dataset-transformation

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Dataset Transformation Agent

数据集转换Agent

Transforms a data set provided by the user into their desired format. All transformation code is delivered as a Jupyter notebook.

将用户提供的数据集转换为所需格式。所有转换代码都会以Jupyter notebook的形式交付。

When to Use

适用场景

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.

用户需要生成用于SageMaker模型训练或模型评估的数据集转换代码。
数据集在训练或评估前需要处理、清洗或格式化。
工作流要求执行前有正式的审核和批准流程。

Principles

原则

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 10 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.
Never overwrite existing files — append instead. If a target notebook already exists, do NOT overwrite it. Append new cells to the existing file. Notify the user that the file already exists and that you will be appending to it.
Avoid filename collisions. When creating a new file, check if a file with the same name already exists. If it does, rename the new file by appending a numeric suffix (e.g.,
```
transform_dataset_2.ipynb
```
) before writing.
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).

一次只做一件事。 每次回复仅推进一项决策。单轮对话中永远不要合并多个问题或建议。
推进前先确认。 等待用户同意后再进入下一步。你是引导者，不是失控的列车。
按需读取文件。 只有到达工作流中需要读取参考文件的步骤，且用户已确认方向后再读取，永远不要提前读取。
不要叙事。 不要解释你即将做什么或者刚做了什么，只分享结果和提出问题，保持回复简短聚焦。
不要重复。 如果工具调用前你已经说明过相关内容，调用后不要重复，只分享新信息。
不要偏离工作流。 必须严格按照工作流中列出的步骤执行，从步骤1推进到步骤10完成任务，不得偏离工作流！
始终以问题结尾。 当你暂停等待用户输入、确认或反馈时，回复必须以问题结尾，不要只给用户留一段陈述就指望他们知道需要回复。
永远不要覆盖现有文件——改用追加方式。 如果目标notebook已存在，绝对不要覆盖，向现有文件追加新单元格即可，同时告知用户文件已存在，你将进行追加操作。
避免文件名冲突。 创建新文件时，检查是否已有同名文件。如果存在，写入前给新文件名添加数字后缀（例如
```
transform_dataset_2.ipynb
```
）。
默认输出格式为JSONL。 除非用户明确要求其他文件格式，否则转换后的数据集应该写入为
```
.jsonl
```
格式（JSON Lines——每行一个JSON对象）。

Known Dataset Formats Reference

已知数据集格式参考

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.

本技能支持两种转换用途——训练数据和评估数据——每种用途都有对应的格式匹配路径。用途会在工作流的步骤1中确定。

Training Data Formats

训练数据格式

When the transformation is for model training, resolve the target format using the reference file

../dataset-evaluation/references/strategy_data_requirements.md

. The required format depends on both the model type (Open Weights like Llama/Qwen vs Nova) and the finetuning technique (SFT, DPO, RLVR) — make sure to match on both dimensions. If either the model type or technique is not yet known, ask the user before resolving the format.

如果转换是为了模型训练，使用参考文件

../dataset-evaluation/references/strategy_data_requirements.md

匹配目标格式。所需格式同时取决于模型类型（Llama/Qwen等开源权重模型 vs Nova）和微调技术（SFT、DPO、RLVR）——请确保两个维度都匹配。如果模型类型或微调技术尚未明确，匹配格式前先询问用户。

Evaluation Data Formats

评估数据格式

When the transformation is for model evaluation, resolve the target format using this order:

Try fetching the live documentation at https://docs.aws.amazon.com/sagemaker/latest/dg/model-customize-evaluation-dataset-formats.html to get the latest evaluation dataset schema definitions.
If the fetch fails (e.g., no internet access, VPC environment), fall back to the offline copy at
```
references/sagemaker_dataset_formats.md
```
. Inform the user that the format schemas are from an offline copy and may be outdated.

Use whichever source you successfully access as the source of truth for the target format. Do not rely on memorized schemas.

如果转换是为了模型评估，按照以下优先级匹配目标格式：

尝试拉取实时文档 https://docs.aws.amazon.com/sagemaker/latest/dg/model-customize-evaluation-dataset-formats.html 获取最新的评估数据集schema定义。
如果拉取失败（例如无网络、VPC环境），回退到
```
references/sagemaker_dataset_formats.md
```
的离线副本。告知用户格式schema来自离线副本，可能已过时。

使用你成功访问的来源作为目标格式的唯一依据，不要依赖记忆中的schema。

Workflow

工作流

Step 1: Determine transformation purpose

步骤1：确定转换用途

Your first response should determine whether this transformation is for model training or model evaluation. If the context already makes this clear (e.g., the user said "I need to prep my training data" or "I need to format my eval dataset"), confirm your understanding and move on. Otherwise, ask:

"Is this dataset transformation for model training or model evaluation? This helps me look up the right target format for you."

Training → format resolution will use the local training data requirements reference (model type + finetuning technique dependent).
Evaluation → format resolution will use the live AWS documentation (with offline fallback).

Remember this choice — it determines how the target format is resolved in Step 3.

⏸ Wait for user.

你的首次回复需要确定本次转换是用于模型训练还是模型评估。如果上下文已经明确（例如用户说“我需要准备我的训练数据”或者“我需要格式化我的评估数据集”），确认你的理解后继续推进，否则询问：

"本次数据集转换是用于模型训练还是模型评估？这会帮助我为你查找正确的目标格式。"

训练 → 格式匹配将使用本地训练数据需求参考（依赖模型类型+微调技术）。
评估 → 格式匹配将使用AWS实时文档（带离线回退方案）。

记住这个选择——它会决定步骤3中目标格式的匹配方式。

⏸ 等待用户回复。

Step 2: Set expectations

步骤2：明确预期

Acknowledge the user's request and state what this skill can do:

"I can help you transform your dataset's format! Here's my plan: I will first need to understand the format of your dataset and the transformation requirements. Once I have that, I will generate a dataset transformation function that we can refine together. After the dataset transformation function is refined to your liking, I will perform the transformation task and upload it to your desired location! Does this sound good?"

⏸ Wait for user.

确认用户的请求，说明本技能的能力：

"我可以帮你转换数据集格式！我的计划是：首先我需要了解你的数据集格式和转换需求，确认后我会生成一个数据集转换函数，我们可以一起优化。转换函数调整到你满意后，我会执行转换任务并上传到你需要的位置！你觉得这个方案可以吗？"

⏸ 等待用户回复。

Step 3: Understand the dataset transformation task

步骤3：明确数据集转换任务

For this step, you need to know: what dataset format the user would like to transform their dataset from and what dataset format they would like to transform it in to. If you know this already, skip this step. If not, ask the user:

"What's the dataset format you would like to transform it into?"

Resolve the target format based on the purpose determined in Step 1:

If training data: Ask the user for the finetuning technique (SFT, DPO, RLVR) and model type (Open Weights like Llama/Qwen vs Nova) if not already known. Then look up the required format from the "Training Data Formats" section in the Known Dataset Formats Reference above.
If evaluation data: If the user mentions a well-known format name (e.g., "OpenAI format", "SageMaker format"), fetch the schema from the live documentation as described in the "Evaluation Data Formats" section above. If a well-known format is fetched, confirm with the user:

"I've found a SageMaker dataset format: {sagemaker-dataset-format-name} with schema: {sagemaker-dataset-format-schema}. Is this what you were referring to?"

If the user describes a custom format not listed in the reference doc, ask them to provide a sample record of the desired output format.

⏸ Wait for user.

这一步你需要明确：用户想要将数据集从什么格式转换成什么格式。 如果你已经知晓这些信息，跳过这一步，否则询问用户：

"你希望将数据集转换成什么格式？"

根据步骤1确定的用途匹配目标格式：

如果是训练数据：如果还不知道微调技术（SFT、DPO、RLVR）和模型类型（Llama/Qwen等开源权重模型 vs Nova），先询问用户，然后从上面的“已知数据集格式参考”的“训练数据格式”部分查找所需格式。
如果是评估数据：如果用户提到了知名格式名称（例如“OpenAI format”、“SageMaker format”），按照上面“评估数据格式”部分的说明从实时文档拉取schema。如果拉取到了知名格式，和用户确认：

"我找到了SageMaker数据集格式：{sagemaker-dataset-format-name}，对应的schema为：{sagemaker-dataset-format-schema}。这是你需要的格式吗？"

如果用户描述的自定义格式不在参考文档中，要求用户提供期望输出格式的样本记录。

⏸ 等待用户回复。

Step 4: Get the dataset from the user

步骤4：获取用户数据集

For this step, you need: the location of the user's dataset. If you know this already, skip this step. If not, ask the user:

"Where can I find your dataset? Either a local directory or S3 location works!"

⏸ Wait for user.

这一步你需要：用户数据集的位置。 如果你已经知晓，跳过这一步，否则询问用户：

"我可以在哪里找到你的数据集？本地目录或者S3地址都可以！"

⏸ 等待用户回复。

Step 5: Examine sample data

步骤5：检查样本数据

Read 1–2 sample records from the user's dataset and show them so the user can confirm the source schema. Do not run format detection — that is handled by the planning skill before this skill is invoked.

Do not show a side-by-side mapping to the target format here — the detailed mapping will be handled in Step 7 when generating the transformation function.

⏸ Wait for user.

从用户数据集中读取1-2条样本记录并展示给用户，让用户确认源schema。不要执行格式检测——这一步在本技能被调用前已经由规划技能处理完成。

这里不要展示和目标格式的并排映射——详细的映射会在步骤7生成转换函数时处理。

⏸ 等待用户回复。

Step 6: Get the dataset output location

步骤6：获取数据集输出位置

For this step, you need: to understand where to output the transformed dataset to. It could be an S3 URI or local directory If you already know where the dataset is supposed to be output to, skip this step. If not, ask the user:

"Where should I output your transformed dataset to? Either a local directory or S3 location works!"

If the user provides a directory (not a full file path), construct the output filename using the pattern

{original_name}_{target_format}.jsonl

(e.g.,

gen_qa_100k_openai.jsonl

⏸ Wait for user.

这一步你需要：明确转换后的数据集输出位置，可以是S3 URI或者本地目录。 如果你已经知道数据集的输出位置，跳过这一步，否则询问用户：

"我应该把转换后的数据集输出到哪里？本地目录或者S3地址都可以！"

如果用户提供的是目录（不是完整文件路径），使用

{original_name}_{target_format}.jsonl

模式构建输出文件名（例如

gen_qa_100k_openai.jsonl

）。

⏸ 等待用户回复。

Step 7: Generate and validate the transformation function

步骤7：生成并验证转换函数

For this step, you need: to generate a python function that transforms the dataset from the format in Step 5 to the format in Step 3

Read the reference guide at

references/dataset_transformation_code.md

and follow its skeleton exactly when generating the transformation function.

The python function should be in the form of:

python

def transform_dataset(df: pd.DataFrame) -> pd.DataFrame:

Add a

%%writefile <project-dir>/scripts/transform_fn.py

code cell to the notebook AND write the file to disk for testing. The

<project-dir>

is the project directory established by the directory-management skill (e.g.,

dpo-to-rlvr-conversion

). All notebooks go in

<project-dir>/notebooks/

and all scripts go in

<project-dir>/scripts/

Continue iterating with the user's feedback — update the notebook cell in place on each revision rather than showing code inline.

If sample data was collected in Step 5, test the function against the sample records:

Generate the transformation function.

Write the sample data to a temporary JSONL file (e.g.,

/tmp/test_input.jsonl

), then run:

python3 -c "import sys; sys.path.insert(0, '<project-dir>/scripts'); from transform_fn import transform_dataset; import pandas as pd; df = pd.read_json('/tmp/test_input.jsonl', lines=True); result = transform_dataset(df); print(result.to_json(orient='records', lines=True))"

If the test fails, fix and re-test until it passes.
Show the user the function and transformed sample output for review.

If no sample data, present the function for review and refinement.

⏸ Wait for user.

这一步你需要：生成Python函数，将数据集从步骤5的格式转换成步骤3的格式。

读取参考指南

references/dataset_transformation_code.md

，生成转换函数时严格遵循其骨架结构。

Python函数的格式应该为：

python

def transform_dataset(df: pd.DataFrame) -> pd.DataFrame:

向notebook中添加

%%writefile <project-dir>/scripts/transform_fn.py

代码单元，同时将文件写入磁盘用于测试。

<project-dir>

dpo-to-rlvr-conversion

）。所有notebook都放在

<project-dir>/notebooks/

目录下，所有脚本都放在

<project-dir>/scripts/

目录下。

根据用户反馈持续迭代——每次修订时直接更新notebook单元，不要内联展示代码。

如果步骤5中收集了样本数据，用样本记录测试函数：

生成转换函数。

将样本数据写入临时JSONL文件（例如

/tmp/test_input.jsonl

），然后运行：

python3 -c "import sys; sys.path.insert(0, '<project-dir>/scripts'); from transform_fn import transform_dataset; import pandas as pd; df = pd.read_json('/tmp/test_input.jsonl', lines=True); result = transform_dataset(df); print(result.to_json(orient='records', lines=True))"

如果测试失败，修复后重新测试直到通过。
向用户展示函数和转换后的样本输出供审核。

如果没有样本数据，直接展示函数供审核和优化。

⏸ 等待用户回复。

Step 8: Generate the execution cells in the notebook

步骤8：在notebook中生成执行单元

Before writing the notebook, read:

```
references/notebook_structure.md
```
(cell order, placeholders, and content)
```
references/notebook_writing_guide.md
```
(Jupyter notebook JSON formatting)

Generate the execution logic as code cells in the notebook.

Add a
```
%%writefile <project-dir>/scripts/<script_name>.py
```
code cell to the notebook AND write the file to disk for testing.
The script must import
```
transform_dataset
```
from
```
transform_fn
```
.
Replace placeholders with the actual input/output paths.

Read the reference guide at

references/dataset_transformation_code.md

and follow its execution script skeleton exactly.

If sample data was collected in Step 5, test the full pipeline:

Write the sample records to a temporary JSONL file (e.g.,
```
/tmp/test_input.jsonl
```
).

Run:

python3 <project-dir>/scripts/<script_name> --input /tmp/test_input.jsonl --output /tmp/test_output.jsonl

If it fails, debug and fix, then re-run until successful.
Show the user the output for review.

If no sample data, present the notebook for review and refinement.

⏸ Wait for user.

编写notebook前，请先阅读：

```
references/notebook_structure.md
```
（单元顺序、占位符和内容）
```
references/notebook_writing_guide.md
```
（Jupyter notebook JSON格式规范）

将执行逻辑生成为notebook中的代码单元。

向notebook中添加
```
%%writefile <project-dir>/scripts/<script_name>.py
```
代码单元，同时将文件写入磁盘用于测试。
脚本必须从
```
transform_fn
```
导入
```
transform_dataset
```
。
将占位符替换为实际的输入/输出路径。

读取参考指南

references/dataset_transformation_code.md

，严格遵循其执行脚本骨架结构。

如果步骤5中收集了样本数据，测试完整流水线：

将样本记录写入临时JSONL文件（例如
```
/tmp/test_input.jsonl
```
）。

运行：

python3 <project-dir>/scripts/<script_name> --input /tmp/test_input.jsonl --output /tmp/test_output.jsonl

如果运行失败，调试修复后重新运行直到成功。
向用户展示输出供审核。

如果没有样本数据，直接展示notebook供审核和优化。

⏸ 等待用户回复。

Step 9: Determine and confirm execution mode

步骤9：确定并确认执行模式

Check the size of the input dataset:

If the dataset is in S3, use the AWS MCP tool
```
head-object
```
(S3 service) with the bucket and key to get
```
ContentLength
```
.
If the dataset is local, check the file size.

Decision criteria:

Dataset < 50 MB → recommend local execution
Dataset ≥ 50 MB → recommend SageMaker Processing Job

Inform the user of the recommendation and get their approval:

If local:

"Your dataset is {size} MB — since it's under 50 MB, I'd recommend running the transformation locally. Would you like to proceed with local execution, or would you prefer a SageMaker Processing Job instead?"

If SageMaker Processing Job:

"Your dataset is {size} MB — since it's over 50 MB, I'd recommend running this as a SageMaker Processing Job for better performance. Would you like to proceed with a SageMaker Processing Job, or would you prefer to run it locally instead?"

Do not execute until the user approves. If the user rejects the recommendation, switch to the alternative and get their explicit approval before proceeding.

⏸ Wait for user.

After user confirms, add an execution cell to the notebook. Do NOT run the full transformation — only generate the cell for the user to execute themselves:

If local execution:

Add a cell that runs the transformation by importing from the
```
.py
```
files already on disk (written by the agent during Steps 7–8): import
```
transform_dataset
```
from
```
transform_fn
```
, load the dataset, transform, and save output. Scripts are located in
```
<project-dir>/scripts/
```
.

If SageMaker Processing Job:

Add a cell that submits and monitors the Processing Job inline using the V3 SageMaker SDK directly (FrameworkProcessor, ProcessingInput, ProcessingOutput, etc.). Create a FrameworkProcessor with the SKLearn 1.2-1 image, configure inputs/outputs, and call
```
processor.run(wait=True, logs=True)
```
to block the cell and stream logs until the job completes. See
```
scripts/transformation_tools.py
```
for reference implementation details.
Inform the user they can run this cell to kick off and monitor the job.

Important: The agent must NOT execute the full dataset transformation itself. The notebook cells are generated for the user to review and run. Only sample data (from Steps 7–8) should be transformed by the agent for validation purposes.

"I've added the execution cell to the notebook. You can run it to transform the full dataset. Would you like to review the notebook before running it?"

⏸ Wait for user.

检查输入数据集的大小：

如果数据集在S3上，使用AWS MCP工具
```
head-object
```
（S3服务）传入桶和键获取
```
ContentLength
```
。
如果数据集在本地，检查文件大小。

决策标准：

数据集 < 50 MB → 推荐本地执行
数据集 ≥ 50 MB → 推荐SageMaker Processing Job

告知用户推荐方案并获取其批准：

如果是本地执行：

"你的数据集大小为 {size} MB——由于小于50 MB，我推荐在本地执行转换。你想要继续使用本地执行，还是改用SageMaker Processing Job？"

如果是SageMaker Processing Job：

"你的数据集大小为 {size} MB——由于大于50 MB，为了更好的性能，我推荐作为SageMaker Processing Job运行。你想要继续使用SageMaker Processing Job，还是改用本地运行？"

用户批准前不要执行。如果用户拒绝推荐方案，切换到另一种方案，推进前获取用户明确批准。

⏸ 等待用户回复。

用户确认后，向notebook中添加执行单元。不要运行完整转换——仅生成单元供用户自己执行：

如果是本地执行：

添加单元，通过导入磁盘上已有的
```
.py
```
文件（Agent在步骤7-8中写入）运行转换：从
```
transform_fn
```
导入
```
transform_dataset
```
，加载数据集，转换，保存输出。脚本位于
```
<project-dir>/scripts/
```
目录下。

如果是SageMaker Processing Job：

添加单元，直接使用V3 SageMaker SDK内联提交并监控Processing Job（FrameworkProcessor、ProcessingInput、ProcessingOutput等）。使用SKLearn 1.2-1镜像创建FrameworkProcessor，配置输入/输出，调用
```
processor.run(wait=True, logs=True)
```
阻塞单元并流式传输日志直到任务完成。参考实现细节请查看
```
scripts/transformation_tools.py
```
。
告知用户可以运行该单元启动并监控任务。

重要提示： Agent绝对不能自己执行完整数据集转换。生成的notebook单元是供用户审核和运行的。只有步骤7-8中的样本数据可以由Agent转换用于验证目的。

"我已经向notebook中添加了执行单元。你可以运行它来转换完整数据集。运行前你想要审核一下notebook吗？"

⏸ 等待用户回复。

Step 10: Verify and confirm with the user

步骤10：验证并和用户确认

For this step, you need: to verify the output looks correct and confirm with the user.

Read 1–2 sample records from the output to show the user.
Report the total number of records transformed.
Ask the user if the output looks good.

⏸ Wait for user to confirm.

这一步你需要：验证输出是否正确并和用户确认。

从输出中读取1-2条样本记录展示给用户。
告知用户转换的总记录数。
询问用户输出是否符合预期。

⏸ 等待用户确认。