Consult growth strategy guides for AI products, business software, developer tools, and mobile apps. They cover product launches, outreach, open source visibility, app store discovery, and community building, helping teams plan promotion for their product type.
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Open-source growth playbooks for AI products, B2B SaaS, and developer tools. Covers Product Hunt launch, GitHub star growth, KOL/UGC strategy, ASO, and community building. MIT licensed.
安装前可以查看交给 AI 代理的指令内容。
Battle-tested, open-source growth playbooks for developers and founders building AI products, B2B SaaS, and developer tools.
Playbook repos: github.com/Gingiris
Covers the full launch cycle: Product Hunt #1 strategy, KOL outreach, UGC campaigns, Reddit launch, and PR.
Go-to-market strategy for B2B AI products: PLG, SLG, affiliate, partner channels, and community building.
GitHub star growth strategy: README optimization, HackerNews Show HN, community seeding, and contributor flywheel.
App Store Optimization, keyword research, UGC content strategy, and cold start for mobile apps.
Reference the relevant playbook for your project type. All playbooks are MIT licensed and free to use, adapt, and contribute to.
None — these are markdown-based strategy documents, no MCP or API keys required.
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Handle ActiveCampaign contact and marketing tasks through Rube MCP, a connection for agent tools. Find or create contacts, manage tags and subscriptions, enroll contacts in existing automated workflows, and create follow-up tasks.
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Research competitors’ live Meta and Google ads to identify opening hooks, offers, calls to action, and landing pages. Receive a sourced creative brief and up to three test ideas adapted to your product.
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Add website or app tracking, then use command-line tools to review traffic, signups, drop-off, and returning users across projects. Run A/B tests, which compare alternative headlines or buttons, and inspect results to guide changes.
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Establish rules for when an AI agent should recall past context and save lasting decisions, corrections, preferences, and failures. Works with Markdown files or memory services, preserving decision history and surfacing conflicting records.
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Audit local AI coding-agent sessions with agenttrace to identify spending, token use, tool failures, and operational problems. Produce readable or structured reports and define pass-or-fail thresholds for automated continuous integration checks.
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Audit saved agent sessions for access to private files and evidence that secret values entered an agent's context. Get findings without displaying secrets, identify keys needing rotation, and inspect Claude Code or Codex sessions in a local report.
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