SkillGrad: Optimizing Agent Skills Like Gradient Descent
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| Authors | Hanyu Wang et al. |
| Year | 2026 |
| HF Upvotes | 25 |
| arXiv | 2605.27760 |
| Download | |
| HF Page | View on Hugging Face |
Abstract
Agent skills provide a lightweight way to adapt LLM agents to specialized domains by storing reusable procedural knowledge in structured files. However, whether downloaded from third parties or self-generated, these skills are often unreliable, incomplete, or outdated. Existing skill-evolution methods often address these deficiencies through heuristic reflections without an explicit optimization formulation. In this paper, we propose SkillGrad, a gradient-descent-inspired framework for optimizing agent skills. SkillGrad treats the skill package as a structured parameter to optimize in a gradient descent fashion: task executions provide trajectory-level loss evidence, automatic diagnoses then provide text-based gradients that indicate the correction directions. To stabilize optimization across iterations, a momentum agent accumulates recurring diagnostic patterns into a persistent memory overlay. Finally, an LLM-based patcher executes the parameter update by applying layer-aware edits to the skill package. Evaluated on SpreadsheetBench Verified and WikiTableQuestions, SkillGrad consistently outperforms training-based skill evolution baselines across two backbone LLMs, improving over the strongest training-based baseline by 6.7 percentage points on average. Ablations further show that momentum and contrastive diagnosis both contribute to the final skill quality.
Engineering Breakdown
The Problem
However, whether downloaded from third parties or self-generated, these skills are often unreliable, incomplete, or outdated.
The Approach
In this paper, we propose SkillGrad, a gradient-descent-inspired framework for optimizing agent skills.
Key Results
Ablations further show that momentum and contrastive diagnosis both contribute to the final skill quality.
Research Areas
This paper contributes to the following areas of AI/ML engineering:
- Machine learning
- Deep learning
- Neural networks
- Model optimization
- AI systems
- Skillgrad
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