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Skilldex: A Package Manager and Registry for Agent Skill Packages with Hierarchical Scope-Based Distribution

arXiv AI Archived Apr 21, 2026 ✓ Full text saved

arXiv:2604.16911v1 Announce Type: new Abstract: Large Language Model (LLM) agents are increasingly extended at runtime via skill packages, structured natural-language instruction bundles loaded from a well-known directory. Community install tooling and registries exist, but two gaps persist: no public tool scores skill packages against Anthropic's published format specification, and no mechanism bundles related skills with the shared context they need to remain mutually coherent. We present Skil

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    Computer Science > Artificial Intelligence [Submitted on 18 Apr 2026] Skilldex: A Package Manager and Registry for Agent Skill Packages with Hierarchical Scope-Based Distribution Sampriti Saha, Pranav Hemanth Large Language Model (LLM) agents are increasingly extended at runtime via skill packages, structured natural-language instruction bundles loaded from a well-known directory. Community install tooling and registries exist, but two gaps persist: no public tool scores skill packages against Anthropic's published format specification, and no mechanism bundles related skills with the shared context they need to remain mutually coherent. We present Skilldex, a package manager and registry for agent skill packages addressing both gaps. The two novel contributions are: (1) compiler-style format conformance scoring against Anthropic's skill specification, producing line-level diagnostics on description specificity, frontmatter validity, and structural adherence; and (2) the skillset abstraction, a bundled collection of related skills with shared assets (vocabulary files, templates, reference documents) that enforce cross-skill behavioral coherence. Skilldex also provides supporting infrastructure: a three-tier hierarchical scope system, a human-in-the-loop agent suggestion loop, a metadata-only community registry, and a Model Context Protocol (MCP) server. The system is implemented as a TypeScript CLI (skillpm / spm) with a Hono/Supabase registry backend, and is open-source. Comments: 8 pages, 1 figure, 5 tables. IEEE conference format Subjects: Artificial Intelligence (cs.AI) ACM classes: D.2.7; K.6.3 Cite as: arXiv:2604.16911 [cs.AI]   (or arXiv:2604.16911v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2604.16911 Focus to learn more Submission history From: Sampriti Saha [view email] [v1] Sat, 18 Apr 2026 08:42:11 UTC (17 KB) Access Paper: HTML (experimental) view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-04 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)
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    arXiv AI
    Category
    ◬ AI & Machine Learning
    Published
    Apr 21, 2026
    Archived
    Apr 21, 2026
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