LangSmith

When to use this skill

• The user already chose LangSmith and needs the smallest useful packet for tracing, evals, review, prompt ownership, or trace propagation.
• A request mentions LangChain tracing, @traceable / traceable, wrap_openai / wrapOpenAI, annotation queues, run trees, trace IDs, datasets, or experiment comparison.
• The team needs to decide whether a change needs debugging traces, an offline eval gate, an online review queue, Prompt Hub / prompt-registry work, or multi-service lineage.
• The user has some LangSmith setup already, but confidence is still low and they need an audit / gap review.

Do not use this skill as the main workflow when:

• The real job is generic dashboards / alerts / SLOs / telemetry ownership → monitoring-observability
• The real job is existing-log root-cause triage → log-analysis
• The real job is code-level reproduction / bug isolation → debugging
• The real job is deployment / rollout choreography → deployment-automation
• The real job is generic exported-metrics interpretation or KPI synthesis → data-analysis
• The real job is runtime guardrails / approvals / policy middleware → the relevant security or policy skill

Core idea

langsmith should behave like a workflow packet router, not a giant SDK encyclopedia.