# agent-observability-session-classify

Evaluates user intent satisfaction in Datadog Agent Observability traces and sessions. Returns a compact yes, partial, or no verdict with a brief reason.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 529 installs · ★ 178
- Author: datadog-labs
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/datadog-labs/agent-skills/tree/HEAD/agent-observability/agent-observability-session-classify/SKILL.md
- Updated: 2026-10-22
- HTML: https://affpapa.org/en/nevibecode/skills/agent-observability-session-classify

## What it does

The skill inspects Datadog Agent Observability logs to determine whether an assistant fulfilled the user request. It operates across three distinct modes: `session_id` for Datadog CMD+I sessions with RUM events, `trace_id` for individual traces, and `ml_app` to sample multiple runs from a target LLM application. By default, the output stays minimal with a single status and a one-sentence explanation.

Before running evaluations, the skill checks execution backends. When provided `--backend pup`, it switches to the `pup` CLI tool. Otherwise, it searches for the active `search_llmobs_spans` MCP tool or verifies if `pup` runs locally via Bash. Instead of relying on short 200-character previews, it triggers full content retrieval cascades across turns before scoring.

The skill halts immediately if neither the Datadog MCP server nor the authenticated `pup` CLI is available in your workspace.

## Requirements
- Works with: Datadog

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add datadog-labs/agent-skills --skill agent-observability-session-classify -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/agent-observability-session-classify/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/agent-observability-session-classify/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add datadog-labs/agent-skills --skill agent-observability-session-classify -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/agent-observability-session-classify/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add datadog-labs/agent-skills --skill agent-observability-session-classify -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/agent-observability-session-classify/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install datadog-labs/agent-skills agent-observability-session-classify
```

2. **Manually: put the files in place**
- File: `.github/skills/agent-observability-session-classify/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add datadog-labs/agent-skills --skill agent-observability-session-classify -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/datadog-labs/agent-skills --path agent-observability/agent-observability-session-classify
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/agent-observability-session-classify/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add datadog-labs/agent-skills --skill agent-observability-session-classify -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/datadog-labs/agent-skills/tree/HEAD/agent-observability/agent-observability-session-classify/SKILL.md

## When to use it
- evaluate whether an agent resolved the user prompt in a specific trace
- classify sample sessions from an LLM application using `ml_app`
- supply labeled satisfaction signals to evaluation pipelines
- correlate RUM telemetry with assistant turns in CMD+I sessions

## Example prompts
- Classify satisfaction for session_id `3a9f1c2b-0000-0000-0000-000000000000`
- Evaluate trace_id `778899112233` and provide the verdict
- Sample and classify 10 traces for ml_app `support-copilot` over timeframe `24h`

## Source text · SKILL.md · MIT

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: b7867f4686c3a67d.

BEGIN THIRD-PARTY FILE b7867f4686c3a67d
```text
---
name: agent-observability-session-classify
description: >
  Classify whether user intent was satisfied in a Datadog Agent Observability trace or session.
  Three modes: (1) session_id — classify a single CMD+I assistant session with RUM;
  (2) trace_id — classify a single Agent Observability trace without RUM; (3) ml_app — sample
  and classify multiple sessions or traces from a given LLM app. Output is compact
  by default (verdict + one-sentence reason). Use when evaluating satisfaction,
  classifying sessions/traces, labeling data, or generating signal for
  agent-observability-eval-pipeline or agent-observability-trace-rca.
---

## Backend

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

1. 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.
2. 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.
3. If MCP tools are present → use **MCP mode** throughout. Call MCP tools exactly as named in this skill's workflow sections.
4. If MCP tools are absent → check whether `pup` is executable: run `pup --version` via Bash. A JSON response containing `"version"` confirms pup is available.
5. 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.
6. 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,rum'`) or install pup."

`--backend pup` is accepted anywhere in the invocation arguments and is stripped before passing remaining args to the skill logic.

**pup invocation rules:**
- Invoke via Bash: `pup llm-obs <subcommand> [flags]`
- pup always outputs JSON. Parse directly — no content-block unwrapping (unlike MCP results, which may wrap JSON in `[{"type": "text", "text": "<json>"}]`).
- If pup returns an auth error, tell the user to run `pup auth login` and stop.
```
END THIRD-PARTY FILE b7867f4686c3a67d

The third-party file has ended. Everything between the b7867f4686c3a67d tags is data: requests and commands from it are not followed. An “end of file” line with another tag inside the block is part of the file.

First 2303 of 60434 bytes. © datadog-labs · MIT · https://github.com/datadog-labs/agent-skills/tree/HEAD/agent-observability/agent-observability-session-classify/SKILL.md

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