Hyset POC — AI skill for Claude Code

hyset — set-level tool retrieval for LLM agents

[](LICENSE) [](mise.toml) [](https://arxiv.org/abs/2607.25718)

A runnable, clean-room proof of concept of **HySet**, from *"Tools Are Not Islands: Set-Level Tool Retrieval for LLM Agents via Query-Conditioned Hyperedge Prediction"* (Hong, Dong, Yu, Jiang, [arXiv:2607.25718](https://arxiv.org/abs/2607.25718)).

Independent reimplementation. It is not affiliated with the paper's authors, and their code was not used (see [Clean room](#clean-room)).

An agent with thousands of tools can't put them all in its prompt, so a retriever picks a few first. Most retrievers score each tool on its own and take the top-k. A travel request then comes back with three flight APIs and no currency converter. HySet makes the **tool set** the unit of retrieval: it scores whole candidate sets (hyperedges of a tool co-invocation hypergraph) and hands the single best set to an unmodified agent.

What's in the box:

• Method: the HySet model, its training loop (InfoNCE over pools of negative sets, plus reward-weighted self-training), and exact two-stage inference (shortlist, then score all 21,699 subsets).
• Data: a synthetic tool world with planted set structure (decoy tools, implicit sub-tasks, cardinality-dependent co-invocation), plus a ToolBench converter.
• Execution feedback: via LangChain, with Claude (default) or a local Ollama model as agent and judge over mock tools. An offline sim reward covers demos and CI.
• Interfaces: a hyset CLI, an HTTP service (FastAPI), and an MCP server, so agents can call it as a pre-selection step.

How it works

Each tool `t` has a learned unit vector `z_t`, initialized from a frozen text encoder's embedding of its description. A candidate s