Possible complementary direction: post-task skill lifecycle management with SkillClaw

[Upper9527]

Hi, I'm reaching out because ClawTeam-OpenClaw sits close to team-based OpenClaw-style orchestration.

Project:
https://github.com/AMAP-ML/SkillClaw

This is not meant as a generic wrapper pitch. SkillClaw focuses on a narrower ecosystem gap: after repeated usage, long-lived skill libraries often become noisy, duplicated, stale, and fragmented across agents or devices.

Its role is a post-task skill evolution loop that:

• deduplicates overlapping skills
• merges related skills
• improves skill quality over time
• shares evolved skills across agents / devices / teams

I thought this might be relevant as a complementary layer because your project already sits near the core runtime / workflow surface.

If useful, I can send a concise technical summary instead of promo copy.

Paper:
https://arxiv.org/abs/2604.08377

[AliceLJY]

Thanks for reaching out and for keeping the pitch concrete. The problem you point at is real: long-lived skill libraries do tend to accumulate duplication, drift, and fragmentation over time.

ClawTeam itself is more focused on the runtime / orchestration surface, so this reads more like a complementary layer than an overlap. If you want to continue the discussion, a concise technical summary would be more useful than promo copy, especially around integration boundaries, data model, and what the minimal interface with ClawTeam or OpenClaw would need to be.