#skillclaw
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abvx.xyz
The most interesting idea in SkillClaw: stop treating agent skills as static prompts.

Treat them as living software that evolves from collective experience.

If one agent learns today, the entire ecosystem improves tomorrow.

bit.ly/4nW0abj

#AI #AgenticAI #MachineLearning
SkillClaw: The End of Static Prompts?
What if every mistake made by one AI agent automatically made every other agent smarter tomorrow?
bit.ly
May 29, 2026 at 2:10 PM
thearcadeathlete.bsky.social
Can You Even Grab These Tiny Pac-Man Ghosts?! 👻🕹️ These Pac-Man characters are so tiny the claw can barely grab them! 👻 I had to give it a shot anyway! think I pulled it off? 😉😉
#thearcadeathlete #arcadewin #clawmachine #pacman #arcadefun #arcadegames #skillclaw #minitoys #retrogaming #arcadevibes
December 10, 2025 at 10:02 PM
githubprojects.bsky.social
SkillClaw evolves AI agent skills collectively from real conversations, compounding experience across sessions, agents, and users.

- Shell installer for macOS/Linux, Python install for Windows
- Works with Hermes, OpenClaw, Claude Code, and OpenAI-compatible APIs
June 7, 2026 at 2:30 AM
paper.bsky.social
Links: abs, pdf
Search: Bluesky, Twitter, Reddit, Hacker News, Hugging Face, alphaXiv
SkillClaw: Let Skills Evolve Collectively with Agentic Evolver
Large language model (LLM) agents such as OpenClaw rely on reusable skills to perform complex tasks, yet these skills remain largely static after deployment. As a result, similar workflows, tool usage...
arxiv.org
April 19, 2026 at 12:04 AM
kuin20260207.bsky.social
マルチユーザーLLMエージェント開発者向けに、集合的スキル進化フレームワークSkillClawが登場しました。これは、ユーザー間の相互作用の経験を収集し、スキルを自律的に洗練・拡張・同期させる仕組みです。これにより、ローカルLLMエージェントは静的なスキルから脱却し、真の集合知へと進化する未来が期待できます。この集合的学習が、次世代のローカルAIの鍵となるでしょう。#LLMEージェント #集合知
April 13, 2026 at 6:05 AM
paper.bsky.social
[13/30] 383 Likes, 6 Comments, 2 Posts
2604.08377, cs․AI | cs․CL, 09 Apr 2026

🆕SkillClaw: Let Skills Evolve Collectively with Agentic Evolver

Ziyu Ma, Shidong Yang, Yuxiang Ji, Xucong Wang, Yong Wang, Yiming Hu, Tongwen Huang, Xiangxiang Chu
Large language model (LLM) agents such as OpenClaw rely on reusable skills to perform complex tasks, yet these skills remain largely static after deployment. As a result, similar workflows, tool usage patterns, and failure modes are repeatedly rediscovered across users, preventing the system from improving with experience. While interactions from different users provide complementary signals about when a skill works or fails, existing systems lack a mechanism to convert such heterogeneous experiences into reliable skill updates. To address these issues, we present SkillClaw, a framework for collective skill evolution in multi-user agent ecosystems, which treats cross-user and over-time interactions as the primary signal for improving skills. SkillClaw continuously aggregates trajectories generated during use and processes them with an autonomous evolver, which identifies recurring behavioral patterns and translates them into updates to the skill set by refining existing skills or extending them with new capabilities. The resulting skills are maintained in a shared repository and synchronized across users, allowing improvements discovered in one context to propagate system-wide while requiring no additional effort from users. By integrating multi-user experience into ongoing skill updates, SkillClaw enables cross-user knowledge transfer and cumulative capability improvement, and experiments on WildClawBench show that limited interaction and feedback, it significantly improves the performance of Qwen3-Max in real-world agent scenarios.
April 19, 2026 at 12:04 AM
ai-firehose.column.social
SkillClaw enhances LLM agent performance by enabling collective skill evolution through cross-user experiences. This innovative framework allows knowledge to accumulate and propagate among users, converting static skill libraries into dynamic, adaptive ecosystems. https://arxiv.org/abs/2604.08377
SkillClaw: Let Skills Evolve Collectively with Agentic Evolver
ArXiv link for SkillClaw: Let Skills Evolve Collectively with Agentic Evolver
arxiv.org
August 12, 2026 at 5:00 AM
kuin20260207.bsky.social
LLMエージェント開発者向けに、ユーザーの相互作用からスキルを自動進化させるフレームワークSkillClawが登場しました。これは、ユーザーの利用履歴を集約し、エージェントのスキルを集合的に改善する仕組みです。この技術により、ローカル環境で動くエージェントの性能がユーザーの経験を通じて累積的に向上し、より実用的な能力を獲得できるようになります。ユーザー主導の進化が加速し、エージェントのポテンシャルが大きく開花する。#LLMEvolution #AgenticAI
April 14, 2026 at 5:00 PM
paper.bsky.social
(2/2) 105 Likes, 0 Comments, 09 Apr 2026, alphaXiv
SkillClaw: Let Skills Evolve Collectively with Agentic Evolver
View 1 comment: The core idea and the dual-session design are both solid. Two problems remain: heterogeneous user needs get forced into a single converging skill with no fork or override mechanism; an...
www.alphaxiv.org
April 19, 2026 at 12:04 AM
paper.bsky.social
(1/2) 278 Likes, 6 Comments, 10 Apr 2026, Hugging Face
Paper page - SkillClaw: Let Skills Evolve Collectively with Agentic Evolver
Join the discussion on this paper page
huggingface.co
April 19, 2026 at 12:04 AM
upppp-magazine.bsky.social
SkillClawでAIエージェントのスキルが自動進化する仕組みと導入方法

#upppp
https://upppp.jp/open-source/20260502-200528/
upppp.jp
May 2, 2026 at 11:05 AM
tom-doerr.bsky.social
https://github.com/AMAP-ML/SkillClaw
April 21, 2026 at 11:20 AM
nanoatp.bsky.social
arXiv Upvote Trends Top 5
1. Adam's Law: Textual F... (501 pts)
2. SkillClaw: Let Skills... (426 pts)
3. Recursive Multi-Agent... (397 pts)
4. Rethinking Generaliza... (368 pts)
5. LLaDA2.0-Uni: Unifyin... (327 pts)
Top paper: https://arxiv.org/abs/2604.02176
Generated 2026-05-06 11:13 UTC
May 6, 2026 at 11:13 AM
mengli512.bsky.social
SkillClaw lets agents learn from every interaction and share skills across devices and teams—finally fixing wasted trial and error. Have you tried it yet?
#SkillClaw #skills
open.substack.com/pub/pythonli...
After OpenClaw, This Collective-Evolution AI Project Is on Fire!
SkillClaw lets AI agents automatically learn, share, and evolve skills across teams. Zero extra effort.
open.substack.com
May 8, 2026 at 7:51 AM
techlatest.bsky.social
① Install Hermes CLI / Gateway
② Stack Skills & Plugins (procedural memory)
③ Add Tools & Utilities (GUIs, SkillClaw, ops)
④ Wire Integrations (MCP, memory bridges)
⑤ Enable Multi-Agent orchestration
June 8, 2026 at 2:48 PM
stephenturner.us
SkillClaw: Let Skills Evolve Collectively with Agentic Evolver arxiv.org/abs/2604.08377
April 16, 2026 at 12:59 PM
githubprojects.bsky.social
Explore it here:
osp.fyi/skillclaw
SkillClaw evolves agent skills collectively from real conversations
Discover SkillClaw on Open Source Projects
osp.fyi
June 7, 2026 at 2:30 AM
onedigital.bsky.social
SkillClaw para equipos técnicos: cómo hacer que tus agentes de IA aprendan juntos de forma controlada

SkillClaw permite que los resultados obtenidos en tareas reales se conviertan en mejoras concretas en el comportamiento de los agentes con el paso del tiempo. Su enfoque se basa en registrar…
SkillClaw para equipos técnicos: cómo hacer que tus agentes de IA aprendan juntos de forma controlada
SkillClaw permite que los resultados obtenidos en tareas reales se conviertan en mejoras concretas en el comportamiento de los agentes con el paso del tiempo. Su enfoque se basa en registrar interacciones, proponer cambios a las habilidades existentes y aceptar solo aquellas modificaciones que demuestran una ventaja clara frente a la versión previa. De este modo, un equipo puede consolidar buenas prácticas sin depender exclusivamente de ajustes manuales de indicaciones.
onedigital.mx
June 9, 2026 at 2:49 PM
vincentquezada.bsky.social
SkillClaw para equipos técnicos: cómo hacer que tus agentes de IA aprendan juntos de forma controlada

SkillClaw permite que los resultados obtenidos en tareas reales se conviertan en mejoras concretas en el comportamiento de los agentes con el paso del tiempo. Su enfoque se basa en registrar…
SkillClaw para equipos técnicos: cómo hacer que tus agentes de IA aprendan juntos de forma controlada
SkillClaw permite que los resultados obtenidos en tareas reales se conviertan en mejoras concretas en el comportamiento de los agentes con el paso del tiempo. Su enfoque se basa en registrar interacciones, proponer cambios a las habilidades existentes y aceptar solo aquellas modificaciones que demuestran una ventaja clara frente a la versión previa. De este modo, un equipo puede consolidar buenas prácticas sin depender exclusivamente de ajustes manuales de indicaciones.
onedigital.mx
June 9, 2026 at 2:49 PM