autoharness vs OpenSpace
Self-learning skill layer for Claude Code: distills skills from your real sessions, merges same-scenario ones, updates them in use and prunes the unused — touching only skills it wrote. — versus — HKUDS's skill lifecycle layer for agents: retrieve the right skill per task, evaluate which ones actually work from real outcomes, share across agents and teammates, evolve with every run.
Both run a skill lifecycle judged by real outcomes rather than benchmarks; openspace adds cross-agent sharing and retrieval, autoharness stays a local Claude Code plugin.
| autoharness | OpenSpace | |
|---|---|---|
| Stars | 11k | 7.7k |
| Forks | 608 | 922 |
| Language | Python | Python |
| License | MIT | MIT |
| Last activity | 2 days ago | 2 months ago |
| Topics | skills, coding | skills |
| Curated connections | 5 | 4 |
autoharness — the curator's take
Install it if you live in Claude Code and want lessons captured without curating skills by hand — zero config, zero deps, and it never touches skills you wrote or installed. Skip it off Claude Code, or if you need measured gains: skills survive on adherence, not a held-out score. Needs Python 3.11+ as `python3` on PATH, or its hooks stay off (macOS's stock /usr/bin/python3 is 3.9).
OpenSpace — the curator's take
The missing half of the skill ecosystem: everyone ships installers, almost nobody closes the loop on whether a skill WORKED. OpenSpace's bet is lifecycle — retrieval at task time, evaluation from real run outcomes, and evolution instead of repeat failures — across Claude Code, Codex, OpenClaw and friends. If your skill folder has become a junk drawer, this is the category to watch. NOT yet the standard: young v2, outcome-evaluation quality depends on how honestly your runs are scored, and the Feishu/WeChat-first community signals where the early adopters are. For plain install/audit needs, a simpler skill manager does less, more predictably.