cloud-logging

# Cloud Logging This skill provides utilities for analyzing logs, errors, and system health across Google Cloud deployments. * Prioritize Logs with `severity` >= "ERROR" as they tend to be less verbose. * Constrain logs around the time of investigation (possibly going back a few hours/days to find smoking guns). ## Logs DOs and DONT's Ingesting Logs into an LMM memory can easily saturate your context. * [BAD] **DO NOT** ingest big files. Antipattern: `ReadFile path/to/access_logs_raw.json`. * It's ok to get raw logs, but also use `jq` or scripts to read them without polluting your context window. * [GOOD] Use simple maniuplation like: `jq -r '.[] | .httpRequest.status'` to get a list of statuses. * [GOOD] Reduce size with scripts such as `scripts/cloudlogging2apachelogs.py <big_logfile.json>` (provided in the skill) * [BAD] Do not call **long**-running calls without precautions, eg `gcloud logging read` * [GOOD] Rather prepend some reasonable `timeout 60 gcloud logging read ...`, or * [BEST] Use the `run_shell_command` tool with `is_background: true` and poll the results being dumped to file periodically. ## Bundled Scripts ### cloudlogging2apachelogs.py Converts GCP Cloud Logging JSON exports into a format loosely resembling Apache Combined Log format. This is much more token-efficient for the LLM. **Usage**: ```bash python3 scripts/cloudlogging2apachelogs.py path/to/logs.json ``` **Testing**: You can verify the script works by running its test: ```bash python3 scripts/cloudlogging2apachelogs_test.py ``` ## Bundled Assets * `assets/sample_logs.json`: A small sample of GCP Cloud Logging JSON for testing conversion scripts.