datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
shellcode_i_a32Shellcode_IA32 is a dataset for shellcode generation from English intents. The shellcodes are compilable on Intel Architecture 32-bits.shell-attack-evolution-dataset
Shell Honeypot Attack Request–Response Dataset
A standardized, MITRE ATT&CK–annotated dataset of post-login shell
attacks captured by Cowrie SSH/Telnet
honeypots across two collection periods — 2021–2022 and 2024. It pairs
attacker shell commands with real captured system responses, enabling both
longitudinal threat analysis and the training/evaluation of AI-driven honeypots.
This is the open-source release accompanying the paper “Unveiling Evolving
Threats: A Data Analysis… See the full description on the dataset page: https://huggingface.co/datasets/zyw-286/shell-attack-evolution-dataset.Shell-Code-Large
Shell-Code-Large
Shell-Code-Large is a large-scale corpus of Shell scripting source code comprising approximately 640,000 code samples stored in JSON Lines (.jsonl) format. The dataset is designed to support research in large language model (LLM) pretraining, code intelligence, DevOps automation, cloud infrastructure engineering, system administration, and software engineering automation.
By providing a high-volume, language-specific corpus focused exclusively on Shell scripting… See the full description on the dataset page: https://huggingface.co/datasets/ajibawa-2023/Shell-Code-Large.linux-shell-corpus-ru-en
Linux Shell RU/EN
A bilingual (Russian / English) Linux shell assistant dataset in chat format.
Overview
This dataset contains 25,000 chat-format examples with a consistent system / user / assistant structure.
The corpus started as a direct Linux command mapping dataset, but has been expanded into a broader shell-assistant training set that now includes:
direct command generation
short command sequences and pipelines
safer operational alternatives
debugging commands… See the full description on the dataset page: https://huggingface.co/datasets/NickIBrody/linux-shell-corpus-ru-en.shell-cmd-instruct
Used to train models that interact directly with shells
Note: This dataset is out-dated in the llm world, probably easier to just setup a tool with a decent model that supports tooling.
Follow-up details of my process
MacOS terminal commands for now. This dataset is still in alpha stages and will be modified.
Contains 500 somewhat unique training examples so far.
GPT4 seems like a good candidate for generating more data, licensing would need to be addressed.
I fine-tuned… See the full description on the dataset page: https://huggingface.co/datasets/byroneverson/shell-cmd-instruct.shell-script-specialist-dataset
Full-Spectrum Shell Script Specialist Dataset
This dataset contains 1,000 curated, unique ChatML conversation records engineered to fine-tune a specialist language model for Production-Grade Shell Scripting (Bash 5+, POSIX /bin/sh, jq, awk, sed).
It was used to train the rajivmehtapy/gemma-4-e4b-shell-specialist model using Unsloth.
Dataset Splits
Split
File
Records
Description
train
train.jsonl
900
Core training set across all 4 production modules… See the full description on the dataset page: https://huggingface.co/datasets/rajivmehtapy/shell-script-specialist-dataset.Shell
Shell@Educhat: Domain-Specific Implicit Risk Benchmark
Dataset Summary
Shell is a benchmark dataset dedicated to uncovering and mitigating Implicit Risks in domain-specific Large Language Models (LLMs). Unlike general safety benchmarks that focus on explicit harms, Shell focuses on deep-seated, context-dependent risks in vertical domains.
This repository hosts a curated benchmark of 750 queries, strictly stratified across three key professional domains (250 queries… See the full description on the dataset page: https://huggingface.co/datasets/feifeinoban/Shell.shellsmith-commands
shellsmith-commands
Curated (natural-language instruction → shell command) pairs for macOS/Linux,
used to fine-tune Qwen2.5-Coder-1.5B-Shellsmith.
Format
JSONL in chat format (mlx-lm / OpenAI style):
{"messages": [
{"role": "system", "content": "You are a shell command generator ..."},
{"role": "user", "content": "list files sorted by size, largest first"},
{"role": "assistant", "content": "ls -lS"}
]}
Splits
File
Rows
train.jsonl… See the full description on the dataset page: https://huggingface.co/datasets/ajayk007/shellsmith-commands.