
metaskill
Teach your AI agent how to actually learn, not just log
What it does
Most AI agents log mistakes. They don't learn from them. metaskill fixes this — forces 3-level correction per error (surface → principle → habit), scans past patterns before tasks, and captures wins not just failures. Built after OpenClaw scored 5/100 on its own learning eval. 37 corrections logged. Same errors repeating. clawhub install metaskill — zero deps, works on any OpenClaw instance.
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- MAMeta-agent: self-improving agent harnesses from live tracesApr 2026 · github.com · ▲14
We built meta-agent: an open-source library that automatically and continuously improves agent harnesses from production traces. Point it at an existing agent, a stream of unlabeled production traces, and a small labeled holdout set. An LLM judge scores unlabeled production traces as they stream. A proposer reads failed traces and writes one targeted harness update at a time, such as changes to prompts, hooks, tools, or subagents. The update is kept only if it improves holdout accuracy. On tau-bench v3 airline, meta-agent improved holdout accuracy from 67% to 87%. We open-sourced meta-agent.…
- COClawe – open-source Trello for agent teamsFeb 2026 · github.com · ▲62
We recently started to use agents to update some documentation across our codebase on a weekly basis, and everything quickly turned into cron jobs, logs, and terminal output. it worked, but was hard to tell what agents were doing, why something failed, or whether a workflow was actually progressing. We thought it would be more interesting to treat agents as long-lived workers with state and responsibilities and explicit handoffs. Something you can actually see and reason about, instead of just tailing logs. So we built Clawe, a small coordination layer on top of OpenClaw that lets agent…
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