Synaplan
開源 AI 平台,供企業使用:自行託管的 AI 代理、符合 GDPR 的聊天機器人小工具、RAG 知識庫、WhatsApp 與 Outlook AI、MCP、多模型路由。Docker、Kubernetes 或託管於 Germ
AI 翻譯
尚未偵測到更新。GitHub 發布版本後會顯示在這裡。
個人 GitHub 帳號下公開儲存庫的擁有者可以登入並驗證所有權,自行管理介紹,無需管理員核准。
登入並產生驗證檔案,在 30 分鐘內將其提交至此儲存庫的預設分支。其他分支或分叉儲存庫中的檔案無效。然後在下方檢查檔案,即可無需管理員核准直接管理專案。請保留檔案以維持管理權限。
如需登入,將前往登入頁面。登入後請回到此處,再次點擊所選的驗證按鈕。
在專案資料夾中執行 /nomorevibe verify,即可驗證所有權並自行管理介紹。
一分鐘完成驗證Agent skill for clearer landing pages, conversion and SEO audits, and verified launches.
翻譯準備中
取自儲存庫 README
正在載入完整 README。
檢視完整 README ↗Launch Check is a set of instructions for your coding agent, not a hosted audit service or a one-command CLI. It uses the repository, browser, and connected accounts you give the agent. It separates what it checked from what it could not verify.
Paste this into an agent chat after installation:
$launch-check audit https://whoownswhat.xyz/ for conversion and SEO.
Tell me what is clear, what is confusing, and which fixes matter first.
Check the visible CTAs, mobile layout, technical SEO, sitemap, and footer.
Show evidence and mark anything you could not verify. Do not change the site.
You can also ask it to build, research, implement, or launch. For a full release, name the repository, hosting project, and domain, and explicitly ask it to publish. See the complete usage guide for copyable prompts for every mode.
Copy the whole skills/launch-check folder, including its references and assets, into your agent's skills directory:
Codex: ~/.agents/skills/launch-check/
Claude Code: ~/.claude/skills/launch-check/
Use your agent's skill selection syntax, such as $launch-check in Codex or /launch-check in Claude Code. Then paste one of the prompts above. The skill has no mandatory paid service or runtime dependency. Browser, repository, Search Console, and analytics access are needed only for checks or changes that use them.
Already installed? Say “Update launch-check from GitHub to the newest version” in your coding agent, or run the included updater. It checks the main branch, backs up older installed copies, and updates on --apply.
These are projects from avo42. They show different kinds of pages and products the skill has helped build or check.
| Project | What it does |
|---|---|
| WhoOwnsWhat | Shared ledger for co-founders to split project costs, track asset ownership, and hand things over cleanly. My latest project, still in development. |
| MakeSquishy | Free tool for kids to design, print, and fold paper squishies. |
| PadelTune | Personal padel training plans for strength, mobility, and match prep. |
| ReadNext | Helps pick one book to read next from an overflowing want-to-read shelf. |
| PlateWeek | Turns a food video into recipes and a practical shopping list. |
| Armando | Client management playbook and follow-through for agencies and consultancies. |
| AI Shift Field Guide | Plain-language guide to AI shifts in software, with a checklist for testing product ideas. |
| AI Markets, explained | A practical reading of AI spending, adoption, and where new products may fit. |
The default research path uses accessible, attributable sources. The agent checks intent and the actual search results, then turns promising topics into page briefs. Numeric volume or difficulty appears only when a connected source supplies it. DataForSEO can add live keyword and SERP data, but it is optional and may have its own cost; the skill does not pretend that it is connected by default.
A brief now separates why someone searched from what the leading pages let them do. The agent opens leading organic results, records each URL and rank, and reports the confirmed format mix alongside volume and organic keyword difficulty when those metrics are available. It then recommends a guide, comparison, calculator, tool, or no page based on the evidence and what the product can actually deliver. For example, a query about splitting startup costs might need an explanation and a calculator. If pages cannot be inspected, their formats stay unverified and the recommendation is a hypothesis.
Mock layout only: the pictured formats were invented to show the report shape. A real run must inspect and cite the ranked pages.
For search discovery, the launch workflow checks canonicals, robots.txt, generated sitemaps, and Google/Bing submission when the right accounts are available. IndexNow can notify participating engines about page changes. None of these steps guarantees indexing or ranking.
I have been iterating on the public repo since late September 2026. Suggestions and real-world edge cases are welcome in GitHub Issues.