Token Usage Tracker

token-usage-tracker

Quick start Configure defaults in skill-config.json (timezone, log_folder). Install scripts (examples provided) into your workspace and wire the interceptor into your message pipeline. Use scripts/context_summarizer.py before sending large contexts to reduce token usage. What this skill provides Logging: token_tracker.py writes per-call token usage to a JSONL log. Includes timestamp normalization. Interceptor: token_interceptor.py example that normalizes timestamps and forwards sanitized messages to the tracker. Alerts: token_alerts.py example for threshold-based alerts (no external posting by default). Compression: context_summarizer.py produces short summaries to reduce token payloads. Utilities: migration and cleanup scripts (convert timestamps, dedupe log entries). When to use Use this skill when you want transparent per-call token accounting, to keep token usage low, or to protect sensitive/verbose contexts by summarizing before sending to the model. Files scripts/ token_interceptor.py — example interceptor (normalizes timestamps) token_tracker.py — logging helper token_alerts.py — alert examples context_summarizer.py — compression helper migrate_timestamps.py — migration utility dedupe_log.py — dedupe utility references/ examples/systemd/ — example unit files (install manually) skill-config.json — configurable defaults README.md — usage and install notes Configuration See skill-config.json for defaults. The skill exposes: timezone: default UTC log_folder: default ./skills/logs (relative to OpenClaw workspace) compression settings: summary_target_tokens, max_context_tokens Security and installation The scripts are examples and safe by default. They do not change system state or install services automatically. Example systemd/unit files are provided in references/systemd/ — apply them manually after review. License: MIT (adapt as you prefer)