What is agent-observability-experiment-analyzer?

An analysis tool for LLM experiments, meaning tests that measure how an AI system performs. It can examine one experiment or compare two experiments, including against a baseline, which is a reference result.

Use it to analyze an experiment, compare experiments, or assess an experiment against a baseline.

It helps turn experiment results into an analysis instead of making you compare IDs and results by hand. It supports both open-ended investigation and question-based analysis.

agent-observability-experiment-analyzer — Skill for Claude Code by dat

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent

npx skills add datadog-labs/agent-skills --skill agent-observability-experiment-analyzer

Clone the repo

git clone --depth 1 https://github.com/datadog-labs/agent-skills

Made for: Claude Code.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

README.md

[![agentmods](https://agentmods.dev/badge/skills/datadog-labs/agent-skills/agent-observability-experiment-analyzer/github.svg)](https://agentmods.dev/skills/datadog-labs/agent-skills/agent-observability-experiment-analyzer)

Your own site

<a href="https://agentmods.dev/skills/datadog-labs/agent-skills/agent-observability-experiment-analyzer"><img src="https://agentmods.dev/badge/skills/datadog-labs/agent-skills/agent-observability-experiment-analyzer/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

Your own site · 80×15

<a href="https://agentmods.dev/skills/datadog-labs/agent-skills/agent-observability-experiment-analyzer"><img src="https://agentmods.dev/badge/skills/datadog-labs/agent-skills/agent-observability-experiment-analyzer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 56 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 6,114 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits

Socket pass 19 Jun 2026
Snyk pass 19 Jun 2026
NVIDIA SkillSpector pass 7 Sept 2026

How audits are shown

Origin original No closer match found in the catalogue.

Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

Model	Per session	Once invoked
Fable 5.1	$0.00056	$0.06114
Opus 5	$0.00028	$0.03057
Sonnet 5	$0.00011	$0.01223
Haiku 4.5	$0.00006	$0.00611

Measured 10d ago against content hash 4a6e518cf60b, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

agent-observability-experiment-analyzer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.

The scan reads SKILL.md. This mod also ships 3 executable files (evals/__init__.py, evals/evaluator.py, evals/executor.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

Copies of this mod

1 near-identical copy found in the catalogue:

agent-observability-experiment-analyzer — 100% identical, 4 lines differ

agent-observability/agent-observability-experiment-analyzer/SKILL.md · 443 lines

How it starts

The opening of the file, as written. The whole thing — 443 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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 a tool named get_llmobs_experiment_summary (with or without the mcp__datadog-llmo-mcp__ prefix) appears in your available tools.
If MCP tools are present → use MCP mode throughout. Tool name binding: note the exact name under which get_llmobs_experiment_summary appears in your active tool list and use that exact name (prefixed or unprefixed) for every MCP tool call this invocation. All other experiment tools follow the same naming convention.
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/api/unstable/mcp-server/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.

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.
Parallelization: issue multiple Bash tool calls in a single message (one pup command per call).

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.

Intent tagging: On every MCP tool call, prefix telemetry.intent with skill:agent-observability-experiment-analyzer[<inv_id>] — followed by a description of why the tool is being called. On the first MCP tool call only, use skill:agent-observability-experiment-analyzer:start[<inv_id>] — instead (note the :start suffix). Example first call: skill:agent-observability-experiment-analyzer:start[3a9f1c2b] — Phase 1: get experiment summary to orient analysis

Read the full file on GitHub · 443 lines

Files

What ships with it

7 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

10d ago First seen · 443 lines · 56 tokens per session scan A 4a6e518cf60b

Subscribe to this mod's changes

agent-observability-experiment-analyzer is a skill published in the GitHub repository datadog-labs/agent-skills (166 stars, last pushed 14d ago), licensed MIT. It adds 56 tokens to every session and 6,114 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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