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Logging Observability

Checked
cosmix·vlatest·devops·MIT

Cosmix’s Logging Observability skill automatically captures and correlates logs for enhanced DevOps insights and proactive issue detection.

Repository

Install on your platform

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

Installs under a different name. Its SKILL.md names it loom-logging-observability, not the name it is listed under, so the command below uses that name.
  1. 1

    Run this in your project

    terminal
    $ npx skills add cosmix/loom --skill loom-logging-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

cosmix

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 passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • 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 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 cosmix/loom at 851ce24346.

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

Commit
851ce24346
Manifest
skills/loom-logging-observability/SKILL.md
Scanned
1 file, 286 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

What it does

This skill, Logging Observability from cosmix, enhances DevOps insights by automatically capturing and correlating logs, metrics, and traces. It focuses on structured logging using JSON format with correlation IDs and contextual data, distributed tracing across services, and centralized log management. The skill provides patterns for collecting system health metrics and creating symptom-based alerts with associated runbooks.

When to use it

  • Debugging Distributed Systems: Track requests across multiple services to pinpoint the source of errors.
  • Improving System Performance: Monitor key metrics (counters, gauges, histograms) to identify bottlenecks and areas for optimization.
  • Proactive Issue Detection: Set up alerts based on specific log patterns or metric thresholds to address problems before they impact users.
  • Centralized Log Analysis: Aggregate logs from various sources into a central location for easier searching and analysis (e.g., using ELK, Loki, Datadog).

Key capabilities

  • Structured Logging (JSON): Logs are formatted as JSON with fields like timestamp, level, logger, message, correlation ID, and span ID.
  • Distributed Tracing: Supports OpenTelemetry, Jaeger, and Zipkin for tracking requests across services.
  • Metrics Collection: Provides patterns for collecting counters, gauges, and histograms to monitor system health (Prometheus compatible).
  • Log Aggregation: Facilitates centralized log management using tools like ELK, Loki, or Datadog.
  • Alerting: Enables symptom-based alerts with runbooks for proactive issue resolution.

Example prompts

  • "Implement structured logging in my Python application."
  • "Show me an example of how to add a correlation ID to my logs."
  • "How can I set up metrics collection using this skill?"

Tips & gotchas

  • The skill provides code examples for both Python and TypeScript. You'll need to adapt these examples to your specific application environment.
  • To leverage distributed tracing, ensure your services are instrumented with OpenTelemetry or compatible frameworks.