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Observability And Instrumentation

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thiientv
observability-and-instrumentation

Designs or reviews logs, metrics, traces, alerts, correlation, dashboards, and diagnostic instrumentation so failures and performance changes can be detected and explained at component boundaries. Use for observability work, incident readiness, instrumentation, or production diagnostics. Not for fixing a specific incident before reproducing it or for adding noisy logging without an operational question.

Overview

Publisherthiientv
Repositorygodmode
Skill nameobservability-and-instrumentation
Stars
99
Forks
76
Bundled files
1
LicenseMIT
Links
Skill sourceRepositoryHomepage
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by thiientv on GitHub. Read the source before you install it.

Installation

Install the Observability And Instrumentation AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/thiientv/godmode/tree/main/skills/observability-and-instrumentation
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/thiientv/godmode.git /tmp/godmode
mkdir -p .claude/skills
cp -r /tmp/godmode/skills/observability-and-instrumentation .claude/skills/observability-and-instrumentation
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Observability And Instrumentation in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Observability And Instrumentation on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Observability And Instrumentation is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Observability and Instrumentation

Start from the question an operator must answer, then emit the smallest safe signal that answers it.

Design sequence

  1. Name the user or business signal, SLO/error budget, failure mode, and responder.
  2. Map the request or job across boundaries and choose correlation/trace IDs, cardinality, sampling, and clock conventions.
  3. Define structured logs for state transitions and failures, metrics for rates/latency/saturation, and traces for cross-service causality.
  4. Include useful dimensions without high-cardinality or secret data. Redact tokens, PII, payloads, and credentials by default.
  5. Add alerts with actionable thresholds, runbook links, ownership, deduping, and a recovery path. Avoid alerts that merely report expected retries.
  6. Verify the signal in a safe environment and test the failure path that should emit it.

Prepare operational baselines

For release and incident use, record the pre-change baseline, freshness, expected variance, promotion or paging threshold, and recovery signal. Include one user or business outcome alongside technical health where possible. A dashboard without a decision owner and action threshold is reference material, not a guardrail.

Preserve a correlation path from release identifier to request, job, dependency, and user-visible outcome. Ensure incident responders can distinguish absent traffic, stale telemetry, collector failure, and healthy zero values.

Read signal-design.md. Instrumentation must not change correctness or become the only source of truth for business data.

Completion condition

An operator can detect the important failure, correlate it across boundaries, understand the likely cause, and follow a bounded response without exposing sensitive data. release-engineering can consume the baseline as a promotion gate; incident-response can consume it as live evidence.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Observability And Instrumentation AI skill do?

Designs or reviews logs, metrics, traces, alerts, correlation, dashboards, and diagnostic instrumentation so failures and performance changes can be detected and explained at component boundaries. Use for observability work, incident readiness, instrumentation, or production diagnostics. Not for fixing a specific incident before reproducing it or for adding noisy logging without an operational question.

Why use Observability And Instrumentation on TypingMind?

Because you install it once and use it with any model. Observability And Instrumentation is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Observability And Instrumentation in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/thiientv/godmode/tree/main/skills/observability-and-instrumentation. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Observability And Instrumentation?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Observability And Instrumentation?

As many as you like. As long as a model supports skills, you can use Observability And Instrumentation with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Observability And Instrumentation AI skill free?

Yes. It is published on GitHub by thiientv under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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