agent-observability-spec
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.
npx skills add mohitagw15856/pm-claude-skills --skill agent-observability-spec --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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. ## 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 Ask for (if not already provided): - **The system's shape** — single LLM call, RAG pipeline, or multi-step tool-using agent - **Traffic volume and cost sensitivity** — full tracing at 10M req/day is a budget decision - **What "misbehaving" means here** — the two or three failure modes that matter most (wrong facts? wrong actions? cost? refusals?) - **Existing observability stack** (Datadog, Langfuse, OTel, homegrown) — spec into it, not around it ## Trace Schema Every request produces one trace; every
- What This Skill Produces
- Required Inputs
- Trace Schema
- Metrics and Alerts
- Sampling & Retention
- Output Format
- Observability Spec: [feature/agent]
- Quality Checks
- Anti-Patterns
What does the agent-observability-spec skill do?
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.
How do I install it?
Run `npx skills add mohitagw15856/pm-claude-skills --skill agent-observability-spec --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From mohitagw15856/pm-claude-skills, a repository with 1,255 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.