arxiv: 2604.25727 · v1 · submitted 2026-04-28 · 💻 cs.AI

Recognition: unknown

Toward Scalable Terminal Task Synthesis via Skill Graphs

Zhiyuan Fan , Tinghao Yu , Yuanjun Cai , Jiangtao Guan , Yun Yang , Dingxin Hu , Jiang Zhou , Xing Wu

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Zhuo Han Feng Zhang Lilin Wang

Authors on Pith no claims yet

Pith reviewed 2026-05-07 16:08 UTC · model grok-4.3

classification 💻 cs.AI

keywords terminal agentstask synthesisskill graphsworkflow pathsexecution trajectoriesmulti-agent harnesscommand-line tasksagent training

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The pith

SkillSynth builds a scenario-mediated skill graph and samples workflow paths to synthesize terminal tasks with controlled diversity in minimal execution trajectories.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents SkillSynth as a framework that first assembles a large skill graph in which scenarios function as transition nodes linking many command-line skills. Paths are then sampled from the graph to stand in for real-world workflows and turned into concrete executable tasks by a multi-agent harness. This grounding in graph paths gives explicit control over the variety of shortest execution sequences that agents must produce to solve the tasks. A sympathetic reader would care because prior synthesis methods mainly increase task volume while leaving the range of experienced trajectories largely uncontrolled, limiting what agents can learn from the data.

Core claim

SkillSynth is an automated framework for terminal task synthesis built on a scenario-mediated skill graph. The framework 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, and the generated task

What carries the argument

The scenario-mediated skill graph, in which scenarios act as intermediate nodes connecting command-line skills, so that sampled paths serve as abstractions of workflows and directly determine the diversity of minimal execution trajectories in the resulting tasks.

If this is right

Agents trained on the synthesized tasks encounter a wider range of minimal execution trajectories than under quantity-focused synthesis.
Task generation becomes scalable while retaining explicit control over trajectory diversity.
The same graph-sampling process can be reused to produce additional task sets without redesigning the synthesis pipeline.
Tasks generated this way have already been used to improve the terminal performance of a deployed agent preview.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The graph structure could support curriculum-style training by ordering paths according to increasing length or number of distinct skills.
Because paths are explicit abstractions, the method might make it easier to diagnose which kinds of workflows an agent still fails to handle.
If the skill graph is updated with new scenarios, the same sampling machinery could generate tasks for evolving command-line environments without starting from scratch.

Load-bearing premise

Building a scenario-mediated skill graph and sampling paths from it produces tasks whose minimal execution trajectories are diverse and representative enough to improve agent performance over earlier synthesis methods, and the multi-agent harness can turn those abstract paths into executable instances without adding artifacts.

What would settle it

Train agents on SkillSynth tasks and on tasks from prior synthesis methods, then measure both success rates and the actual diversity of minimal trajectories on Terminal-Bench; if the SkillSynth agents show no measurable gain in either metric, or if many generated instances contain non-executable artifacts, the claim is falsified.

Figures

Figures reproduced from arXiv: 2604.25727 by Dingxin Hu, Feng Zhang, Jiangtao Guan, Jiang Zhou, Lilin Wang, Tinghao Yu, Xing Wu, Yuanjun Cai, Yun Yang, Zhiyuan Fan, Zhuo Han.

**Figure 2.** Figure 2: Overview of SkillSynth. (a) A compositional path P of scenarios σ and skills κ (highlighted in orange) is sampled from the scenario-mediated skill graph. (b) A multi-agent harness instantiates P into the components of a task instance through a planner and a tool-augmented synthesis agent, followed by dual verification that checks solvability (execution-based) and specification quality (rubric-based); fai… view at source ↗

**Figure 1.** Figure 1: Diversity of synthesized trajectories across datasets, measured by the number of unique scenarios, skills, and (scenario, skill) pairs after semantic canonicalization. Each value is averaged over three independent samples of 1,000 trajectories per dataset. As empirically shown in view at source ↗

**Figure 3.** Figure 3: Overview of the skill graph construction pipeline. view at source ↗

**Figure 4.** Figure 4: A video-domain example of a path sampled from the skill graph and the corresponding view at source ↗

**Figure 5.** Figure 5: Skill category distribution. 10 0 10 1 10 2 10 3 Node degree 10 −5 10 −4 10 −3 10 −2 10 −1 10 0 CCDF mean 4.32 median 2 max 752 view at source ↗

**Figure 6.** Figure 6: Degree distribution of the skill graph. The degree distribution ( view at source ↗

read the original abstract

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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

SkillSynth adds a skill-graph layer to terminal task synthesis so you can sample workflow paths and control trajectory diversity, and the outputs have already gone into training Hy3 Preview.

read the letter

The paper's main move is to build a large skill graph where scenario nodes sit between command-line skills, then sample paths through the graph as workflow abstractions and hand them to a multi-agent harness for turning into executable tasks. This is positioned as a step past prior work that mostly just scaled up the number of tasks without much say over which execution sequences the agent actually sees during training.

Referee Report

1 major / 0 minor

Summary. The paper introduces SkillSynth, an automated framework for terminal task synthesis built on a scenario-mediated skill graph. It constructs a large-scale skill graph with scenarios as intermediate transition nodes connecting diverse command-line skills, 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, it claims to explicitly control the diversity of minimal execution trajectories. Experiments on Terminal-Bench are reported to demonstrate effectiveness, and the synthesized tasks have been adopted to train Hy3 Preview for enhanced agentic capabilities.

Significance. If the results hold, this framework offers a promising way to scale the generation of diverse training trajectories for terminal agents, potentially overcoming limitations of existing synthesis methods that focus mainly on quantity rather than controlled diversity. The adoption into Hy3 Preview training provides some evidence of practical value, but without detailed metrics, the significance remains difficult to quantify.

major comments (1)

Abstract: The claim that 'Experiments on Terminal-Bench demonstrate the effectiveness of SkillSynth' is not supported by any metrics, baselines, ablation details, or quantitative results on trajectory diversity or agent performance gains. This is load-bearing for the central contribution, as it prevents evaluation of whether graph-based path sampling meaningfully improves upon prior task synthesis approaches.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address the major comment point-by-point below and will incorporate revisions to improve clarity and support for our claims.

read point-by-point responses

Referee: Abstract: The claim that 'Experiments on Terminal-Bench demonstrate the effectiveness of SkillSynth' is not supported by any metrics, baselines, ablation details, or quantitative results on trajectory diversity or agent performance gains. This is load-bearing for the central contribution, as it prevents evaluation of whether graph-based path sampling meaningfully improves upon prior task synthesis approaches.

Authors: We agree that the abstract statement is too high-level and does not provide immediate quantitative grounding for the effectiveness claim. Our experiments section does report results on Terminal-Bench, including comparisons of trajectory diversity (via path coverage and minimal execution length metrics) against prior synthesis baselines, as well as downstream agent performance when training on the synthesized tasks. To address the concern directly, we will revise the abstract to include concise references to these key metrics and baseline comparisons, ensuring the claim is explicitly supported by the reported findings. revision: yes

Circularity Check

0 steps flagged

No significant circularity; framework is a direct construction

full rationale

The paper presents SkillSynth as an explicit pipeline: construct a scenario-mediated skill graph, sample workflow paths, and instantiate tasks via multi-agent harness. The central claim that this controls diversity of minimal execution trajectories follows directly from the graph-sampling design choice itself, without any fitted parameters, self-referential predictions, or reductions to prior self-citations. No equations, uniqueness theorems, or ansatzes are invoked that collapse back to the inputs. The derivation chain is self-contained as a proposed engineering framework rather than a mathematical result that must be verified against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 2 invented entities

The central claim depends on the domain assumption that skill graphs with scenario nodes can faithfully abstract real-world terminal workflows and that path sampling yields controllable diversity; no free parameters or invented entities with external evidence are specified.

axioms (1)

domain assumption Scenarios can serve as meaningful intermediate nodes that connect diverse command-line skills into realistic workflow abstractions.
This underpins the graph construction and path sampling steps described in the abstract.

invented entities (2)

SkillSynth framework no independent evidence
purpose: Automated synthesis of terminal tasks via skill graphs
Newly proposed system whose effectiveness is asserted but not independently evidenced in the abstract.
scenario-mediated skill graph no independent evidence
purpose: Structure for connecting skills and sampling workflow paths
Core conceptual invention enabling the claimed control over trajectory diversity.

pith-pipeline@v0.9.0 · 5514 in / 1403 out tokens · 149404 ms · 2026-05-07T16:08:54.441443+00:00 · methodology

discussion (0)

Reference graph

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