I reverse-engineered the workflow that made Manus worth $2B and turned it into a Claude Code skill

Meta just acquired Manus for $2 billion. I dug into how their agent actually works and open-sourced the core pattern. The problem with AI agents: after many tool calls, they lose track of goals. Context gets bloated. Errors get buried. Tasks drift. Manus's fix is stupidly simple — 3 markdown files: * `task_plan.md` → track progress with checkboxes * [`notes.md`](http://notes.md) → store research (not stuff context) * [`deliverable.md`](http://deliverable.md) → final output The agent reads the plan before every decision. Goals stay in the attention window. That's it. I packaged this into a **Claude Code skill**. Works with the CLI. Install in 10 seconds: `cd ~/.claude/skills` `git clone` [`https://github.com/OthmanAdi/planning-with-files.git`](https://github.com/OthmanAdi/planning-with-files.git) MIT licensed. First skill to implement this specific pattern. https://preview.redd.it/dkvk3d0uc3bg1.png?width=1329&format=png&auto=webp&s=bd3320c687aec02b3a7fa92fc3272485838a36f5 Curious what you think — anyone else experimenting with context engineering for agents?