SkillZip: Contract-Preserving Graph Compression for Scalable Agent Skill Libraries

2026-08-06

AI Summary

This work addresses the challenge of efficiently reusing fine-grained, executable, and contract-consistent skill units from large agent skill repositories under limited context budgets. To this end, we propose SkillZip, a framework that performs node-level compression of skills through an execution-aware graph structure, abstracting redundant contract-valid subgraphs into invertible, ported macros. These macros preserve boundary signatures and dependency closures while supporting on-demand expansion. SkillZip is the first approach to achieve contract-preserving, dependency-closed, verifiable, and scalable skill graph compression, effectively bridging the unit mismatch among skill retrieval, compression, and execution. Experiments demonstrate that SkillZip improves performance by an average of 12.2 points across multiple agent benchmarks, achieves a 3.46× compression ratio, retains 99.2% of dependencies, attains 98.7% verifiability coverage, and enables stable retrieval in skill libraries with over 100,000 entries.

Abstract

Large Language Models (LLMs) increasingly act as agents whose procedural knowledge is stored in reusable skill packages and loaded at inference time. As skill libraries grow, a central challenge is to expose the smallest sufficient executable context under a limited context budget. Existing systems struggle to reuse routines below the whole-skill level, preserve procedural contracts during compression, keep compressed routines executable and expandable, and update the compressed library as skills evolve. These challenges reveal a unit mismatch: skills are retrieved as packages, compressed as text, and converted into execution graphs only after retrieval, whereas reliable reuse requires a contract-bearing procedural unit. We propose SkillZip, an execution-aware procedural abstraction framework that performs contract-preserving compression over section-level graphs. SkillZip rewrites recurring contract-valid motifs into reversible ported macros while preserving boundary signatures, dependency closure, verifier reachability, and source-level expansion. At inference time, it hydrates a compact, dependency-closed context and expands macros only when required. ReZip further integrates new skills and revises risky macros using execution evidence. Comprehensive experiments1 on technical and embodied agent benchmarks show SkillZip consistently outperforms the strongest baseline by up to 12.2 points, while achieving a 3.46x compression ratio with 99.2% dependency preservation and 98.7% verifier reachability. Scaling analyses further confirm robust retrieval across skill libraries ranging from 200 to 100K skills.

Problem

• skill compression
• procedural contracts
• context budget
• executable routines
• skill libraries

Innovation

• contract-preserving compression
• execution-aware abstraction
• ported macros
• dependency-closed context
• skill graph