智能体工作流

agent-observability-eval-pipeline

Name: agent-observability-eval-pipeline
Author: datadog-labs

datadog-labs/ag…t-skills

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. Pure orchestration over the agent-observability sub-skills (`agent-observability-session-classify`, `agent-observability-trace-rca`, `agent-observability-eval-bootstrap`, `agent-observability-experiment-bootstrap`, `agent-observability-experiment-analyzer`). Use when user says "run the eval pipeline", "go from traces to evals", "bootstrap evals end to end", "classify then RCA then bootstrap", "build an eval set from scratch", "onboard me to datasets and experiments", "walk me through experiments", "I have an ml_app, now what", "Agent Observability onboarding", "guided experiment setup", "from traces to experiments", or wants a deterministic, narrated tour from production data through evaluators, datasets, and experiments. Stop early with `--stop-after <phase>` to short-circuit at evaluators or dataset, or resume mid-flow with `--start-at <phase>`.

551 次安装
180 星标
收录于 2026.06.19

安装命令

$ npx skills add https://github.com/datadog-labs/agent-skills --skill agent-observability-eval-pipeline

SKILL.md

Backend

Detection — At the start of every invocation, before taking any action, determine which backend to use:

If the user passed --backend pup anywhere in their invocation → use pup mode immediately, regardless of whether MCP tools are present. Skip steps 2–4.
Check whether MCP tools are present in your active tool list. The canonical signal is whether mcp__datadog-llmo-mcp__search_llmobs_spans appears in your available tools.
If MCP tools are present → use MCP mode throughout. Call MCP tools exactly as named in the sub-skill workflow sections.
If MCP tools are absent → check whether pup is executable: run pup --version via Bash. A JSON response containing "version" confirms pup is available.
If pup responds → use pup mode throughout. Each sub-skill carries its own Tool Reference appendix with the full MCP→pup mapping.
If neither is available → stop and tell the user:

"Neither the Datadog MCP server nor the pup CLI is available. Connect the MCP server (claude mcp add --scope user --transport http datadog-llmo-mcp 'https://mcp.datadoghq.com/v1/mcp?toolsets=llmobs') or install pup."

--backend pup is accepted anywhere in the invocation arguments. Strip it from args before passing to sub-skills, but carry the pup-mode decision forward — every sub-skill must also operate in pup mode for the entire pipeline run.

Sub-skill backend propagation: The backend detected at startup applies to all sub-skills invoked across the six phases. Do not re-detect per phase. Announce once at startup:

MCP mode: "(Running in MCP mode — all features available.)"
pup mode: "(Running in pup mode — pup commands used throughout. All features available.)"

Invocation ID: At the very start of each invocation, before any MCP tool call, generate an 8-character hex invocation ID (e.g., 3a9f1c2b). Keep it constant for the entire invocation.

SKILL.md

Backend

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