Agent skill evolution and sharing across heterogeneous LLMs, and the potential for emergent opportunistic behaviors within the evolution engine.

Hi Team,

Thank you for open-sourcing this exciting project! 🙌 Attracted by the self-evolving cat on the top of README.md that grew lobster claws 🦞🐈 That cute and shy cat 😎 has geek AI agent spirit! @chaohuang-ai

I had an unsolved issue at ClawWork previously github.com/HKUDS/ClawWork/is... seems like a better fit for OpenSpace! 🌟 I’ve tweaked my original questions a bit to match OpenSpace's context here. Looking forward to your report on this! 😺

1. When multiple Agents backed by different LLMs (e.g., Claude, GPT) share evolved Skills through OpenSpace's community, will their Skill libraries gradually converge toward a homogeneous "universal style" ? Or will each Agent's underlying model biases cause it to evolve and apply shared Skills in distinct ways?

当不同底层 LLM 的 Agent（如 Claude、GPT）通过 OpenSpace共享进化后的 Skill 时，各 Agent 的 Skill 库是否会逐渐收敛为同一套"通用风格"？还是说每个 Agent 由于底层模型的偏好差异，即便使用相同的 Skill，仍会在应用和进化路径上产生差异？

2. OpenSpace's evolution engine (AUTO-FIX / DERIVED / CAPTURED) updates Skills based on historical task performance. As Skills accumulate from Phase 1 → Phase 2 and cross-Agent sharing, could opportunistic behaviors arise? for example, an AI agent learning to deliberately trigger execution failures to force Skill regeneration? or systematically over-generalizing high-scoring Skills to unrelated task types to minimize token cost?

OpenSpace 的进化引擎（AUTO-FIX / DERIVED / CAPTURED）基于历史任务表现持续更新 Skill。随着 Phase 1→Phase 2 的 Skill 积累以及跨 Agent 共享，是否可能出现涌现式的投机行为？例如 Agent 学...