Model Usage Monitor

监控并统计模型调用次数和成本，计算缓存命中率，支持实时监控与每小时自动告警。

This skill largely does local, read-only log analysis as advertised, but two things to watch: (1) monitor.py reads session JSONL files under ~/.openclaw/agents/main/sessions — those files can contain conversation text and metadata; the SKILL.md did not disclose this access. If you have sensitive conversations stored, review the code and consider restricting or removing session access. (2) setup.py invokes the 'openclaw' CLI to create a cron job; confirm you trust that CLI and understand whether it contacts any external service. Before installing: inspect monitor.py (you have full source), run the script manually in a safe environment to see what it reads, and if you prefer, disable automatic cron creation (do not run setup.py) and instead run the script manually or create a cron job yourself after review. If you need higher assurance, run the install in an isolated test environment first.

Type: OpenClaw Skill Name: model-usage-monitor Version: 1.0.1 The skill is a local model usage and cost monitoring tool that parses OpenClaw log files (`gateway.log`, `semantic_check.log`) and session metadata to estimate token costs and cache hit rates. The installation process via `install.sh` and `setup.py` is transparent, using the `openclaw` CLI to schedule a local cron job for hourly alerts. The core logic in `monitor.py` contains no network calls, no obfuscation, and no evidence of data exfiltration, performing all analysis locally on the user's machine.