End-to-end Agent Observability pipeline for an instrumented ml_app — classify production traces, root-cause failures, bootstrap evaluators, then (optionally) sample + publish a dataset, generate + run an experiment, and analyze results. Six narrated phases with a standardized banner and a "continue" checkpoint between each.
Install
npx skills add datadog-labs/agent-skills --skill agent-observability-eval-pipelinenpx skills add datadog-labs/agent-skills --skill agent-observability-eval-pipeline -a claude-code -gnpx skills add datadog-labs/agent-skills --skill agent-observability-eval-pipeline -a codex -ggemini skills install https://github.com/datadog-labs/agent-skills.git --path agent-observability/agent-observability-eval-pipelinegh skill install datadog-labs/agent-skills agent-observability-eval-pipelinegit clone --depth 1 https://github.com/datadog-labs/agent-skills /tmp/datadog-labs__agent-skills
mkdir -p ~/.claude/skills ~/.agents/skills
cp -R /tmp/datadog-labs__agent-skills/agent-observability/agent-observability-eval-pipeline ~/.claude/skills/agent-observability-eval-pipeline # Claude Code
cp -R /tmp/datadog-labs__agent-skills/agent-observability/agent-observability-eval-pipeline ~/.agents/skills/agent-observability-eval-pipeline # Codex, Cursor, Gemini CLI, Copilot, OpenCode