What Should a Skill Remember? Quality--Cost Trade-offs in Cost-Aware Skill Rewriting for Language Model Agents

• URL: http://arxiv.org/abs/2606.09421v2
• Date: Tue, 09 Jun 2026 02:58:45 GMT
• Title: What Should a Skill Remember? Quality--Cost Trade-offs in Cost-Aware Skill Rewriting for Language Model Agents
• Abstract summary: We study skill rewriting through an economic lens.<n>Our framework profiles skill structure and rewrites skills using information-preservation strategies.<n> Experiments on SkillsBench reveal distinct quality--cost trade-offs across strategies.
• Score: 58.87681796862133
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language model agents increasingly rely on skills: reusable procedural documents encoding workflows, tool use, implementation patterns, validation checks, and domain rules. Skill rewriting is often treated as prompt compression, but shorter skills can make agents more expensive by removing sparse operational anchors that prevent exploration, debugging, and recovery. We study skill rewriting through this economic lens. Our controlled framework profiles skill structure, rewrites skills using information-preservation strategies, and evaluates the rewrites under fixed task instructions, environments, and verifiers. Experiments on SkillsBench reveal distinct quality--cost trade-offs across strategies: API/code anchoring, workflow guarding, and rule/formula anchoring benefit different task families, with no universally dominant template. In the main held-out evaluation, the learned policy reduces total cost by 7.0% and downstream agent-token cost by 6.0%; in frozen cross-model transfer, the corresponding reductions average 14.7% and 13.7%, while verifier quality is preserved. These results position skill design as cost-aware operational knowledge engineering rather than prompt compression. Resources: https://github.com/1Reminding/Skill_EE.

Related papers

• When Not to Imitate: Boundary-Aware Skill Memory for Reliable Tool-Use LLM Agents [62.10623192614306]
  Extracting skills from past successes is critical for the efficient evolution of Large Language Model (LLM) agents.<n>We propose textbfBoundary-Aware Skill Memory (BASM), which augments each skill with explicit boundary fields.<n>BASM consistently outperforms success-distilled skill-memory baselines.
  arXiv  Detail & Related papers  (2026-08-23T10:16:06Z)
• Agent Skills Can Be Harmful: An Empirical Study of Skill-Induced Failures in LLM Agents [9.71657681051469]
  This paper presents a comprehensive analysis of skill-induced agent failures by attributing task failures and cost regressions to specific loaded skills.<n>We instantiate this framework on SkillsBench and SWE-Skills-Bench, yielding 307 skill-induced failures, including 125 functional failures and 182 efficiency regressions.<n>We propose research topics and tooling improvements for safer and more cost-aware skill reuse.
  arXiv  Detail & Related papers  (2026-08-12T10:15:19Z)
• What Is a Skill Worth? Structure-Aware Shapley Valuation of Agent Skills [18.13304162006297]
  We introduce SkillSV, a structure-aware framework for skill valuation.<n> SkillSV compiles a skill into units, dependencies, and hierarchy, so that only valid counterfactual skills are evaluated.<n>It estimates the resulting values with a rollout-budgeted estimator for noisy agent evaluations.
  arXiv  Detail & Related papers  (2026-08-05T07:56:26Z)
• Progressive Agent Skill Generation via Reinforcement Learning [29.782307460602876]
  We propose Skill-$$, a reinforcement learning method for progressively generating high-quality agent skills.<n>Specifically, we formulate skill generation as a sequential editing process that decomposes skill construction into individually evaluable edits.<n>Experiments show that Skill-$$ generates more effective skills than methods based on pipeline-style consolidation.
  arXiv  Detail & Related papers  (2026-08-03T04:14:59Z)
• SkillAudit: Ground-Truth-Free Skill Evolution via Paired Trajectory Auditing [81.51044612408793]
  SkillAudit is a framework for evolving agent skills without ground-truth feedback.<n>To turn behavioral differences into edit guidance, SkillAudit uses Process-Aligned Contrastive Evaluation.<n>It routes edits through two pipelines: Refine removes noisy or irrelevant guidance from broadly useful skills, while Repair replaces passages that conflict with the task.
  arXiv  Detail & Related papers  (2026-06-12T08:20:09Z)
• SkillComposer: Learning to Evolve Agent Skills for Specification and Generalization [99.18305770138677]
  Agent skills are reusable strategies that guide agent reasoning and action.<n>Current skill construction methods treat the problem as one-shot extraction.<n>We introduce SkillComposer, a framework that decomposes skill construction into three learnable operations.
  arXiv  Detail & Related papers  (2026-06-04T12:16:08Z)
• Contractual Skills: A GovernSpec Design Framework for Enterprise AI Agents [8.419155861590548]
  This paper proposes contractual skills, a GovernSpec-inspired design framework for organizing SKILL.md files as readable task contracts.<n>The framework clarifies the boundary between contractual skills, GovernSpec YAML contracts, Model Context Protocol (MCP) surfaces, tool adapters, runtime guardrails, tracing, and evaluation systems.
  arXiv  Detail & Related papers  (2026-05-21T15:40:05Z)
• SkillGraph: Skill-Augmented Reinforcement Learning for Agents via Evolving Skill Graphs [70.1970574147839]
  We propose SKILLGRAPH, a framework that represents reusable skills as nodes in a directed graph.<n>SKILLGRAPH retrieves not just individual skills, but an ordered skill subgraph that can guide multi-step decision making.<n> Experiments show that SKILLGRAPH achieves state-of-the-art performance against memory-augmented RL methods.
  arXiv  Detail & Related papers  (2026-05-12T12:21:49Z)
• SkillLens: Adaptive Multi-Granularity Skill Reuse for Cost-Efficient LLM Agents [6.293920920196533]
  SkillLens is a hierarchical skill-evolution framework that organizes skills into a four-layer graph of policies, strategies, procedures, and primitives.<n>It retrieves semantically relevant skill seeds, expands them through degree-corrected random walk over the skill graph, and then uses a verifier to decide whether each visited unit should be accepted, decomposed, rewritten, or skipped.<n>Across MuLocbench and ALFWorld, SkillLens consistently improves over strong skill-based baselines.
  arXiv  Detail & Related papers  (2026-05-08T18:48:04Z)
• From Context to Skills: Can Language Models Learn from Context Skillfully? [56.5857437218136]
  Ctx2Skill is a self-evolving framework that autonomously discovers, refines, and selects context-specific skills.<n>It can be plugged into any language model to obtain better context learning capability.
  arXiv  Detail & Related papers  (2026-04-30T09:53:15Z)
• SKILL0: In-Context Agentic Reinforcement Learning for Skill Internalization [61.37478254738943]
  Inference-time skill augmentation is fundamentally limited.<n>We introduce SKILL0, an in-context reinforcement learning framework designed for skill internalization.<n>SKILL0 achieves substantial improvements over the standard RL baseline.
  arXiv  Detail & Related papers  (2026-04-02T17:03:05Z)
• SCPatcher: Automated Smart Contract Code Repair via Retrieval-Augmented Generation and Knowledge Graph [6.3824778021369]
  SCPatcher is a framework that combines retrieval-augmented generation with a knowledge graph for automated smart contract repair.<n>We construct a knowledge graph from 5,000 verified contracts, extracting function-level relationships to build a semantic network.<n>SCPatcher achieves 81.5% overall repair rate and 91.0% compilation pass rate, substantially outperforming existing methods.
  arXiv  Detail & Related papers  (2026-04-01T09:44:45Z)
• EvoSkill: Automated Skill Discovery for Multi-Agent Systems [6.319876096746374]
  We introduce textbfEvoSkill, a self-evolving framework that automatically discovers and refines agent skills.<n>EvoSkill analyzes execution failures, proposes new skills or edits to existing ones, and materializes them into structured, reusable skill folders.<n>We evaluate EvoSkill on two benchmarks: OfficeQA, a grounded reasoning benchmark over U.S. Treasury data, and SealQA, a noisy retrieval benchmark.
  arXiv  Detail & Related papers  (2026-03-03T09:07:22Z)
• SkillCraft: Can LLM Agents Learn to Use Tools Skillfully? [67.69996753743129]
  We introduce SkillCraft, a benchmark explicitly stress-test agent ability to form and reuse higher-level tool compositions.<n> SkillCraft features realistic, highly compositional tool-use scenarios with difficulty scaled along both quantitative and structural dimensions.<n>We propose a lightweight evaluation protocol that enables agents to auto-compose atomic tools into executable Skills, cache and reuse them inside and across tasks.
  arXiv  Detail & Related papers  (2026-02-28T15:44:31Z)
• SoK: Agentic Skills -- Beyond Tool Use in LLM Agents [6.356997609995175]
  Agentic systems increasingly rely on reusable procedural capabilities, textita.k.a., agentic skills, to execute long-horizon reliably.<n>This paper maps the skill layer across the full lifecycle (discovery, practice, distillation, storage, composition, evaluation, and update)<n>We analyze the security and governance implications of skill-based agents, covering supply-chain risks, prompt injection via skill payloads, and trust-tiered execution.
  arXiv  Detail & Related papers  (2026-02-24T13:11:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.