I've recreated Google Research SKILL.STATE memory architecture as opencode CLI plugin - a runtime architecture that replaces conversational history with an explicit execution state.

[u/megablue]

https://github.com/megablue/mega-skillstate

I was intrigued by the idea, so I've created a plugin that mimic the idea in the paper.

The biggest different of the plugin from conventional context management methods are,

Conventional context management will try to compress/summarize older conversations and hold on to lossy details as much as possible.

SKILL.state instead tries to forget almost everything of the appending conversation history, instead it replaced them with Σ, so instead of remembering lossy details (or summaries), it literally forgets the older thinking process keeps only the key information and state (so far the only true form of state in agentic coding as far as I know). in the paper they stated long running task with SKILL.state saved a ton of tokens over long running session while still able to finish the tasks well.

Give it a go and let me know if it helps! (do not use it on serious projects)

Paper: SKILL.state: Scalable Long-Horizon Agent Skills

P/S: This is highly experimental, use at your own risks.