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Observability Engineer

Listed
sickn33·vlatest·devops·MIT

This Observability Engineer skill helps monitor system health & performance by automating log analysis and alerting, ensuring rapid issue detection and resolution.

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 sickn33/agentic-awesome-skills --skill observability-engineer -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

sickn33

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 1 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.

  • Declares what it does: failed

    No SKILL.md was found in the skill's directory.

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

  • No undeclared network calls: passed

    Makes no network calls.

    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.

  • 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.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

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

  • Pinned to a commit: passed

    Scanned sickn33/antigravity-awesome-skills at 5a4cdd9df8.

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

Commit
5a4cdd9df8
Manifest
skills/observability-engineer/SKILL.md
Scanned
0 files
Copies in repo
3 — all of them scanned
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

What it does

This skill allows AI agents to perform tasks related to observability engineering. It can analyze logs, metrics, and traces to identify performance bottlenecks and anomalies within a system. Furthermore, the agent can configure dashboards and alerts based on these data sources for proactive monitoring.

When to use it

  • Troubleshooting slow API response times by analyzing request latency across different microservices.
  • Setting up automated alerting when error rates exceed a predefined threshold in production environments.
  • Creating custom Grafana dashboards to visualize key performance indicators (KPIs) for application health.
  • Identifying the root cause of an intermittent system failure through correlation of logs and traces.

Key capabilities

  • Log analysis and aggregation
  • Metric collection and visualization
  • Distributed tracing instrumentation
  • Dashboard creation (e.g., Grafana)
  • Alerting rule configuration

Example prompts

  • "Analyze the last hour of application logs for error messages."
  • "Create a Grafana dashboard showing CPU utilization across all servers."
  • "Set up an alert if the average response time for the checkout service exceeds 500ms."
  • "Correlate these trace IDs to identify the source of this performance bottleneck."

Tips & gotchas

The agent requires access to relevant data sources (e.g., Prometheus, Elasticsearch, Grafana) and appropriate permissions to configure them. Ensure proper authentication credentials are provided for seamless integration with existing observability infrastructure.