license: cc-by-4.0
task_categories:
- text-generation
- question-answering
language:
- en
pretty_name: ShellOps
tags:
- cli-agents
- reinforcement-learning
- filesystem
- shell
- verifiable-evaluation
ShellOps
ShellOps is a verifiable dataset suite for CLI agents that interact with filesystem workspaces through executable shell actions. It accompanies the paper Learning CLI Agents with Structured Action Credit under Selective Observation and the code release at Agentic-RL-A3.
The dataset focuses on shell-driven information extraction and file editing tasks, where an agent receives a natural language instruction, observes an initial workspace, executes CLI actions, and is scored by verifiable terminal output or post-execution file state.
Dataset Overview
- ShellOps: 1624 standard CLI tasks across training and evaluation splits
- ShellOps-Pro: 150 harder out-of-distribution tasks for infer-only evaluation
- Task axes: Lookup, Aggregate, Edit, and Mixed
- Workspace setting: repository-like file trees with structured data, logs, code, prose, configuration files, and extensionless files
- Evaluation target: exact stdout, file-state match, or hybrid output plus file-state objectives
File Layout
shellops/
train_src.parquet
train.parquet
test.parquet
assets/
shellops_pro/
test.parquet
assets/
In shellops, train_src.parquet is the full training-side corpus claimed in the paper, while train.parquet is the sampled subset used for the reported training runs. The train_src.parquet and test.parquet files together form the 1624-task ShellOps corpus.
shellops_pro is an OOD infer-only dataset, so only test.parquet is provided. If you use our TEST MODE workflow, create a placeholder train.parquet in shellops_pro/ to keep the file layout consistent. This placeholder parquet is not used during inference.
Citation
@misc{su2026learningcliagentsstructured,
title={Learning CLI Agents with Structured Action Credit under Selective Observation},
author={Haoyang Su and Ying Wen},
year={2026},
eprint={2605.08013},
archivePrefix={arXiv},
primaryClass={cs.AI},
url={https://arxiv.org/abs/2605.08013},
}