# Toward Scalable Terminal Task Synthesis via Skill Graphs

arXiv: https://arxiv.org/abs/2604.25727 · HTML: https://mlsanity.com/paper/2604.25727/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 25 references that are in the map.
- First posted on arXiv 28 Apr 2026.

## Builds on (most cited first, 14 of 14)

- [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025)
- [SWE-bench: Can Language Models Resolve Real-World GitHub Issues?](https://mlsanity.com/paper/2310.06770/) (2023)
- [Voyager: An Open-Ended Embodied Agent with Large Language Models](https://mlsanity.com/paper/2305.16291/) (2023)
- [OpenHands: An Open Platform for AI Software Developers as Generalist Agents](https://mlsanity.com/paper/2407.16741/) (2024)
- [Terminal-Bench: Benchmarking Agents on Hard, Realistic Tasks in Command Line Interfaces](https://mlsanity.com/paper/2601.11868/) (2026)
- [SWE-smith: Scaling Data for Software Engineering Agents](https://mlsanity.com/paper/2504.21798/) (2025)
- [InterCode: Standardizing and Benchmarking Interactive Coding with Execution Feedback](https://mlsanity.com/paper/2306.14898/) (2023)
- [NL2Bash: A Corpus and Semantic Parser for Natural Language Interface to the Linux Operating System](https://mlsanity.com/paper/1802.08979/) (2018)
- [Organizing, Orchestrating, and Benchmarking Agent Skills at Ecosystem Scale](https://mlsanity.com/paper/2603.02176/) (2026)
- [Endless Terminals: Scaling RL Environments for Terminal Agents](https://mlsanity.com/paper/2601.16443/) (2026)
- [On Data Engineering for Scaling LLM Terminal Capabilities](https://mlsanity.com/paper/2602.21193/) (2026)
- [Large-Scale Terminal Agentic Trajectory Generation from Dockerized Environments](https://mlsanity.com/paper/2602.01244/) (2026)
- [SWE-Bench++: A Framework for the Scalable Generation of Software Engineering Benchmarks from Open-Source Repositories](https://mlsanity.com/paper/2512.17419/) (2025)
- [CLI-Gym: Scalable CLI Task Generation via Agentic Environment Inversion](https://mlsanity.com/paper/2602.10999/) (2026)

## Built on by (most cited first, 22 of 22)

- [CLI-Universe: Towards Verifiable Task Synthesis Engine for Terminal Agents](https://mlsanity.com/paper/2606.22883/) (2026)
- [SPADE: Self-Play in Adaptive Synthetic Executable Environments](https://mlsanity.com/paper/2608.19197/) (2026)
- [FACET: Preserving Source Intent and Executable State in Terminal Task Synthesis](https://mlsanity.com/paper/2608.18580/) (2026)
- [CalibForge: Adversarial Solver Calibration for Scaling Learnable Terminal Tasks](https://mlsanity.com/paper/2608.06352/) (2026)
- [Terminal-Universe: Turning Agent Trajectories into Scalable Terminal Environments](https://mlsanity.com/paper/2609.04148/) (2026)
- [NexForge: Scaling Agent Capabilities through Requirement-Driven Task Synthesis for LLMs](https://mlsanity.com/paper/2607.14186/) (2026)
- [T1: Terminal Agent Reinforcement Learning for Long-Horizon Tasks](https://mlsanity.com/paper/2609.11042/) (2026)
- [Meta-Task: Turning Terminal Task Synthesis into a Terminal Task for Scalable Agent Training](https://mlsanity.com/paper/2607.27929/) (2026)
- [Envs-FORGE: Frontier-Optimized Reward-Grounded Environment Synthesis for Agent RL](https://mlsanity.com/paper/2608.14312/) (2026)
- [SKT: Skill-Use Training at Scale via Verified Synthetic Data Generation](https://mlsanity.com/paper/2608.02287/) (2026)
- [What Makes Good Agentic Data? An ACE Lens on Data Generation for LLM Agents](https://mlsanity.com/paper/2608.27260/) (2026)
- [Learning Generalizable Behaviors for Terminal Agents](https://mlsanity.com/paper/2608.22631/) (2026)
- [Environment Evolution for Terminal Agents](https://mlsanity.com/paper/2609.04128/) (2026)
- [Towards a Systems Foundation for Agentic Skills: Architecture, Lifecycle, and Security](https://mlsanity.com/paper/2608.29596/) (2026)
- [Skill2Env: Capability-Oriented Environment Synthesis from Skills for General Agents](https://mlsanity.com/paper/2609.33772/) (2026)
- [SkillGym: Training Skill-Use Agents with Automatic Verifiable Environment Generation](https://mlsanity.com/paper/2609.37539/) (2026)
- [GraphForge: Training Working Agents with Graph-Anchored Workspace Synthesis](https://mlsanity.com/paper/2609.38923/) (2026)
- [Scaling Verifiable Environments for Long-horizon Work Agents](https://mlsanity.com/paper/2610.04906/) (2026)
- [Scaling Agentic Data for Long-Horizon Terminal Intelligence](https://mlsanity.com/paper/2610.13166/) (2026)
- [WorkBorne: Discovering Agentic Tasks from Real-World Artifact Transitions](https://mlsanity.com/paper/2610.14915/) (2026)
- [AssetForge: Asset-Augmented Task Synthesis for Business Automation Agents](https://mlsanity.com/paper/2610.18330/) (2026)
- [Interlock: From Local Correctness to Task Completion in Long-Horizon Agents](https://mlsanity.com/paper/2610.19686/) (2026)

## Abstract (arXiv)

Terminal agents have demonstrated strong potential for autonomous command-line execution, yet their training remains constrained by the scarcity of high-quality and diverse execution trajectories. Existing approaches mitigate this bottleneck by synthesizing large-scale terminal task instances for trajectory sampling. However, they primarily focus on scaling the number of tasks while providing limited control over the diversity of execution trajectories that agents actually experience during training. In this paper, we present SkillSynth, an automated framework for terminal task synthesis built on a scenario-mediated skill graph. SkillSynth first constructs a large-scale skill graph, where scenarios serve as intermediate transition nodes that connect diverse command-line skills. It then samples paths from this graph as abstractions of real-world workflows, and uses a multi-agent harness to instantiate them into executable task instances. By grounding task synthesis in graph-sampled workflow paths, SkillSynth explicitly controls the diversity of minimal execution trajectories required to solve the synthesized tasks. Experiments on Terminal-Bench demonstrate the effectiveness of SkillSynth. Moreover, task instances synthesized by SkillSynth have been adopted to train Hy3 Preview, contributing to its enhanced agentic capabilities in terminal-based settings.