How do I install the GCP Logs Monitoring skill?

Run npx skillmds@latest add bcrema/gcp-logs-monitoring in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is GCP Logs Monitoring safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with GCP Logs Monitoring?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is GCP Logs Monitoring free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published GCP Logs Monitoring?

bcrema (@bcrema) published this skill. Their other Agent Skills are listed on their SkillMD profile.

GCP Logs Monitoring by bcrema — Agent Skill

Name: GCP Logs Monitoring
Author: bcrema

GCP Logs Monitoring

Use when the user needs to inspect Google Cloud (GCP) logs, metrics, and monitoring signals via gcloud for incident triage, debugging, or operational analysis. Supports Cloud Logging queries, Cloud Monitoring time-series reads, and environment checks for a target project.

bcrema Updated Sep 17, 2026

File contents

Goal

Inspect Cloud Logging and Cloud Monitoring data quickly and repeatably from the terminal.

Inputs to collect (ask only if missing)

project_id
time_window for investigation (for example: last 30m, 2h, or explicit UTC start/end)
service/resource context (cloud_run_revision, k8s_container, gce_instance, load balancer, etc.)
signals of interest (errors, latency, CPU, memory, request count, restarts)
output_format (table for quick scans, json for deeper analysis)

Execution workflow

Validate prerequisites: bash .agents/skills/gcp-logs-monitoring/scripts/check_prereqs.sh --project <project_id>
Query Cloud Logging: bash .agents/skills/gcp-logs-monitoring/scripts/read_logs.sh --project <project_id> --filter '<LOG_FILTER>' --freshness 1h --limit 100 --format json
Query Cloud Monitoring time series: bash .agents/skills/gcp-logs-monitoring/scripts/read_metrics.sh --project <project_id> --filter '<METRIC_FILTER>' --start <UTC_ISO8601> --end <UTC_ISO8601> --format json
Correlate timestamps between logs and metrics, then summarize likely root cause and next checks.

Common filter templates

Cloud Logging

Cloud Run errors: resource.type="cloud_run_revision" severity>=ERROR
GKE container errors: resource.type="k8s_container" severity>=ERROR
HTTP 5xx in load balancer logs: resource.type="http_load_balancer" jsonPayload.statusDetails=~"5.."
Timeout text search: textPayload:"timeout" OR jsonPayload.message:"timeout"

Cloud Monitoring

Cloud Run request count: metric.type="run.googleapis.com/request_count" AND resource.type="cloud_run_revision"
Cloud Run request latencies: metric.type="run.googleapis.com/request_latencies" AND resource.type="cloud_run_revision"
VM CPU utilization: metric.type="compute.googleapis.com/instance/cpu/utilization" AND resource.type="gce_instance"

Guardrails

Prefer passing --project on every command instead of changing global gcloud config.
Start with short windows (15m to 2h) and widen only when needed.
Use --format json when output will be parsed or compared across sources.
If auth or project checks fail, fix environment first and then re-run queries.

---name: gcp-logs-monitoringdescription: Use when the user needs to inspect Google Cloud (GCP) logs, metrics, and monitoring signals via gcloud for incident triage, debugging, or operational analysis. Supports Cloud Logging queries, Cloud Monitoring time-series reads, and environment checks for a target project.---## GoalInspect Cloud Logging and Cloud Monitoring data quickly and repeatably from the terminal.## Inputs to collect (ask only if missing)- `project_id`- `time_window` for investigation (for example: last `30m`, `2h`, or explicit UTC `start/end`)- `service/resource context` (`cloud_run_revision`, `k8s_container`, `gce_instance`, load balancer, etc.)- `signals of interest` (errors, latency, CPU, memory, request count, restarts)- `output_format` (`table` for quick scans, `json` for deeper analysis)## Execution workflow1. Validate prerequisites: `bash .agents/skills/gcp-logs-monitoring/scripts/check_prereqs.sh --project <project_id>`2. Query Cloud Logging: `bash .agents/skills/gcp-logs-monitoring/scripts/read_logs.sh --project <project_id> --filter '<LOG_FILTER>' --freshness 1h --limit 100 --format json`3. Query Cloud Monitoring time series: `bash .agents/skills/gcp-logs-monitoring/scripts/read_metrics.sh --project <project_id> --filter '<METRIC_FILTER>' --start <UTC_ISO8601> --end <UTC_ISO8601> --format json`4. Correlate timestamps between logs and metrics, then summarize likely root cause and next checks.## Common filter templates### Cloud Logging- Cloud Run errors: `resource.type="cloud_run_revision" severity>=ERROR`- GKE container errors: `resource.type="k8s_container" severity>=ERROR`- HTTP 5xx in load balancer logs: `resource.type="http_load_balancer" jsonPayload.statusDetails=~"5.."`- Timeout text search: `textPayload:"timeout" OR jsonPayload.message:"timeout"`### Cloud Monitoring- Cloud Run request count: `metric.type="run.googleapis.com/request_count" AND resource.type="cloud_run_revision"`- Cloud Run request latencies: `metric.type="run.googleapis.com/request_latencies" AND resource.type="cloud_run_revision"`- VM CPU utilization: `metric.type="compute.googleapis.com/instance/cpu/utilization" AND resource.type="gce_instance"`## Guardrails- Prefer passing `--project` on every command instead of changing global gcloud config.- Start with short windows (`15m` to `2h`) and widen only when needed.- Use `--format json` when output will be parsed or compared across sources.- If auth or project checks fail, fix environment first and then re-run queries.

Frequently asked questions