awslabs/agent-plugins is a collection of plugins, skills, instructions, an MCP server, a hook, and a setting that guide AI coding agents in architecting, deploying, and operating software on Amazon Web Services. It is for developers using coding agents such as Claude Code, Codex, and Cursor with AWS projects. The catalogue entries are the repository's own agent workflows and supporting components.
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.
npx skills add awslabs/agent-plugins --skill dataset-transformationgit clone --depth 1 https://github.com/awslabs/agent-pluginsWrote 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.
[](https://agentmods.dev/skills/awslabs/agent-plugins/dataset-transformation)<a href="https://agentmods.dev/skills/awslabs/agent-plugins/dataset-transformation"><img src="https://agentmods.dev/badge/skills/awslabs/agent-plugins/dataset-transformation/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.
<a href="https://agentmods.dev/skills/awslabs/agent-plugins/dataset-transformation"><img src="https://agentmods.dev/badge/skills/awslabs/agent-plugins/dataset-transformation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00110 | $0.03090 |
| Opus 5.5 | $0.00044 | $0.01236 |
| Sonnet 5.5 | $0.00022 | $0.00618 |
| Haiku 4.5 | $0.00011 | $0.00309 |
Grade A, and why
dataset-transformation 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 1mo 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- dataset-transformation — 91% identical, 22 lines differ
How it starts
The opening of the file, as written. The whole thing — 236 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Dataset Transformation Agent
Transforms a data set provided by the user into their desired format.
When to Use
- User needs to generate code for transforming datasets for SageMaker model training or model evaluation.
- A dataset requires processing, cleaning, or formatting before training or evaluation.
- Workflow requires a formal review and approval cycle before execution.
Prerequisites
- The SDK environment has been verified (SDK version, region, execution role). If not done, activate the
sdk-getting-startedskill first.
Principles
- One thing at a time. Each response advances exactly one decision. Never combine multiple questions or recommendations in a single turn.
- Confirm before proceeding. Wait for the user to agree before moving to the next step. You are a guide, not a runaway train.
- Don't read files until you need them. Only read reference files when you've reached the workflow step that requires them and the user has confirmed the direction. Never read ahead.
- No narration. Don't explain what you're about to do or what you just did. Share outcomes and ask questions. Keep responses short and focused.
- No repetition. If you said something before a tool call, don't repeat it after. Only share new information.
- Do not deviate from the Workflow. The steps listed in the workflow should be followed exactly as described. Progress from Step 1 to Step 11 to complete the task. Do not deviate from the workflow!
- Always end with a question. Whenever you pause for user input, acknowledgment, or feedback, your response must end with a question. Never leave the user with a statement and expect them to know they need to respond.
- Default output format is JSONL. Unless the user explicitly requests a different file format, the transformed dataset should be written as
.jsonl(JSON Lines — one JSON object per line).
Known Dataset Formats Reference
This skill supports two transformation purposes — training data and evaluation data — each with its own format resolution path. The purpose is determined in Step 1 of the workflow.
What ships with it
5 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.
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.
- 1mo ago First seen · 236 lines · 110 tokens per session scan A e41de7752b85
dataset-transformation is a skill published in the GitHub repository awslabs/agent-plugins (924 stars, last pushed 6d ago), licensed Apache-2.0. It adds 110 tokens to every session and 3,090 once invoked, about $0.0004 per session on Opus 5.5. 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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