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Monitoring Observability

Listed
ahmedasmar·vlatest·devops

Proactively identifies and alerts on system anomalies using advanced observability techniques and automated monitoring dashboards.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add ahmedasmar/devops-claude-skills --skill monitoring-observability -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

ahmedasmar

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill failed 2 of 6 checks. A failure is not proof of bad intent; it means something in the code needs a human to look at it before you run it.

  • No undeclared network calls: failed

    Contacts 2 hosts that are neither well-known infrastructure nor declared in the skill's frontmatter: api.datadoghq.com, s3.amazonaws.com.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

    Hosts contacted

    api.datadoghq.comundeclareds3.amazonaws.comundeclared

    What it found

    • monitoring-observability/skills/SKILL.md:349contacts api.datadoghq.com
      curl -s -X GET "https://api.datadoghq.com/api/v1/usage/summary?start_month=$(date -u +%Y-%m)" \
    • monitoring-observability/skills/SKILL.md:354contacts api.datadoghq.com
      curl -s -X GET "https://api.datadoghq.com/api/v1/usage/hosts?start_hr=$(date -u -v-24H +%Y-%m-%dT%H)" \
    • monitoring-observability/skills/references/tool_comparison.md:90contacts s3.amazonaws.com
      DD_API_KEY=xxx bash -c "$(curl -L https://s3.amazonaws.com/dd-agent/scripts/install_script.sh)"

  • No pipe-to-shell installers: failed

    Runs code fetched at install time in 1 place.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

    What it found

    • monitoring-observability/skills/references/tool_comparison.md:90remote script run via command substitution
      DD_API_KEY=xxx bash -c "$(curl -L https://s3.amazonaws.com/dd-agent/scripts/install_script.sh)"

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • Pinned to a commit: passed

    Scanned ahmedasmar/devops-claude-skills at 1489c33ad8.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
1489c33ad8
Manifest
monitoring-observability/skills/SKILL.md
Scanned
18 files, 7,663 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

The monitoring-observability skill empowers AI agents to analyze system health, interpret logs, and correlate metrics across distributed infrastructure. It enables automated detection of anomalies and generation of actionable remediation steps for complex DevOps environments.

When to use it

  • Diagnosing performance bottlenecks in microservices architectures by analyzing latency and throughput data.
  • Investigating root causes of service outages through log aggregation and error pattern recognition.
  • Automating incident response workflows by triggering alerts based on real-time metric thresholds.
  • Generating synthetic dashboards to visualize system status for non-technical stakeholders.

Key capabilities

  • Real-time log parsing and filtering across multiple sources.
  • Metric correlation to identify dependencies between services.
  • Anomaly detection using statistical baselines.
  • Automated report generation for incident post-mortems.
  • Infrastructure topology mapping from configuration data.

Example prompts

  • "Analyze the error logs from our payment service and identify the top three recurring exceptions in the last hour."
  • "Correlate high CPU usage spikes with database query latency to determine if the bottleneck is compute or storage."
  • "Generate a summary of system health status including uptime percentage and active alert counts for the morning standup."

Tips & gotchas

Ensure your AI agent has read-only access to monitoring endpoints like Prometheus, Datadog, or ELK stacks before deployment. This skill relies on structured data availability; unstructured or noisy logs may reduce diagnostic accuracy.