| name | cloud-logging |
| description | 🐉 Skill for interacting with and analyzing Google Cloud Logging and Error Reporting. Use this when you need to process large JSON logs from GCP or convert them to Apache format for easier analysis. |
| metadata | {"author":"Riccardo Carlesso","version":"0.3.0","status":"published"} |
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:
python3 scripts/cloudlogging2apachelogs.py path/to/logs.json
Testing:
You can verify the script works by running its test:
python3 scripts/cloudlogging2apachelogs_test.py
Bundled Assets
assets/sample_logs.json: A small sample of GCP Cloud Logging JSON for testing conversion scripts.
