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open-source-marketing-playbook

Marketing playbook for open-source projects led by non-technical founders. Covers README optimization, community building, contributor attraction, and translating OSS traction into revenue. By @WeiYipei.

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含まれるファイル(7)

  • SKILL.md4.8 KB
  • _meta.json149 B
  • .clawhub/origin.json162 B
  • references/en/README.md2.5 KB
  • references/iris-oss-field-notes.md1.2 KB
  • references/ja/README.md3.5 KB
  • references/ko/README.md3.0 KB

SKILL.md(原文)

インストールする前に、エージェントに与えられる指示の中身を確認できます。

Open Source Marketing for Non-Technical Founders

🌍 Language / 语言: 中文 | English | 日本語 | 한국어

📦 Install

clawhub install open-source-marketing-playbook

What you get after installing:

  • README-as-landing-page structure with badges and social proof
  • Community-first growth strategy (Discord/Slack without writing code)
  • Star momentum tactics and contributor funnel design

You don't need to be a developer to market an open-source project. This playbook bridges the gap and produces an executable launch packet.

  • README as landing page: Structure, badges, and social proof that convert visitors
  • Community-first growth: Discord/Slack strategy without writing code
  • Star momentum: How to trigger GitHub Trending without gaming the system
  • Contributor funnel: Attract and retain open-source contributors
  • Monetization bridge: From stars to sponsors to paid tiers

0. Open-source readiness gate

Before planning distribution, answer four questions:

  1. Is this for enterprises, developers, or both?
  2. Are API/SDK, self-hosting and documentation usable enough for a stranger to reach first value?
  3. Is the goal influence, commercial leads, recruiting, fundraising, or a defined mix?
  4. Can the team maintain issues, releases and community after launch—and capture qualified SMB/enterprise demand?

If any answer is unclear, return a readiness-gap table before proposing launch channels.

Score the result as Go, Fix, or No-Go for positioning, clean-machine quickstart, reproducible proof, license/security/privacy, launch-week ownership, and post-star conversion. A No-Go blocks concentrated distribution.

1. Required launch packet

Produce these assets together:

  • Positioning: [known closed-source comparator] alternative for [audience], differentiated by [verifiable benefit].
  • README: first-screen tagline, demo, working quick start, docs, deployment, community and contribution paths.
  • PR/article: comparative advantage, why now, proof, use cases and reusable excerpts for secondary distribution.
  • Developer docs: fastest path to first value, one-click deploy where possible, troubleshooting and next-step links.
  • Community operating map: Reddit for discovery, X for 1:1 feedback/news, Discord/Telegram for retention and contributor coordination.

2. Launch-week execution

  • Complete the concentrated announcement cycle within 24 hours, while preserving authentic channel-native posts.
  • Hold 1:1 conversations with early users and relevant community operators; do not mass-post identical copy.
  • Publish where the comparator/category is already discussed, including relevant news communities.
  • Track source → repo visit → star/install → activated user → qualified lead → paid.
  • Route non-code contributors to translation, documentation, community moderation and global operations instead of losing them.

Use one row per channel:

channel | ICP | channel-native angle | CTA | UTM/source | owner | local time + timezone |
success threshold | stop condition | evidence URL

Review at 2h (operations only), 24h (continue/change/pause), 72h (second wave), and 7d (reuse/budget). Mark unavailable native metrics as data unavailable.

Never buy stars or accounts, coordinate fake/incentivized votes, conceal sponsorships, bulk-scrape and spam users, or use AI to impersonate authentic participation. Re-check current community rules, creator metrics, rates and contacts before use.

3. Post-launch operating loop

exposure → early users → qualified customers → needs → product iteration → monetization

Run at least 30 early interviews before treating stars as PMF. Let the user speak most of the session; verify existing paid behavior and request a screen-share of the workflow. Start monetization experiments early and balance refactoring with user-visible improvements.

Full evidence map and output template: references/iris-oss-field-notes.md.

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