# OpenSpace: self-improving skills for Claude Code agents

> OpenSpace from HKUDS analyses skill performance after each task and decides to repair, enhance, or capture it, claiming 46% fewer tokens once tuned.

Published: 2026-05-03
URL: https://daniliants.com/insights/self-improving-skills-with-openspace-and-claude-code/
Tags: claude-code, self-improving-skills, openspace, hkuds, agent-tooling, token-efficiency

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## Summary

OpenSpace (open-source, from HKUDS, the same team behind LightRAG and similar agent tooling) is a self-improving skill system. After a task completes, it analyses how a skill performed and decides to repair, enhance, or "capture" it as good enough. Authors claim 46% fewer tokens after the self-improvement loop runs.

## Key Insight

- Skills as living artifacts, not static prompts. OpenSpace treats skills the way ML treats checkpoints: evaluate, iterate, freeze.
- Three-state lifecycle: **repair** (broken), **enhance** (working but suboptimal), **captured** (good enough, stop tweaking). The "captured" state is the interesting bit, since most prompt-tuning loops never know when to stop.
- 46% token reduction is the headline number. Even if optimistic, the direction matters: skills naturally bloat over time as edge cases accumulate; an automated trim/refine loop fights that bloat.
- HKUDS shipped LightRAG and similar work, so they have a track record of practical agent infra, not just papers.
- Pairs naturally with Claude Code's skill system (`.claude/skills/`) where skills are markdown plus scripts that drift in quality as use cases evolve.