  • Translate the winning technical explanation for one relevant regional community.
  • Publish a build log or benchmark with reproducible methodology.

Days 11–14 — compounding loop

  • Invite activated users—not all stargazers—to interviews.
  • Convert recurring requests into good first issue tasks.
  • Recognize contributors and document the next release milestone.
  • Keep only channels that generated activated or retained users.

Stop conditions and recovery

  • Views rise but activation does not: stop distribution and fix quickstart/product.
  • Stars rise but installs do not: rewrite positioning and demo.
  • Reddit post is removed: do not repost around moderation; review rules and contribute normally.
  • Show HN is flat after the initial window: do not manufacture engagement; reuse the technical asset elsewhere.
  • Security or data-handling issue appears: pause promotion, publish the fix and incident scope.

Compliance

Never buy stars, exchange stars, automate starring, conceal affiliation, use sockpuppets or ask communities to manipulate GitHub Trending. Do not publish unverifiable benchmarks.

Required output

Return:

  1. readiness verdict and blockers;
  2. competitor-window table;
  3. README change list;
  4. 14-day channel calendar with owner and UTM/source;
  5. measurement dashboard schema;
  6. stop conditions and next experiment.

Related full playbook: gingiris-opensource.

加入出海社群与购买资料包 · Community & Resources

  • 加入出海社群: 微信添加 staywildgrow,留言「加入社群」即可。

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  • Join the global growth community: Add staywildgrow on WeChat and send “加入社群” (join the community).

  • Get the resource pack: Add staywildgrow on WeChat to purchase the Gingiris global growth resource pack for CNY 599.

社群与资料包为可选入口,不影响本开源 Skill 的安装和使用。

The community and paid resource pack are optional; neither is required to install or use this open-source skill.

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 Github Stars Playbook AI skill do?

Design and execute a 14-day GitHub repository growth sprint using README conversion, competitor-window research, Reddit, Hacker News, launch directories, community activation, and contributor loops. Use when launching an open-source repository, diagnosing weak star conversion, or building sustained GitHub discovery without buying stars.

Why use Github Stars Playbook on TypingMind?

Because you install it once and use it with any model. Github Stars Playbook 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 Github Stars Playbook in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Gingiris-1031/gingiris-skills/tree/main/skills/github-stars-playbook. 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 Github Stars Playbook?

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 Github Stars Playbook?

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

Is the Github Stars Playbook AI skill free?

Yes. It is published on GitHub by Gingiris-1031 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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