15:I[981,["658","static/chunks/658-a698693f56d0116a.js","183","static/chunks/183-6beeeb801c274aaa.js","474","static/chunks/app/%5Btype%5D/layout-e8e83705b5c8d3b7.js"],"Posts"] 16:T6ba,Hi HN, I'm Hiroki, founder of Epismo. Just released the Epismo CLI (https://npmjs.com/package/epismo). 380+ downloads right after launch. Thank you.

The problem: I got a great result in a Claude Code thread. A week later I couldn't reproduce how I got there. The real workflow lived across chat histories, tabs, tool settings, and tiny followup prompts. Prompts copy easily, but multi-step processes don't.

If GitHub made code reusable and Hugging Face made models reusable, we want to make human-AI workflows reusable. And there's a second problem emerging. Claude Code now orchestrates other agents like Codex. Ticket-based task management is becoming standard in agent orchestration. But there's no good way to share and manage the full process across agents working in parallel.

Epismo is a platform where agents and humans collaborate. You can save and share the full process of how you got your outcomes. The users aren't just humans. Agents can share their best practices too, and others can import and run them. Our core insight: the best format for agent workflows is a chain of markdown data. Once you import a workflow, your agent runs each step as a task. It turns scattered know-how into structured, repeatable execution.

The CLI is agent-native: - npm install -g epismo - epismo login --email you@example.com (no browser, OTP from terminal) - Works with Claude Code, Codex, and any agent with terminal access

Skills repo (MIT): https://github.com/epismoai/skills17:T608,I've been reflecting on how LLMs are changing our learning habits as engineers, and realized something worrying.

AI can now qui

Show_HN: ClawHub Skills Benchmarking – Find Bugs, Drift, and Slowdowns