monitoring-operations

acedergren/oci-agent-skills · updated Apr 8, 2026

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$npx skills add https://github.com/acedergren/oci-agent-skills --skill monitoring-operations
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summary

OCI monitoring setup, alarm configuration, and troubleshooting for metrics, logs, and observability gaps.

  • Covers metric namespace patterns, alarm threshold gotchas (sparse data handling, trigger delays), and missing data strategies across OCI services
  • Provides decision trees for log collection troubleshooting, Service Connector setup, and IAM policy requirements
  • Highlights critical anti-patterns: metric lag (10-15 minutes), dimension requirements, notification channel setup, and Clou
skill.md

OCI Monitoring and Observability - Expert Knowledge

🏗️ Use OCI Landing Zone Terraform Modules

Don't reinvent the wheel. Use oracle-terraform-modules/landing-zone for observability stack.

Landing Zone solves:

  • ❌ Bad Practice #10: No logging, monitoring, notifications (Landing Zone deploys complete observability)
  • ❌ Bad Practice #7: Limited security services (Landing Zone integrates Cloud Guard, VSS, OSMS)

This skill provides: Metrics, alarms, and troubleshooting for monitoring deployed WITHIN a Landing Zone.


⚠️ OCI CLI/API Knowledge Gap

You don't know OCI CLI commands or OCI API structure.

Your training data has limited and outdated knowledge of:

  • OCI CLI syntax and parameters (updates monthly)
  • OCI API endpoints and request/response formats
  • Monitoring service CLI operations (oci monitoring alarm, oci monitoring metric)
  • Metric namespaces and MQL (Monitoring Query Language)
  • Latest Logging and Service Connector features

When OCI operations are needed:

  1. Use exact CLI commands from this skill's references
  2. Do NOT guess metric namespace names
  3. Do NOT assume AWS CloudWatch patterns work in OCI
  4. Load reference files for detailed MQL documentation

What you DO know:

  • General observability concepts
  • Alerting and threshold design principles
  • Log aggregation patterns

This skill bridges the gap by providing current OCI-specific monitoring patterns and gotchas.


NEVER Do This

NEVER assume metrics are instant (10-15 minute lag)

  • Metrics published every 1-5 minutes
  • Processing delay: 5-10 minutes
  • Total lag: 10-15 minutes from event to visible metric
  • Don't debug "missing metrics" within first 15 minutes of resource creation

NEVER use = for alarm thresholds with sparse metrics

# WRONG - alarm never fires if metric has gaps
MetricName[1m].mean() = 0

# RIGHT - handle missing data
MetricName[1m]{dataMissing=zero}.mean() > 0

NEVER forget metric dimensions (causes "no data")

# WRONG - missing required dimension
CPUUtilization[1m].mean()

# RIGHT - include resourceId dimension
CPUUtilization[1m]{resourceId="<instance-ocid>"}.mean()

NEVER set alarm thresholds without trigger delay (alert fatigue)

# BAD - fires on every CPU spike
CPUUtilization[1m].mean() > 80

# BETTER - sustained high CPU
CPUUtilization[5m].mean() > 80
Trigger delay: 5 minutes (fires after 5 consecutive breaches)

NEVER create alarms without notification channels

# WRONG - alarm fires but nobody knows
oci monitoring alarm create ... --destinations '[]'

# RIGHT - always link to notification topic
oci monitoring alarm create ... --destinations '["<notification-topic-ocid>"]'

Cost impact: Undetected outages cost $5,000-50,000/hour in production

NEVER ignore Cloud Guard findings (security audit failure)

  • Cloud Guard detects misconfigurations BEFORE they become incidents
  • Integrate Cloud Guard → Notifications → Email/Slack/PagerDuty
  • Cost impact: $100,000+ per security breach vs $0 for proactive remediation

Metric Namespace Gotchas

OCI Metrics Use Service-Specific Namespaces:

Service Namespace Example Metric
Compute oci_computeagent CPUUtilization, MemoryUtilization
Autonomous DB oci_autonomous_database CpuUtilization, StorageUtilization
Load Balancer oci_lbaas HttpRequests, UnHealthyBackendServers
Object Storage oci_objectstorage ObjectCount, BytesUploaded

Common Mistake: Using wrong namespace (oci_compute vs oci_computeagent)

Alarm Missing Data Handling

Setting Behavior Use When
treatMissingDataAsBreaching Alarm fires if no data Critical services (outage = breach)
treatMissingDataAsNotBreaching Alarm silent if no data Optional monitoring
{dataMissing=zero} Treat missing as 0 Counters (requests/sec)

Log Collection Common Gaps

Problem: Logs not showing in Log Analytics

Logs not appearing?
├─ Is log enabled on resource?
│  └─ Compute: oci-compute-agent must be running
│  └─ Function: Logging enabled in function config
├─ Is Service Connector configured?
│  └─ Source: Log Group → Target: Log Analytics
│  └─ Check: Service Connector status = ACTIVE
├─ IAM policy for Service Connector?
│  └─ "Allow any-user to use log-content in tenancy"
│  └─ "Allow service loganalytics to READ logcontent in tenancy"
└─ 10-15 minute ingestion lag?
   └─ Wait before debugging

Metric Query Optimization

Expensive (slow):

# Queries ALL instances
CPUUtilization[1m].mean()

Optimized (filter by dimension):

# Query specific instance
CPUUtilization[1m]{resourceId='<instance-ocid>'}.mean()

Cost: Queries free, but rate limited (1000 req/min)

Progressive Loading References

OCI Monitoring Reference (Official Oracle Documentation)

WHEN TO LOAD oci-monitoring-reference.md:

  • Need comprehensive list of all OCI service metrics
  • Understanding MQL (Monitoring Query Language) in depth
  • Implementing complex alarm conditions and composites
  • Need official Oracle guidance on Logging and Service Connector
  • Setting up Log Analytics and APM integration

Do NOT load for:

  • Quick alarm setup (examples in this skill)
  • Common metric patterns (tables above)
  • Troubleshooting decision trees (covered above)

When to Use This Skill

  • Alarms: threshold configuration, missing data handling, trigger delay
  • Troubleshooting: metrics not showing, alarms not firing, namespace errors
  • Log collection: Service Connector, IAM policies, missing logs
  • Performance: query optimization, dimension filtering
how to use monitoring-operations

How to use monitoring-operations on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add monitoring-operations
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/acedergren/oci-agent-skills --skill monitoring-operations

The skills CLI fetches monitoring-operations from GitHub repository acedergren/oci-agent-skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/monitoring-operations

Reload or restart Cursor to activate monitoring-operations. Access the skill through slash commands (e.g., /monitoring-operations) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

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Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
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general reviews

Ratings

4.527 reviews
  • Jin Zhang· Dec 28, 2024

    Useful defaults in monitoring-operations — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Ganesh Mohane· Dec 20, 2024

    I recommend monitoring-operations for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Nikhil Malhotra· Nov 27, 2024

    monitoring-operations reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Jin Harris· Nov 19, 2024

    I recommend monitoring-operations for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Sakshi Patil· Nov 11, 2024

    Useful defaults in monitoring-operations — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Min Liu· Oct 18, 2024

    I recommend monitoring-operations for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Amelia Desai· Oct 10, 2024

    monitoring-operations reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Chaitanya Patil· Oct 2, 2024

    monitoring-operations is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Min Agarwal· Sep 25, 2024

    monitoring-operations fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Piyush G· Sep 9, 2024

    Keeps context tight: monitoring-operations is the kind of skill you can hand to a new teammate without a long onboarding doc.

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