Feature Request: Cloud Monitoring & Logging Skill

# Feature Request: Cloud Monitoring & Logging Skill

## Summary

Production workloads on Google Cloud require robust observability — yet no existing skill covers Cloud Monitoring (metrics, alerts, SLOs, dashboards) or Cloud Logging (log routing, query syntax, cost optimization). This leaves a critical Day-2 operations gap for agents assisting with GCP deployments.

## The Gap

Today, if a user asks an agent:
- 'How do I set up alerts for my Cloud Run service?'
- 'Why did my GKE pod crash? Where are the logs?'
- 'How do I reduce my Cloud Logging bill?'

The agent must fall back to generic knowledge or unrelated skills. There is no single skill that documents:

- Creating Alerting Policies via gcloud, Terraform, or the Console
- Writing PromQL / MQL queries for Cloud Monitoring
- Exporting logs to BigQuery or Cloud Storage for long-term retention
- Configuring log-based metrics for custom business KPIs
- Using Error Reporting to aggregate and track exceptions across services
- Distributed tracing with Cloud Trace (OpenTelemetry integration)

## Proposed Skill

A `cloud-observability-basics` skill that agents load when users mention: monitoring, alerting, logs, metrics, SLO, error reporting, tracing, Cloud Monitoring, Cloud Logging, or observability.

### Suggested SKILL.md frontmatter

```yaml
---
name: cloud-observability-basics
description: >
Use when the user asks about monitoring, logging, alerting, tracing, or observability
for Google Cloud services. Covers Cloud Monitoring (metrics, dashboards, alerting policies,
SLOs), Cloud Logging (log routing, log-based metrics, excluded logs), Cloud Trace
(distributed tracing, OpenTelemetry), and Error Reporting. WHEN: set up alert, create
dashboard, view logs, reduce logging cost, trace request, debug crash, observability.
compatibility: Requires monitoring.metricWriter, logging.logWriter, and cloudtrace.agent IAM roles.
---
```

### Key reference topics

1. **Cloud Monitoring**
- Alerting policies: metric thresholds, uptime checks, log-based alerts
- Dashboards: JSON model, MQL vs PromQL
- SLOs: defining SLI, error budget, burn rate alerts
- Custom metrics: OpenCensus / OpenTelemetry instrumentation
2. **Cloud Logging**
- Log Explorer query syntax (LogQL), regex, JSON subfield extraction
- Log buckets, log views, and IAM
- Log sinks: BigQuery, Cloud Storage, Pub/Sub export
- Exclusion filters to control ingestion cost
- Log-based metrics (counter & distribution)
3. **Cloud Trace**
- OpenTelemetry auto-instrumentation for Cloud Run, GKE, App Engine
- Trace span analysis and latency debugging
4. **Error Reporting**
- Automatic exception grouping from Cloud Functions, Cloud Run, GKE
- Notifications via email / Pub/Sub / Slack
5. **Cost Optimization**
- Logging ingestion pricing tiers
- When to use `_Default` vs custom log buckets
- Scheduled queries vs streaming exports

## Why Now?

- All existing compute skills (GKE, Cloud Run, Cloud Functions) stop at 'deploy successfully' with no Day-2 operational guidance.
- Cloud Billing and cost optimization are recurring user concerns; logging is often the surprise cost driver.
- Google is strongly pushing OpenTelemetry as the unified observability standard; a skill should bridge GCP-native tools with OTel.

## Reference Implementation

Google Cloud official docs:
- https://cloud.google.com/monitoring/docs
- https://cloud.google.com/logging/docs
- https://cloud.google.com/trace/docs
- https://cloud.google.com/error-reporting/docs

Happy to contribute a SKILL.md draft if this direction is accepted.

Start with the proposed cloud-observability-basics SKILL.md frontmatter and the Google Cloud Monitoring, Logging, Trace, and Error Reporting documentation linked in the issue. Review how existing compute skills cover deployment, then define the skill's scope across alerting, logs, tracing, error reporting, and cost optimization. Done means a usable SKILL.md draft that covers the listed triggers and reference topics.

Written by the indexing model from the issue text.