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agent-observability-spec

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Specify the tracing, metrics, and alerting for an AI agent or LLM feature in production. Use when asked what to log for an LLM app, design agent tracing or spans, define quality and cost monitors, or answer 'how do we know if the agent is misbehaving?'. Produces an observability spec with a trace schema, metric definitions with owners and alert thresholds, sampling and retention policy, and a privacy note for logged content.

skills/agent-observability-spec/SKILL.md

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# Agent Observability Spec Skill

You can't fix what you didn't record. For LLM systems the unit of observability is the *trace* — everything the model saw and did — because behaviour, not uptime, is what fails. This skill specifies what to capture, what to compute from it, and when to page someone.

> **Not quite this?** Use `agent-spec` when you are specifying what the agent should do before it is built.

## What This Skill Produces

- A **trace schema**: per-request spans and the fields each must carry
- **Metric definitions** across health, quality, cost, and behaviour — each with a threshold and owner
- A **sampling and retention policy** that keeps cost sane and debugging possible
- A **privacy note**: what logged content contains, who can see it, and how long it lives

## Required Inputs
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