# SkillHone: A Harness for Continual Agent Skill Evolution Through Persistent Decision History

arXiv: https://arxiv.org/abs/2606.08671 · HTML: https://mlsanity.com/paper/2606.08671/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Placed in Agent skill learning and evolution, one of 16 directions in LLM agents and tool use.
- Builds on 14 of its 30 references that are in the map.
- First posted on arXiv 7 Jun 2026.

## Placed in

- LLM agents and tool use / [Agent skill learning and evolution](https://mlsanity.com/agents/agent-skill-learning-and-evolution/)

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

- [SWE-bench: Can Language Models Resolve Real-World GitHub Issues?](https://mlsanity.com/paper/2310.06770/) (2023)
- [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023)
- [GAIA: a benchmark for General AI Assistants](https://mlsanity.com/paper/2311.12983/) (2023)
- [Large Language Models Are Human-Level Prompt Engineers](https://mlsanity.com/paper/2211.01910/) (2022)
- [Multi-Agent Collaboration Mechanisms: A Survey of LLMs](https://mlsanity.com/paper/2501.06322/) (2025)
- [Multi-Agent Collaboration: Harnessing the Power of Intelligent LLM Agents](https://mlsanity.com/paper/2306.03314/) (2023)
- [SkillsBench: Benchmarking How Well Agent Skills Work Across Diverse Tasks](https://mlsanity.com/paper/2602.12670/) (2026)
- [SkillRL: Evolving Agents via Recursive Skill-Augmented Reinforcement Learning](https://mlsanity.com/paper/2602.08234/) (2026)
- [WebShaper: Agentically Data Synthesizing via Information-Seeking Formalization](https://mlsanity.com/paper/2507.15061/) (2025)
- [Live-SWE-agent: Can Software Engineering Agents Self-Evolve on the Fly?](https://mlsanity.com/paper/2511.13646/) (2025)
- [SWE-Skills-Bench: Do Agent Skills Actually Help in Real-World Software Engineering?](https://mlsanity.com/paper/2603.15401/) (2026)
- [Agent Skills: A Data-Driven Analysis of Claude Skills for Extending Large Language Model Functionality](https://mlsanity.com/paper/2602.08004/) (2026)
- [SkillForge: Forging Domain-Specific, Self-Evolving Agent Skills in Cloud Technical Support](https://mlsanity.com/paper/2604.08618/) (2026)
- [Automated Skill Discovery for Language Agents through Exploration and Iterative Feedback](https://mlsanity.com/paper/2506.04287/) (2025)

## Built on by (most cited first, 7 of 7)

- [Rethinking Self-Evolving Agent Skills: Feedback Dynamics over Multiple Rounds](https://mlsanity.com/paper/2608.02636/) (2026)
- [Bidirectional Context Self-Distillation for Reinforcement Learning of Skill-Based LLM Agents](https://mlsanity.com/paper/2608.09555/) (2026)
- [EngramBench: A Capability-Grounded Benchmark for Skill-Evolution Harnesses](https://mlsanity.com/paper/2609.39284/) (2026)
- [Repo2Skill-Evo: Repository Skills Go Stale in Silence](https://mlsanity.com/paper/2608.21964/) (2026)
- [Who Maintains Agent Skills? A Longitudinal Study of Human-Governed, AI-Assisted Skill Maintenance](https://mlsanity.com/paper/2609.05677/) (2026)
- [GeoSkill:Experience-Driven Hierarchical Skill Learning with Collaborative Revision forGeospatialAgents](https://mlsanity.com/paper/2609.13667/) (2026)
- [Socratic Skill Learning: Interleaving Teaching and Practice for Transferable Agent Skills](https://mlsanity.com/paper/2610.17860/) (2026)

## Abstract (arXiv)

Agent skills extend language-model agents with task-specific procedures, scripts, and references, but the tasks and environments they target continually change. Existing methods improve skills in bounded runs and retain only the final artifact, discarding the decision history that later agents need to interpret prior revisions, evaluations, and rejected alternatives. We introduce SkillHone, a harness for continual agent skill evolution grounded in persistent decision history. SkillHone pairs skill revisions with evaluation-side evidence that supplies practice feedback, recording structured histories of diagnoses, revisions, evidence, and outcomes. Role-separated subagents run candidate skills on practice probes with redacted reporting and propose revisions informed by prior decisions, enabling cross-session refinement without rediscovering past rationale. On deep-research benchmarks, SkillHone runs without a pre-integrated search stack and outperforms the commercially backed deep-research agent by 15.8 points on GAIA and 3.2 points on WebWalkerQA-EN, while also exceeding prior skill-evolution methods. We further deploy SkillHone on internal tool-mediated analysis scenarios, where it improves accuracy by an average of 18.8 points across seven settings.