Metaclaw

Generischer Skill für Memory-Management, Hybrid Retrieval und Skill Evolution mit PluginEval Integration

This skill appears coherent with its stated purpose, but review and take these precautions before installing or enabling automatic runs: - Inspect and run in an isolated environment (use the advised .venv-metaclaw) so Python packages and model downloads are separated from your system. - The scripts read and write files under your workspace (MEMORY.md, memory/*.md, SESSION-STATE.md, .session-count). Back up important data before running consolidation/auto-fix steps. - memory-extract sends content to a local Ollama endpoint (http://localhost:11434). Confirm you run a trusted local LLM; do not point OLLAMA_HOST (if you modify code) to an untrusted remote service or put sensitive data into prompts sent to external hosts. - skill-eval's auto-fix can modify files when invoked with --allow-write; run first with read-only flags (--layer1, --auto-fix without --allow-write) to preview changes. Keep backups and review diffs before applying fixes. - There are minor path/name inconsistencies in docs vs. scripts (e.g., references to memory-digest-enhanced.sh vs memory-digest.sh and varying script paths). Verify the correct paths before installing crontab or heartbeat hooks. If you want higher confidence, provide answers or artifacts for these points and I can re-evaluate with higher confidence: - Do you plan to run a local Ollama or other LLM? If not, which extraction fallback do you intend to use? - Will you allow CLI '--allow-write' or cron/heartbeat integration (automated writes)? - Do you want me to scan specific files in your workspace to see what this skill would read/process?

Type: OpenClaw Skill Name: metaclaw Version: 1.0.0 The MetaClaw skill bundle is a legitimate toolset for advanced memory management and skill quality maintenance within the OpenClaw ecosystem. It features hybrid retrieval (semantic and FTS) using LanceDB, automated memory extraction via a local Ollama instance (memory-extract.py), and a static analysis tool (skill-eval.py) designed to improve skill metadata. All operations are confined to the local workspace, and the 'auto-fix' functionality is protected by explicit safety flags. No indicators of data exfiltration, malicious execution, or harmful prompt injection were detected.