数据与研究

agent-observability-trace-rca

Name: agent-observability-trace-rca
Author: datadog-labs

datadog-labs/ag…t-skills

Root cause analysis on production LLM traces. Diagnoses why an LLM application is failing — works from eval judge verdicts, runtime errors, or structural anomalies depending on what signals are present. Walks the span tree from symptom to root cause. Use when user says "what's wrong with my app", "why is my eval failing", "analyze errors", "root cause analysis", "diagnose failures", or wants to understand production failure patterns.

563 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-trace-rca

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__list_llmobs_evals appears in your available tools.
If MCP tools are present → use MCP mode throughout. Call MCP tools exactly as named in this skill's 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. Translate every MCP tool call to its pup equivalent using the Tool Reference appendix at the bottom of this file.
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 and is stripped before passing remaining args to the skill logic.

SKILL.md

Backend

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