What is agent-observability-experiment-bootstrap?

A tool for creating a repeatable experiment that tests an AI task against a versioned dataset and records evaluator scores, settings, and origin information.

Scaffolding experiments, benchmarks, regression checks, datasets, evaluators, and LLM-as-a-judge tests in Python or Node.js.

It gives developers a consistent starting structure for comparing AI behavior and tracking how results were produced.

agent-observability-experiment-bootstrap — Skill for Claude Code and C

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-bootstrap

Clone the repo

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

Made for: Claude Code, Codex.

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-bootstrap/github.svg)](https://agentmods.dev/skills/datadog-labs/agent-skills/agent-observability-experiment-bootstrap)

Your own site

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

Per session 60 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 1,843 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

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	$0.00060	$0.01843
Opus 5	$0.00030	$0.00922
Sonnet 5	$0.00012	$0.00369
Haiku 4.5	$0.00006	$0.00184

Measured 9d ago against content hash edd3948f6d95, 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-bootstrap 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 9d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (references/python/env_setup_template.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.

agent-observability/agent-observability-experiment-bootstrap/SKILL.md · 176 lines

How it starts

The opening of the file, as written. The whole thing — 176 lines — stays where its author put it; the contents beside it link to each section on GitHub.

LLM Observability Experiment Bootstrap

Generate one reproducible experiment artifact. The artifact evaluates a task over a versioned dataset, records outputs and evaluator metrics, carries configuration and provenance, and prints a result link or identifiers when possible.

This skill is adapter-independent. Each adapter owns a language-specific directory under references/; load only the selected adapter contract. The directories are intentionally symmetric even when one adapter currently has fewer supporting references.

Invocation and compatibility

The installed directory and legacy invocation remain valid:

/agent-observability-experiment-bootstrap [--purpose TEXT] [--format py|ipynb|mjs]
  [--dataset PATH | --dataset-name NAME] [--dataset-version N]
  [--project-name NAME] [--evaluator-style function|class|remote]
  [--jobs N] [--output PATH] [--task-source module:function]
  [--placeholder-task] [--app-root PATH] [--env-file PATH]

General options:

--adapter python|node             # default: python
--format py|ipynb|mjs             # Python: py/ipynb; Node: mjs
--site SITE                      # otherwise DD_SITE or datadoghq.com

Do not prompt for optional defaults. Resolve a non-empty purpose from --purpose, the request, or a focused question. Keep the purpose as reasoning context, not a fixed taxonomy.

Mandatory context loading

Load context in this order:

Parse the adapter.
Read exactly one adapter reference:
- Python SDK → references/python/python.md
- Node SDK → references/node/nodejs.md
For Python task generation, read only the selected provider reference under references/python/providers/.
For Python task generation, read only the selected evaluator reference under references/python/evaluator-styles/.

Do not load all provider, evaluator, Python, and Node references “for completeness.” The selected reference is the source of truth for syntax and API behavior.

Adapter selection

Use Python when the application or requested artifact is Python, or when no adapter is specified. Use Node when the application is JavaScript/TypeScript and the local dd-trace package exposes tracer.llmobs.experiments.

Read the full file on GitHub · 176 lines

Files

What ships with it

13 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.

9d ago First seen · 176 lines · 60 tokens per session scan A edd3948f6d95

Subscribe to this mod's changes

agent-observability-experiment-bootstrap is a skill published in the GitHub repository datadog-labs/agent-skills (166 stars, last pushed 13d ago), licensed MIT. It adds 60 tokens to every session and 1,843 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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