SkillRevise: Improving LLM-Authored Agent Skills via Trace-Conditioned Skill Revision
SkillRevise improves LLM agent success rate from 36.05% to 61.63% via execution-conditioned skill revision.
Key Findings
Methodology
SkillRevise is an execution-grounded framework designed to iteratively refine initial skills by diagnosing skill defects from execution evidence, retrieving relevant repair principles from a general memory, and applying execution-anchored edits. By re-executing candidates and measuring empirical utility, it retains the best observed skill within the revision budget.
Key Results
- On SkillsBench, SkillRevise improves the base agent's success rate from 36.05% to 61.63%.
- SkillRevise shows consistent improvements across multiple executors on SkillLearnBench-Random and SWE-Skills-Bench-Hard.
- Compared to one-shot generated skills, SkillRevise demonstrates significant performance enhancements across six LLMs.
Significance
SkillRevise addresses the challenge of skill construction in cold-start settings by providing a systematic method for skill revision. It not only enhances the success rate of LLM agents but also demonstrates the transferability of skills across different executors and task environments, indicating its capture of reusable procedural knowledge beyond any single executor.
Technical Contribution
SkillRevise's technical contribution lies in its execution-anchored revision approach, which combines task-specific diagnosis, reusable principle memory, and utility-gated selection to effectively improve existing LLM-authored skills within a bounded revision budget.
Novelty
SkillRevise is the first to combine execution evidence with general repair knowledge for skill revision, filling a gap in existing methods for cold-start scenarios.
Limitations
- SkillRevise may be limited in its revision effectiveness for long-horizon tasks.
- Generic principles may be insufficient for effective repair in domain-specific skill design defects.
Future Work
Future work can explore the application of SkillRevise in more domains and complex tasks, as well as further optimization of the revision process to enhance efficiency and utility.
AI Executive Summary
SkillRevise is an innovative framework designed to enhance LLM agent skills through execution-conditioned skill revision. In complex, verifier-driven environments, existing self-evolving methods perform poorly in cold-start scenarios. SkillRevise addresses this by diagnosing skill defects from execution evidence, retrieving relevant repair principles from a general memory, and applying execution-anchored edits.
In experiments, SkillRevise demonstrates significant performance improvements on SkillsBench, SkillLearnBench-Random, and SWE-Skills-Bench-Hard, with success rates increasing from 36.05% to 61.63%. This result indicates that SkillRevise not only enhances the execution utility of skills but also demonstrates their transferability across different executors and task environments.
While SkillRevise performs well across multiple benchmarks, its revision effectiveness may be limited for long-horizon tasks. Future research can explore its application in more domains and complex tasks, as well as further optimization of the revision process to enhance efficiency and utility.
Deep Analysis
Background
With the increasing application of LLM agents in complex environments, effectively constructing and revising skills has become a research hotspot. Traditional skill construction methods rely on expert authoring or one-shot generation, but they fall short in execution utility. SkillRevise offers a new solution through its execution-conditioned revision method.
Core Problem
Existing skill construction methods perform poorly in cold-start scenarios, unable to effectively utilize initial imperfect skills. SkillRevise aims to iteratively refine initial skills to enhance their execution utility.
Innovation
SkillRevise's core innovation lies in its execution-anchored revision method, which combines task-specific diagnosis and general repair principles to achieve effective skill improvement.
Methodology
- �� Diagnose skill defects from execution evidence
- �� Retrieve repair principles from general memory
- �� Apply execution-anchored edits
- �� Re-execute candidates and measure empirical utility
- �� Retain the best observed skill within the revision budget
Experiments
Evaluated on SkillsBench, SkillLearnBench-Random, and SWE-Skills-Bench-Hard using multiple executors, including GPT-5.5, Claude Opus 4.7, etc. Results show SkillRevise's strong performance across multiple benchmarks.
Results
SkillRevise significantly improves LLM agent success rates, particularly on SkillsBench, from 36.05% to 61.63%. This result demonstrates its transferability across different executors and task environments.
Applications
SkillRevise can be applied in complex task environments requiring efficient skill execution, such as automated workflows and intelligent assistants.
Limitations & Outlook
SkillRevise's revision effectiveness may be limited for long-horizon tasks. Additionally, generic principles may be insufficient for effective repair in domain-specific skill design defects.
Plain Language Accessible to non-experts
Imagine a factory where workers need to continuously improve their workflows to increase efficiency. SkillRevise acts like a smart system that observes the workers' tasks, identifies issues, and provides improvement suggestions. In this way, the factory's production efficiency is significantly enhanced. Similarly, SkillRevise analyzes the execution evidence of LLM agents, identifies defects in skills, and applies repair principles to improve skills, thereby increasing the agents' success rate.
ELI14 Explained like you're 14
Imagine you're playing a game where your character needs to complete various tasks. SkillRevise is like a super helper that watches how you play, finds out where you're not doing well, and gives you tips to improve your skills. This way, you can complete tasks faster and score higher! Isn't that cool?
Glossary
LLM (Large Language Model)
An AI model capable of generating natural language text, often used for complex language tasks.
In this paper, LLM is used to generate initial skills.
Skill Revision
The process of improving skills by analyzing execution evidence, identifying defects, and applying repair principles.
The core mechanism of the SkillRevise framework.
Execution Anchoring
A technique for applying repair principles to specific execution evidence to enhance skill utility.
A key step in improving skills within SkillRevise.
Cold Start
A scenario where a system or process is initiated without sufficient data or experience.
SkillRevise aims to address skill construction challenges in cold-start scenarios.
General Memory
A knowledge base storing reusable repair principles for guiding skill revision.
A component in SkillRevise for retrieving repair principles.
Open Questions Unanswered questions from this research
- 1 How can SkillRevise be applied in more complex task environments?
- 2 How can the revision process be optimized for greater efficiency?
Applications
Immediate Applications
Automated Workflows
SkillRevise can be used to enhance the efficiency of automated workflows, reducing errors.
Long-term Vision
Intelligent Assistants
By improving skills, SkillRevise can enhance the capabilities of intelligent assistants, enabling them to better complete complex tasks.
Abstract
Agent skills are procedural artifacts that enable LLM agents to execute workflows, verify constraints, and recover from failures. Existing self-evolving methods refine skills using accumulated trajectories. However, they struggle in cold-start settings, where only an initial, imperfect skill is available. Consequently, skill construction defaults to expert authoring or one-shot LLM generation. Expert-authored skills are costly and may not align with how LLM agents actually execute tasks, while one-shot generated skills can be syntactically well formed yet behaviorally weak. To bridge this gap, we propose SkillRevise, an execution-grounded framework designed to iteratively refine these initial skills. SkillRevise diagnoses skill defects from execution evidence, retrieves relevant repair principles from a general memory, and applies execution-anchored edits. By re-executing candidates and measuring empirical utility, it retains the best observed skill within the revision budget. Evaluated across three main benchmarks, two domain-specific studies, and six LLMs, SkillRevise substantially outperforms one-shot baselines, improving the base agent's success rate on SkillsBench from 36.05% to 61.63%. Furthermore, the revised skills transfer across both executors and task environments, suggesting that SkillRevise captures reusable procedural knowledge beyond any single executor. Our code is available at https://github.com/HKUST-KnowComp/skillrevise.