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Observability

Skill kimtth/agent-skill-100-lines-or-less/skills/observability

🧿 Minimal but effective AI agent skill definitions in 100 lines or less.

Install
npx -y skills add kimtth/agent-skill-100-lines-or-less --skill observability

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

2 things to look at

  • no licenseNo license file was found in the repository. Code published without one is not open source by default, so using it at work is a question for whoever answers licensing questions where you are.
  • 2 stars2 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

Use when: instrument a system with logs, metrics, and traces to understand it in production.

SKILL.md

1.1 KB, 210 tokens by cl100k_base, as published. Nobody here has run it

Goal: be able to answer new questions about production without redeploying.

Use for:

  • instrumenting services for debugging and alerting
  • diagnosing latency, errors, and saturation
  • designing what to measure and emit

Workflow:

  1. Cover the three pillars: logs, metrics, and traces.
  2. Emit structured logs with correlation/trace IDs.
  3. Track the golden signals: latency, traffic, errors, saturation.
  4. Propagate trace context across service boundaries.
  5. Alert on symptoms users feel, not noisy internals.
  6. Verify you can trace one request end to end.

What to capture:

  • request-level traces with timing per span
  • RED/USE metrics for services and resources
  • structured, queryable logs with context
  • meaningful, low-noise alerts on SLOs

Rules:

  • correlate logs, metrics, and traces with shared IDs
  • alert on user-facing symptoms, not every blip
  • instrument boundaries and dependencies first
  • never log secrets or PII in telemetry

Gives 2 of the 12 instructions most monitoring observability skills give in 210 tokens

Counted across 481 of the 483 authors here whose files we hold, read 2026-08-06

  • link every alert to a runbookin 43 of 481, across 35 files
  • use structured json loggingin 36 of 481, across 31 files
  • alert on user-facing symptomshere, and in 20 of 481, across 15 files
  • emit structured JSON logs with stable event namesin 18 of 481, across 13 files
  • propagate trace context across boundarieshere, and in 16 of 481
  • use histograms for latency trackingin 14 of 481, across 9 files
  • use OpenTelemetry for distributed tracingin 13 of 481, across 8 files
  • include a correlation ID on every log linein 13 of 481, across 8 files
  • Define service level objectivesin 10 of 481, across 7 files
  • Call useAzureMonitor before importing other modulesin 9 of 481, across 2 files
  • stop and ask for clarification if inputs are missingin 9 of 481, across 2 files
  • define on-call questions before adding telemetryin 9 of 481, across 4 files

Said here and by no other author read

  • capture request-level traces with span timing
  • instrument boundaries and dependencies first

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.

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