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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](https://arxiv.org/abs/2605.08013) and the code release at [Agentic-RL-A3](https://github.com/Hoyant-Su/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
```text
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
```bibtex
@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},
}
```
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