Observability

Organization

DevelopersGlobal

observability

Structured logging, distributed tracing, and alerting for AI systems and traditional services. You can't fix what you can't see.

Overview

Publisher	DevelopersGlobal
Repository	`ai-agent-skills`
Skill name	`observability`
Stars	212
Forks	33
Bundled files	Instructions only
License	MIT
Links	Skill source Repository Homepage

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.
Self-contained
Everything the model needs lives in the instructions — no extra files to sync.
Open source
Published by DevelopersGlobal on GitHub. Read the source before you install it.

Installation

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

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/DevelopersGlobal/ai-agent-skills/tree/main/skills/observability
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.

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/DevelopersGlobal/ai-agent-skills.git /tmp/ai-agent-skills
mkdir -p .claude/skills
cp -r /tmp/ai-agent-skills/skills/observability .claude/skills/observability

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Observability 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 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 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.

Overview

Observability is the ability to understand the internal state of a system from its external outputs. For AI systems this is especially critical: agents make decisions that are hard to interpret without detailed telemetry.

The three pillars: Logs (what happened), Traces (how long and where), Metrics (aggregate health).

When to Use

Before deploying any new service to production
When adding AI agent capabilities to an existing system
When debugging production issues
When designing multi-agent pipelines

Process

Step 1: Structured Logging

All logs must be structured (JSON, not free text). Fields: timestamp, level, service, traceId, message, context.
Log levels used correctly:
- ERROR: Something failed that requires immediate attention
- WARN: Something unexpected happened but the system recovered
- INFO: Normal significant events (requests received, jobs completed)
- DEBUG: Detailed diagnostic information (off in production by default)
Never log secrets, PII, or auth tokens.
For AI systems, log: prompt inputs (sanitized), model outputs, token counts, latency, model version.

Verify: Logs are structured JSON. No secrets in logs. AI interactions logged.

Step 2: Distributed Tracing

Every request gets a unique traceId generated at the entry point.
traceId is propagated through all downstream calls (HTTP headers, message queues, agent calls).
Each service/agent creates a span for its work, with: start time, end time, parent span ID.
Use OpenTelemetry as the standard instrumentation library.

Verify: You can trace a single request across all services/agents in a single view.

Step 3: Metrics

Define and track key metrics:
- RED metrics: Rate (requests/sec), Errors (error rate %), Duration (latency p50/p95/p99)
- AI-specific: Token usage, prompt cost, model latency, hallucination rate, retrieval precision
Dashboards: one dashboard per service with RED metrics, one dashboard for AI system health.

Verify: RED metrics are tracked for every service. AI-specific metrics tracked for AI systems.

Step 4: Alerting

Alerts must be actionable — every alert should have a runbook.
Alert on symptoms (high error rate, high latency), not just causes.
AI-specific alerts: token budget exceeded, model error rate spike, retrieval failure rate spike.
On-call rotation: someone is responsible for every alert at all times.

Verify: Every alert has a runbook. On-call rotation defined.

Common Rationalizations (and Rebuttals)

Excuse	Rebuttal
"We'll add monitoring after launch"	You'll be fighting fires blind. Add it before.
"Console.log is enough"	In production, console.log is noise. Structured logs with context are signals.
"The AI model handles it internally"	Model internals are a black box. You must observe the inputs and outputs.

Verification

Structured JSON logging on all services
No secrets in logs
Distributed tracing with trace ID propagation
RED metrics tracked for all services
AI-specific metrics tracked (tokens, cost, latency)
Alerts configured with runbooks

References

production-deployment skill
multi-agent-orchestration skill
OpenTelemetry documentation

Frequently asked questions

What does the Observability AI skill do?

Structured logging, distributed tracing, and alerting for AI systems and traditional services. You can't fix what you can't see.

Why use Observability on TypingMind?

Because you install it once and use it with any model. Observability 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 in TypingMind?

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

Which AI models can use Observability?

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?

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

Is the Observability AI skill free?

Yes. It is published on GitHub by DevelopersGlobal 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Overview

When to Use

Process

Step 1: Structured Logging

Step 2: Distributed Tracing

Step 3: Metrics

Step 4: Alerting

Common Rationalizations (and Rebuttals)

Verification

References

Frequently asked questions

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