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SkillGenBench: Benchmarking Skill Generation Pipeli... | AI Research

Key Takeaways

SkillGenBench is a new benchmark designed to evaluate how well AI models can turn raw information—such as software code repositories or long technical docume...
Existing benchmarks primarily evaluate the efficacy of given skills or the ability of agents to solve downstream tasks from raw context, but they do not isolate skill generation itself as the object of study.
We introduce SkillGenBench, a benchmark for evaluating skill generation pipelines under a unified and controlled protocol.
In SkillGenBench, a generator receives raw corpora and produces standardized skill artifacts, which are then executed under fixed harnesses and assessed with unified evaluation procedures.
We provide standardized task specifications, pinned environments, and evaluation protocols centered on deterministic execution-based checks, supplemented by auxiliary signals for diagnosis.

Paper AbstractExpand

As LLM agents are increasingly built around reusable skills, a central challenge is no longer only whether agents can use provided skills, but whether they can generate correct, reusable, and executable skills from repositories and documents. Existing benchmarks primarily evaluate the efficacy of given skills or the ability of agents to solve downstream tasks from raw context, but they do not isolate skill generation itself as the object of study. We introduce SkillGenBench, a benchmark for evaluating skill generation pipelines under a unified and controlled protocol. In SkillGenBench, a generator receives raw corpora and produces standardized skill artifacts, which are then executed under fixed harnesses and assessed with unified evaluation procedures. The benchmark covers two generation regimes: task-conditioned generation, where a task-specific skill is synthesized after the task is revealed, and task-agnostic generation, where a reusable skill library must be distilled before downstream tasks are known. It also spans two complementary procedural sources: repository-grounded instances, where procedures are distributed across code, configuration, and scripts, and document-grounded instances, where procedures and constraints must be distilled from long-form text. We provide standardized task specifications, pinned environments, and evaluation protocols centered on deterministic execution-based checks, supplemented by auxiliary signals for diagnosis. Experiments across a range of skill-generation methods and backbones show substantial performance variation, highlight the difficulty of reusable skill distillation, and reveal distinct failure modes in skill generation from software repositories versus long-form documents. SkillGenBench establishes a reproducible testbed for studying skill generation as an independent research problem in agent systems.

SkillGenBench is a new benchmark designed to evaluate how well AI models can turn raw information—such as software code repositories or long technical documents—into "skills." As AI agents become more advanced, they are increasingly built using reusable procedural artifacts (skills) rather than just relying on simple prompts. This paper argues that current benchmarks focus too much on whether an agent can use a skill, rather than whether the agent can successfully create a correct, executable skill in the first place. SkillGenBench provides a controlled, reproducible environment to test these "skill generation pipelines" as independent components.

How the Benchmark Works

The benchmark treats the skill generator as the primary object of study. It provides the generator with raw source materials and asks it to produce a standardized skill artifact. This artifact is then tested in a fixed, containerized environment to see if it actually works. The benchmark covers two main ways of generating skills:

Task-conditioned generation: The model creates a skill specifically to solve a task it has already been shown.
Task-agnostic generation: The model must distill a library of reusable skills from raw data before it even knows what tasks it will be asked to perform later.
The benchmark also tests two types of source material: "repository-grounded" instances, where procedures are hidden across code and configuration files, and "document-grounded" instances, where procedures must be extracted from long-form text.

Evaluating Performance

To ensure fairness and reproducibility, SkillGenBench uses a strict evaluation protocol. It uses deterministic, execution-based checks to see if the generated skill performs correctly. In cases where there isn't a single "right" answer, it uses artifact-based evaluation, comparing the output of the skill against a reference using methods like semantic similarity or LLM-based judging. The benchmark includes 187 tasks across various domains, all validated to ensure they are challenging enough to require proper procedural extraction.

Key Findings

Experiments conducted across several different AI models revealed significant performance gaps. The researchers found that:

Code is harder than documents: Models consistently struggled more with repository-grounded tasks (10.8%–14.4% success) compared to document-grounded tasks (21.4%–25.0%). This is likely because repository tasks require the model to infer implicit workflows from scattered code and scripts.
Skills aren't always helpful: The study found that automatically generated skills are not universally beneficial. If a skill is poorly generated, it can introduce incorrect assumptions or interface inconsistencies that actually make the agent perform worse than if it had no skill at all.
Method sensitivity: The effectiveness of a skill-generation pipeline depends heavily on the interaction between the specific generation method used and the underlying AI model. No single method was a "silver bullet" across all scenarios.

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