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

2026-05-17 · Source: cs.CL updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, extended

Summary

The research introduces a cost-aware skill rewriting framework for large language model agents, moving beyond simple prompt compression. It addresses the issue where shortening skills can paradoxically increase agent execution costs by removing critical "operational anchors" like API signatures or validation rules. The framework profiles skill structure, applies information-preservation strategies (API/code anchoring, workflow guarding, rule/formula anchoring), and evaluates rewrites on SkillsBench. Experiments show that a learned policy reduces total cost by 7.0% and downstream agent-token cost by 6.0% in held-out evaluation, with average reductions of 14.7% and 13.7% in cross-model transfer across Gemini 3 Pro, Codex with GPT-5.4, and Claude Code with Opus 4.6, all while preserving verifier quality. This positions skill design as knowledge engineering focused on cost-aware preservation.

Key takeaway

For MLOps Engineers optimizing LLM agent deployments, blindly compressing skill prompts can increase overall operational costs. You should adopt a cost-aware skill rewriting approach, focusing on preserving critical operational anchors like API calls or validation rules based on task and skill structure. This strategy, demonstrated to reduce total execution costs by up to 14.7% while maintaining quality, ensures that your agents remain efficient and reliable without incurring hidden downstream expenses.

Key insights

Skill rewriting for LLM agents must preserve operational anchors to balance quality and total execution cost, not just shorten prompts.

Principles

• Skill rewriting is cost-aware knowledge preservation, not prompt compression.
• Effective rewrite strategies are task-dependent, not universally optimal.
• Evaluate skill rewrites by agent execution cost, not just input length.

Method

The framework profiles task-skill structure, generates strategy-conditioned rewrites (API/code, rule/formula, workflow), evaluates them under fixed task conditions, and learns a task-conditioned selector from quality-cost feedback.

In practice

• Prioritize preserving API signatures, code snippets, and validation thresholds.
• Use task-specific profiles to select optimal preservation strategies.
• Monitor downstream agent-token usage to assess true cost savings.

Topics

• LLM Agents
• Skill Rewriting
• Cost Optimization
• Operational Anchors
• Prompt Engineering
• SkillsBench

Code references

• 1Reminding/Skill_EE

Best for: AI Engineer, NLP Engineer, Research Scientist, AI Scientist, Machine Learning Engineer, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CL updates on arXiv.org.