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Weaviate — open-source vector database with built-in ML. Hybrid search (vector + keyword), generative search, graph connections, multi-modal (text + image), and automatic schema inference.
Passed 23 of 24 test cases with the skill, and 21 of 24 without it.
2 fixed21 passed either way1 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 2 test cases the skill fixed. Same model, same prompt, with and without the skill.
When setting up collection schema configurations in Python using the modern Weaviate SDK, which helper classes subpackage should be imported alongside the main module?
| Checked | Without skill | With skill |
|---|---|---|
| Output imports weaviate.classes as wvc | Missed | Passed |
The instructions your model reads when the skill loads.
Weaviate is an open-source vector database with built-in vectorization modules (OpenAI, Cohere, HuggingFace, Transformers, CLIP, multi-modal). Supports hybrid search (vector + BM25 keyword), generative search (RAG with LLM integration), and multi-modal data.
docker run -p 8080:8080 semitechnologies/weaviate:latest
import weaviate
import weaviate.classes as wvc
client = weaviate.connect_to_local()
collection = client.collections.create(
name="Documents",
vectorizer_config=wvc.config.Configure.Vectorizer.text2vec_transformers(),
)
collection.data.insert({
"title": "Paris",
"content": "Paris is the capital of France. It is known for the Eiffel Tower.",
})
# Hybrid search (vector + keyword)
response = collection.query.hybrid(query="French capital", limit=5)
for obj in response.objects:
print(obj.properties)
test cases passed, vs 21 / 24 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.