Have you ever tried to manage a complex project through an AI agent conversation? It goes something like this: you ask the agent to build something, it starts working, then you switch contexts, come back, and the agent has no idea what was happening before.
I ran into this problem constantly. So I built Project Overseer — a file-driven project management system that works across OpenClaw, Claude Code, Codex CLI, Cline, and Gemini CLI.
AI agents have no persistent memory between sessions. They forget context the moment you switch tasks. Traditional project management tools (Jira, Notion, Linear) are designed for humans, not agents.
Instead of relying on the agent's conversation memory, Project Overseer stores every piece of project state in plain text files:
projects/my_project/
├── plan.md # Full WBS breakdown
├── status.json # Current state, risks, dependencies
└── tasks/
├── design.md # One file per sub-task
├── backend.md
└── frontend.md
Each sub-task is a separate file. The agent only loads what it needs. When switching tasks, it saves current progress, clears context, and loads the next task file.
When a sub-task completes, the agent automatically extracts 6 categories of cross-task impact and propagates them to all dependent tasks as pre-start notes. This prevents naming conflicts and design inconsistencies across your entire project.
No API keys. No external services. No database. Just drop the SKILL.md file into your project.
OpenClaw, Claude Code, Codex CLI, Cline / RooCode, Gemini CLI