hyperspaceai/matrix-v6

Matrix v6 — Hyperspace capability retrieval index

Retrieval index for 446,897 agent capabilities (tools, skills, agents) drawn from the skills.sh ecosystem (387 K SKILL.md files across 7,029 GitHub repos) plus Hyperspace's curated tool/agent catalog.

The retriever itself is Qwen/Qwen3-Embedding-0.6B used pretrained, no fine-tune — a deliberate choice documented in MATRIX_V6_ARCHITECTURE.md.

Contents

File	What
capability_embeddings.fp16.npy	[446897, 1024] fp16 embeddings, L2-normalized
capability_names.json	Per-row metadata (cap_id, name, kind, repo)
capability_clusters.json	cap_id → cluster_id (190,085 intent clusters)
capabilities.jsonl	Full source rows (name, description, snippet)
config.json	Index metadata: retriever base, dim, query prompt, pooling

Quick eval (5 K held-out test queries, brute-force cosine)

Metric	v6	v5 backbone same setup
ret@1	16.6 %	6.0 %
ret@5	47.3 %	15.6 %
ret@10	57.4 %	18.8 %
cluster@1	51.6 %	16.0 %
MRR@20 (cluster)	0.56	0.18

~3 × across every metric, with a backbone 0.4 × the size (596 M vs 1.5 B).

Usage

from huggingface_hub import snapshot_download
import numpy as np, json, torch
import torch.nn.functional as F
from transformers import AutoTokenizer, AutoModel

# 1. pull the index
path = snapshot_download("hyperspaceai/matrix-v6")
emb = np.load(f"{path}/capability_embeddings.fp16.npy")
names = json.load(open(f"{path}/capability_names.json"))

# 2. load the (pretrained) retriever
tok = AutoTokenizer.from_pretrained("Qwen/Qwen3-Embedding-0.6B")
tok.padding_side = "left"
if tok.pad_token is None: tok.pad_token = tok.eos_token
model = AutoModel.from_pretrained("Qwen/Qwen3-Embedding-0.6B",
                                   torch_dtype=torch.bfloat16).cuda().eval()

# 3. embed query
QP = ("Instruct: Given a user request, retrieve relevant agent "
      "capabilities (tools / skills / agents) from the catalog.\nQuery: ")
def encode(text):
    enc = tok(QP + text + tok.eos_token, return_tensors="pt",
              truncation=True, max_length=192).to("cuda")
    with torch.no_grad():
        h = model(**enc).last_hidden_state
    return F.normalize(h[:, -1, :].float(), dim=-1).cpu().numpy()

# 4. retrieve top-10
q = encode("write a SQL query that finds the top 10 customers by revenue")
sims = q @ emb.astype(np.float32).T
top10 = sims[0].argsort()[::-1][:10]
for i in top10:
    print(f"{sims[0, i]:.3f}  {names[i]['name']}  ({names[i]['repo']})")

License

Index/embeddings: Apache-2.0. Each SKILL.md remains under its source repository's license — see the repo field in capability_names.json.

The Matrix v6 corpus + scripts are in the Hyperspace agentic-os-prod monorepo; the harvester is thor-services/skills-sh-harvest.js.