// 🐉 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.
🐉 [SRE] Use when the user wants to set up Google Managed MCP (OneMCP) servers for their Gemini CLI environment. Automates enabling services, MCP servers, generating API keys, and configuring ~/.gemini/settings.json.
🐉 Detects anomalies in time-series data from various sources.
🐉 Fetches and parses time-series data from various sources.
🐉 [SRE] Use when you need to follow established SRE playbooks for GCP/GKE investigations, including infrastructure discovery and common mitigation steps.
🐉 Initial Google Cloud environment verification and authentication setup. Use when starting a new session to ensure correct identities across gcloud, ADC, and kubectl.
🐉 [SRE] Manage Service Level Objectives (SLOs) on Google Cloud. Use when you need to discover Monitoring Services, list existing SLOs, or create new SLOs (Availability/Latency) via the REST API when gcloud commands are unavailable.
| 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"} |
This skill provides utilities for analyzing logs, errors, and system health across Google Cloud deployments.
severity >= "ERROR" as they tend to be less verbose.Ingesting Logs into an LMM memory can easily saturate your context.
ReadFile path/to/access_logs_raw.json.
jq or scripts to read them without polluting your context window.jq -r '.[] | .httpRequest.status' to get a list of statuses.scripts/cloudlogging2apachelogs.py <big_logfile.json> (provided in the skill)gcloud logging read
timeout 60 gcloud logging read ..., orrun_shell_command tool with is_background: true and poll the results being dumped to file periodically.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
assets/sample_logs.json: A small sample of GCP Cloud Logging JSON for testing conversion scripts.