oraclecloud-observability

Set up programmatic monitoring, logging, and alarms for OCI resources. Use when configuring OCI Monitoring metrics, creating alarm rules, publishing custom metrics, or searching logs via the Logging service. Trigger with "oraclecloud observability", "oci monitoring", "oci alarms", "oci logging", "oracle cloud observability".

1,868 stars

Best use case

oraclecloud-observability is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Set up programmatic monitoring, logging, and alarms for OCI resources. Use when configuring OCI Monitoring metrics, creating alarm rules, publishing custom metrics, or searching logs via the Logging service. Trigger with "oraclecloud observability", "oci monitoring", "oci alarms", "oci logging", "oracle cloud observability".

Teams using oraclecloud-observability should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/oraclecloud-observability/SKILL.md --create-dirs "https://raw.githubusercontent.com/jeremylongshore/claude-code-plugins-plus-skills/main/plugins/saas-packs/oraclecloud-pack/skills/oraclecloud-observability/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/oraclecloud-observability/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How oraclecloud-observability Compares

Feature / Agentoraclecloud-observabilityStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Set up programmatic monitoring, logging, and alarms for OCI resources. Use when configuring OCI Monitoring metrics, creating alarm rules, publishing custom metrics, or searching logs via the Logging service. Trigger with "oraclecloud observability", "oci monitoring", "oci alarms", "oci logging", "oracle cloud observability".

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

Related Guides

SKILL.md Source

# Oracle Cloud Observability

## Overview

Set up programmatic monitoring for OCI infrastructure using the Monitoring, Logging, and Notifications services. The OCI Console buries these features behind nested menus, and the status page has historically failed to acknowledge outages (e.g., London region, January 2026). This skill builds monitoring you control through code — metric queries, alarm rules, custom metric publishing, and log searches — so you are never surprised by an outage you should have caught.

**Purpose:** Create a code-driven observability stack that queries metrics, fires alarms, publishes custom metrics, and searches logs without depending on the OCI Console.

## Prerequisites

- **OCI tenancy** with an API signing key in `~/.oci/config`
- **Python 3.8+** with `pip install oci`
- **Compartment OCID** containing the resources to monitor
- **IAM policies** granting `manage alarms` and `read metrics` in the target compartment
- **Notification topic** created for alarm destinations (or create one in Step 4)

## Instructions

### Step 1: Query Metrics with MonitoringClient

OCI publishes built-in metrics for compute, networking, block storage, and more. Query them programmatically:

```python
import oci
from datetime import datetime, timedelta

config = oci.config.from_file("~/.oci/config")
monitoring = oci.monitoring.MonitoringClient(config)

# Query CPU utilization for all instances in a compartment
response = monitoring.summarize_metrics_data(
    compartment_id="ocid1.compartment.oc1..example",
    summarize_metrics_data_details=oci.monitoring.models.SummarizeMetricsDataDetails(
        namespace="oci_computeagent",
        query='CpuUtilization[5m]{availabilityDomain = "Uocm:US-ASHBURN-AD-1"}.mean()',
        start_time=(datetime.utcnow() - timedelta(hours=1)).isoformat() + "Z",
        end_time=datetime.utcnow().isoformat() + "Z"
    )
)

for metric in response.data:
    for dp in metric.aggregated_datapoints:
        print(f"{dp.timestamp}: {dp.value:.1f}% CPU")
```

### Step 2: Create Alarm Rules

Alarms trigger when a metric crosses a threshold. Create them via SDK so they survive Console UI changes:

```python
monitoring.create_alarm(
    oci.monitoring.models.CreateAlarmDetails(
        display_name="High CPU Alert",
        compartment_id="ocid1.compartment.oc1..example",
        metric_compartment_id="ocid1.compartment.oc1..example",
        namespace="oci_computeagent",
        query='CpuUtilization[5m].mean() > 80',
        severity="CRITICAL",
        body="CPU utilization exceeded 80% for 5 minutes.",
        destinations=["ocid1.onstopic.oc1..example"],
        is_enabled=True,
        pending_duration="PT5M",
        repeat_notification_duration="PT15M"
    )
)
print("Alarm created: High CPU Alert")
```

### Step 3: Publish Custom Metrics

Push application-level metrics into OCI Monitoring so they can trigger the same alarm system:

```python
from datetime import datetime

monitoring.post_metric_data(
    oci.monitoring.models.PostMetricDataDetails(
        metric_data=[
            oci.monitoring.models.MetricDataDetails(
                namespace="custom_app",
                compartment_id="ocid1.compartment.oc1..example",
                name="RequestLatencyMs",
                dimensions={"service": "api-gateway", "endpoint": "/v1/orders"},
                datapoints=[
                    oci.monitoring.models.Datapoint(
                        timestamp=datetime.utcnow().isoformat() + "Z",
                        value=142.5
                    )
                ]
            )
        ]
    )
)
print("Custom metric published: RequestLatencyMs = 142.5ms")
```

### Step 4: Set Up Notifications

Create a notification topic and email subscription to receive alarm alerts:

```python
notifications = oci.ons.NotificationDataPlaneClient(config)
control_plane = oci.ons.NotificationControlPlaneClient(config)

# Create topic
topic = control_plane.create_topic(
    oci.ons.models.CreateTopicDetails(
        name="infra-alerts",
        compartment_id="ocid1.compartment.oc1..example",
        description="Infrastructure alarm notifications"
    )
).data

# Subscribe an email endpoint
notifications.create_subscription(
    oci.ons.models.CreateSubscriptionDetails(
        topic_id=topic.topic_id,
        compartment_id="ocid1.compartment.oc1..example",
        protocol="EMAIL",
        endpoint="oncall@example.com"
    )
)
print(f"Topic created: {topic.topic_id}")
```

### Step 5: Search Logs

Query the OCI Logging service to find specific events across your infrastructure:

```python
logging_search = oci.loggingsearch.LogSearchClient(config)

results = logging_search.search_logs(
    oci.loggingsearch.models.SearchLogsDetails(
        time_start=(datetime.utcnow() - timedelta(hours=1)).isoformat() + "Z",
        time_end=datetime.utcnow().isoformat() + "Z",
        search_query=(
            'search "ocid1.compartment.oc1..example" '
            '| where data.statusCode = 500'
        ),
        is_return_field_info=False
    )
)

for log_entry in results.data.results:
    print(f"{log_entry.data}")
```

### Step 6: Health Check Probes

Monitor endpoint availability with OCI Health Checks:

```python
health = oci.healthchecks.HealthChecksClient(config)

health.create_http_monitor(
    oci.healthchecks.models.CreateHttpMonitorDetails(
        compartment_id="ocid1.compartment.oc1..example",
        display_name="API Health Check",
        targets=["api.example.com"],
        protocol="HTTPS",
        port=443,
        path="/health",
        interval_in_seconds=30,
        timeout_in_seconds=10,
        is_enabled=True
    )
)
print("Health check probe created: api.example.com/health every 30s")
```

## Output

Successful completion produces:
- Metric queries returning CPU, memory, and network data for your compartment
- Alarm rules that fire to notification topics when thresholds are breached
- Custom application metrics published to OCI Monitoring
- A notification topic with email subscription for alert delivery
- Log search queries for troubleshooting 500 errors and other events
- HTTP health check probes for endpoint availability monitoring

## Error Handling

| Error | Code | Cause | Solution |
|-------|------|-------|----------|
| NotAuthenticated | 401 | Bad API key or expired config | Verify `~/.oci/config` fingerprint matches your API key |
| NotAuthorizedOrNotFound | 404 | Missing IAM policy for monitoring | Add: `Allow group X to manage alarms in compartment Y` |
| TooManyRequests | 429 | Rate limited on metric queries | Reduce query frequency; cache results for dashboards |
| InternalError | 500 | OCI Monitoring service issue | Check [OCI Status](https://ocistatus.oraclecloud.com) and retry |
| InvalidParameter | 400 | Wrong MQL query syntax | Verify namespace and metric name; use `list_metrics` to discover available metrics |
| ServiceError status -1 | N/A | Request timeout on large queries | Narrow the time window or add dimension filters |

## Examples

**Quick metric check with OCI CLI:**

```bash
# List available metric namespaces
oci monitoring metric list \
  --compartment-id ocid1.compartment.oc1..example \
  --namespace oci_computeagent

# List all alarms
oci monitoring alarm list \
  --compartment-id ocid1.compartment.oc1..example
```

**List all metrics in a namespace to discover what's available:**

```python
import oci

config = oci.config.from_file("~/.oci/config")
monitoring = oci.monitoring.MonitoringClient(config)

metrics = monitoring.list_metrics(
    compartment_id="ocid1.compartment.oc1..example",
    list_metrics_details=oci.monitoring.models.ListMetricsDetails(
        namespace="oci_computeagent"
    )
).data

for m in metrics:
    print(f"{m.name} — dimensions: {m.dimensions}")
```

## Resources

- [OCI Monitoring](https://docs.oracle.com/en-us/iaas/Content/Monitoring/home.htm) — metrics, alarms, and MQL query language
- [OCI Logging](https://docs.oracle.com/en-us/iaas/Content/Logging/home.htm) — centralized log service
- [OCI Notifications](https://docs.oracle.com/en-us/iaas/Content/Notification/home.htm) — alarm delivery via email, Slack, PagerDuty
- [OCI Python SDK](https://docs.oracle.com/en-us/iaas/tools/python/latest/) — SDK reference
- [OCI Known Issues](https://docs.oracle.com/en-us/iaas/Content/knownissues.htm) — current platform issues

## Next Steps

After monitoring is in place, proceed to `oraclecloud-performance-tuning` to optimize shape and storage performance, or see `oraclecloud-cost-tuning` to set up budget alerts that use the same notification topics.

Related Skills

windsurf-observability

1868
from jeremylongshore/claude-code-plugins-plus-skills

Monitor Windsurf AI adoption, feature usage, and team productivity metrics. Use when tracking AI feature usage, measuring ROI, setting up dashboards, or analyzing Cascade effectiveness across your team. Trigger with phrases like "windsurf monitoring", "windsurf metrics", "windsurf analytics", "windsurf usage", "windsurf adoption".

webflow-observability

1868
from jeremylongshore/claude-code-plugins-plus-skills

Set up observability for Webflow integrations — Prometheus metrics for API calls, OpenTelemetry tracing, structured logging with pino, Grafana dashboards, and alerting for rate limits, errors, and latency. Trigger with phrases like "webflow monitoring", "webflow metrics", "webflow observability", "monitor webflow", "webflow alerts", "webflow tracing".

vercel-observability

1868
from jeremylongshore/claude-code-plugins-plus-skills

Set up Vercel observability with runtime logs, analytics, log drains, and OpenTelemetry tracing. Use when implementing monitoring for Vercel deployments, setting up log drains, or configuring alerting for function errors and performance. Trigger with phrases like "vercel monitoring", "vercel metrics", "vercel observability", "vercel logs", "vercel alerts", "vercel tracing".

veeva-observability

1868
from jeremylongshore/claude-code-plugins-plus-skills

Veeva Vault observability for enterprise operations. Use when implementing advanced Veeva Vault patterns. Trigger: "veeva observability".

vastai-observability

1868
from jeremylongshore/claude-code-plugins-plus-skills

Monitor Vast.ai GPU instance health, utilization, and costs. Use when setting up monitoring dashboards, configuring alerts, or tracking GPU utilization and spending. Trigger with phrases like "vastai monitoring", "vastai metrics", "vastai observability", "monitor vastai", "vastai alerts".

twinmind-observability

1868
from jeremylongshore/claude-code-plugins-plus-skills

Monitor TwinMind transcription quality, meeting coverage, action item extraction rates, and memory vault health. Use when implementing observability, or managing TwinMind meeting AI operations. Trigger with phrases like "twinmind observability", "twinmind observability".

speak-observability

1868
from jeremylongshore/claude-code-plugins-plus-skills

Monitor Speak API health, assessment latency, session metrics, and pronunciation score distributions. Use when implementing observability, or managing Speak language learning platform operations. Trigger with phrases like "speak observability", "speak observability".

snowflake-observability

1868
from jeremylongshore/claude-code-plugins-plus-skills

Set up Snowflake observability using ACCOUNT_USAGE views, alerts, and external monitoring. Use when implementing Snowflake monitoring dashboards, setting up query performance tracking, or configuring alerting for warehouse and pipeline health. Trigger with phrases like "snowflake monitoring", "snowflake metrics", "snowflake observability", "snowflake dashboard", "snowflake alerts".

shopify-observability

1868
from jeremylongshore/claude-code-plugins-plus-skills

Set up observability for Shopify app integrations with query cost tracking, rate limit monitoring, webhook delivery metrics, and structured logging. Trigger with phrases like "shopify monitoring", "shopify metrics", "shopify observability", "monitor shopify API", "shopify alerts", "shopify dashboard".

salesforce-observability

1868
from jeremylongshore/claude-code-plugins-plus-skills

Set up observability for Salesforce integrations with API limit monitoring, error tracking, and alerting. Use when implementing monitoring for Salesforce operations, tracking API consumption, or configuring alerting for Salesforce integration health. Trigger with phrases like "salesforce monitoring", "salesforce metrics", "salesforce observability", "monitor salesforce", "salesforce alerts", "salesforce API usage dashboard".

retellai-observability

1868
from jeremylongshore/claude-code-plugins-plus-skills

Retell AI observability — AI voice agent and phone call automation. Use when working with Retell AI for voice agents, phone calls, or telephony. Trigger with phrases like "retell observability", "retellai-observability", "voice agent".

replit-observability

1868
from jeremylongshore/claude-code-plugins-plus-skills

Monitor Replit deployments with health checks, uptime tracking, resource usage, and alerting. Use when setting up monitoring for Replit apps, building health dashboards, or configuring alerting for deployment health and performance. Trigger with phrases like "replit monitoring", "replit metrics", "replit observability", "monitor replit", "replit alerts", "replit uptime".