Skill: eval-session-classify

Classify whether user intent was satisfied in a Datadog Agent Observability trace or session.

Context cost
16k tokensestimated from the bundle, loaded when it triggers
Bundle
1 file · 63.9 kBtext throughout, nothing executable
Licence
MITthe author’s own terms
Last change
—no release on file
Servers it uses
Noneruns standalone

What it does

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.

What this skill changes about the agent has not been set out here as a list. Its SKILL.md, shown below, says it in the author’s own words.

analytics

The skill itself

A skill is instructions the model reads, so you can see what you are getting: below is the start of its SKILL.md. The front matter loads with every session, the body loads when the skill triggers, and the whole file comes with the install.

SKILL.md63.9 kB · preview
--- name: agent-observability-session-classify ---
13## Backend
14
15**Detection** — At the start of every invocation, before taking any action, determine which backend to use:
16
171. 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.
182. 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.
193. If MCP tools are present → use **MCP mode** throughout. Call MCP tools exactly as named in this skill's workflow sections.
204. If MCP tools are absent → check whether pup is executable: run pup --version via Bash. A JSON response containing "version" confirms pup is available.
215. 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.
226. If neither is available → stop and tell the user:
23 > "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/api/unstable/mcp-server/mcp?toolsets=llmobs,rum') or install pup."
24
25--backend pup is accepted anywhere in the invocation arguments and is stripped before passing remaining args to the skill logic.
26
27**pup invocation rules:**
28- Invoke via Bash: pup llm-obs <subcommand> [flags]
29- pup always outputs JSON. Parse directly — no content-block unwrapping (unlike MCP results, which may wrap JSON in [{"type": "text", "text": "<json>"}]).
30- If pup returns an auth error, tell the user to run pup auth login and stop.
31- Parallelization: issue multiple Bash tool calls in a single message (one pup command per call).
This is a preview of SKILL.md. Download or install the skill to get the whole thing.
In the file
SKILL.md8,823 words
Files1
LicenceMIT
Why you can read it

Nothing in a skill executes. The client loads the text and the model follows it, so a skill can be audited the way a runbook is — by reading it.

What it costs in context

Skills are not billed by the call. They are paid for in context: every token the instructions occupy is a token your code, your diff and your conversation cannot use. Here is what this one takes and when it takes it.

≈160
always loaded
The name and description, so the model knows the skill exists and when to reach for it.
15,815
on trigger
The instruction body, read only when the skill fires.
8.0%
of a 200k window
Ten skills this size would take about 80% of the window before you open a file.
050k100k150k200k context window

16k tokens, estimated from the bundle at four bytes to the token, held for the rest of the session once it triggers. Heavy. Teams tend to install this one per project rather than globally, and load it only when the job comes up.

Servers bill, skills cost

A server charges by the month. A skill charges once per session, in context, and then keeps charging it for as long as the session lives.

Before and after

The same question, put to the same model twice: once as it comes, and once with these instructions loaded.

No worked example has been published for this skill yet.

The procedure it runs

The procedure has not been set out here step by step. It is in the body of its SKILL.md: the start of it is shown above, and the whole file comes with the install.

Prose, not code

These steps are written for a model to follow, not executed by a runtime. It can still be told to skip one, and it will say so when it does.

Servers it uses

None. This skill calls no MCP servers at all.

Everything it needs is in the instructions, so it works in a project with nothing connected — the model reads the file and changes how it works with what it can already reach.

Its SKILL.md does not declare which tools it asks the agent for, so what it touches is whatever the instructions themselves ask for — read them before you install it.

What it asks for
Writes filesnot declared
Network accessnot declared

This SKILL.md carries no allowed-tools line, so neither is declared. A skill grants no permissions of its own — it can only ask for tools your client already has.

What it will not do

Every skill is narrow, and the useful ones say where they stop. These are the jobs this one is the wrong tool for.

What this skill is not for has not been published here. Nothing is implied by that: it is a section the author has not filled in.

What is in the bundle

1 file, 63.9 kB on disk. A bundle is text throughout: the instructions the model reads, plus the templates it fills in.

  • SKILL.md63.9 kB
What is not in it

No dependencies and nothing executable: a skill is text the agent reads, so the bundle is 1 file you can review in full before installing. The MIT licence covers the templates and examples as well as the instructions.

Install

Installing copies the bundle into your project. Nothing runs at install time — the files sit on disk until the model reads them.

# Skill: eval-session-classify · 16k tokens when loaded · macOS, Linux mkdir -p .claude/skills && t=$(mktemp -d) && curl -fsSL -o "$t/bundle.tar.gz" https://mcprush.com/api/skills/skill-eval-session-classify/bundle.tar.gz && tar -xzf "$t/bundle.tar.gz" -C "$t" && [ -d "$t/skill-eval-session-classify" ] && { [ ! -e .claude/skills/skill-eval-session-classify ] || { mv .claude/skills/skill-eval-session-classify "$t/.previous" && echo "Previous copy moved to $t/.previous"; }; } && mv "$t/skill-eval-session-classify" .claude/skills/ && echo "Installed .claude/skills/skill-eval-session-classify"
On Windows, or any OS with Node 20+
npx mcprush@latest skill add datadog-labs/skill-eval-session-classify

Windows PowerShell says 'running scripts is disabled on this system'? Run Set-ExecutionPolicy -Scope CurrentUser RemoteSigned once, or type npx.cmd instead of npx (codex.cmd, gemini.cmd, grok.cmd likewise). PowerShell 7 needs neither. Writes to .claude/skills/skill-eval-session-classify/ in the current project. For every project instead, run mkdir -p ~/.claude/skills && t=$(mktemp -d) && curl -fsSL -o "$t/bundle.tar.gz" https://mcprush.com/api/skills/skill-eval-session-classify/bundle.tar.gz && tar -xzf "$t/bundle.tar.gz" -C "$t" && [ -d "$t/skill-eval-session-classify" ] && { [ ! -e ~/.claude/skills/skill-eval-session-classify ] || { mv ~/.claude/skills/skill-eval-session-classify "$t/.previous" && echo "Previous copy moved to $t/.previous"; }; } && mv "$t/skill-eval-session-classify" ~/.claude/skills/ && echo "Installed ~/.claude/skills/skill-eval-session-classify" (macOS, Linux) — or add --global to the npx line. The curl line replaces .claude/skills/skill-eval-session-classify/ only after the download succeeds, so files left there by another skill of the same name, or by an older release, do not mix in. A copy already there is moved aside, not deleted, and the line prints where. It appears as agent-observability-session-classify in Gemini CLI, Codex, Copilot and Grok (its own name); Claude Code lists it by the folder name, skill-eval-session-classify.

Which clients pick it up on their own

A skill is a folder of text. Most of these read a skills folder and pick it up without being told; the rest take the same folder another way — an upload in the app, a parameter on an API call — and then choose it by its description just the same.

Claude Code.claude/skills/
Claude DesktopCustomize › Skills
ChatGPTSkills › Create › Upload
Cursor.cursor/skills/
VS Code.github/skills/
Codex CLI.agents/skills/
Gemini CLI.gemini/skills/
Grok Build.grok/skills/
Microsoft CopilotSkills › Upload a skill
PerplexityComputer › Skills › Upload a skill
DeepSeek Harness.dsh/skills/
Zed.agents/skills/
Devin Desktop.devin/skills/
Windsurf (legacy).windsurf/skills/
OpenAI Agents SDKInstructions or sandbox Skills
HTTP / APIa request parameter
This release
Versionnot versioned
Publishedno release date on file
PriceFree
Referencedatadog-labs/skill-eval-session-classify

Versions

Its author publishes no version number, so there is nothing here to pin to: what you install is the folder as it stands today. Instructions change more often than APIs do — a skill can be rewritten entirely without anything it depends on moving.

Pinning

Nothing to pin to: this skill carries no version number of its own. What you install is what the folder holds on the day you install it.

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