A coaching command for creating a trusted evaluation dataset for an AI agent, deploying the agent, and handing the result to an ML engineer for production use with SageMaker and MLflow.

Use it to build labeled evaluation data, deploy the agent, and prepare it for a SageMaker machine-learning pipeline tracked with MLflow.

It organizes the handoff from domain experts to machine-learning engineers and makes the required stages explicit.

gedd — Command for Claude Code by aws-samples/sample-GEDD

About the project

GEDD is a framework for evaluating AI-product responses by turning judgments from domain experts and product managers into repeatable tests and release gates. Teams use it to identify failures, calibrate LLM judges, and improve customer-facing assistants through an offline-online feedback loop. The catalogue entries provide commands for carrying out this evaluation workflow.

aws-samples/sample-GEDD · 13 stars · on GitHub

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.

Clone the repo

git clone --depth 1 https://github.com/aws-samples/sample-GEDD

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/commands/aws-samples/sample-gedd/gedd/github.svg)](https://agentmods.dev/commands/aws-samples/sample-gedd/gedd)

Your own site

<a href="https://agentmods.dev/commands/aws-samples/sample-gedd/gedd"><img src="https://agentmods.dev/badge/commands/aws-samples/sample-gedd/gedd/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/commands/aws-samples/sample-gedd/gedd"><img src="https://agentmods.dev/badge/commands/aws-samples/sample-gedd/gedd.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 0 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.

When invoked 1,710 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.

Origin unknown 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	$0.00000	$0.01710
Opus 5.5	$0.00000	$0.00684
Sonnet 5.5	$0.00000	$0.00342
Haiku 4.5	$0.00000	$0.00171

Measured 2mo ago against content hash dd220ef06e94, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

gedd 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 2mo ago.

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.

grounded-evals/.claude/commands/gedd.md · 217 lines

The source is not reproduced here

Licensed MIT-0

The repository is licensed MIT-0, which this catalogue does not treat as permission to reproduce the file. Read it at the source.

Read it on GitHub

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.

2mo ago First seen · 217 lines · 0 tokens per session scan A dd220ef06e94

Subscribe to this mod's changes

gedd is a command published in the GitHub repository aws-samples/sample-GEDD (13 stars, last pushed 2d ago), licensed MIT-0. It costs nothing until one of its globs matches a file; then it loads 1,710 tokens. 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.

Building agents Data and AI ai-agents ai-quality ai-testing amazon-bedrock eval-framework grounded-theory

gedd next to 6 commands from other repositories
Mod	Type	Stars	Last push	Licence	Scan	Tokens	Works with
geddaws-samples/sample-GEDD	Command	13	2d ago	MIT-0	A	0	Claude Code
installaws-samples/sample-claude-code-multi-model	Command	11	3mo ago	MIT-0	A	0	Claude Code
bootmicrosoft/PromptKit	Command	113	9d ago	MIT	A	14	GitHub Copilot
music-suno-promptfrankxai/Starlight-Intelligence-System	Command	10	yesterday	MIT	A	23	Claude Code
audit-promptOwl-Listener/ai-design-skills	Command	199	5mo ago	MIT	A	13	Claude Code
laravel-ai-sdkjpcaparas/superpowers-laravel	Command	157	4mo ago	MIT	A	27	Claude Code
vlm-ocr-evaluationscdenney/open-science-skills	Command	69	yesterday	none	A	0	Claude Code