weaviate-search
Perform hybrid vector and keyword search using Weaviate at {{WEAVIATE_HOST}}:{{WEAVIATE_PORT}}.
Best use case
weaviate-search is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Perform hybrid vector and keyword search using Weaviate at {{WEAVIATE_HOST}}:{{WEAVIATE_PORT}}.
Teams using weaviate-search should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/weaviate-search/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How weaviate-search Compares
| Feature / Agent | weaviate-search | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Perform hybrid vector and keyword search using Weaviate at {{WEAVIATE_HOST}}:{{WEAVIATE_PORT}}.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Weaviate Search
Weaviate is available at `http://{{WEAVIATE_HOST}}:{{WEAVIATE_PORT}}` within the Docker network.
## Create a Class
```bash
curl -X POST "http://{{WEAVIATE_HOST}}:{{WEAVIATE_PORT}}/v1/schema" \
-H "Content-Type: application/json" \
-d '{"class": "Document", "vectorizer": "none", "properties": [{"name": "content", "dataType": ["text"]}]}'
```
## Add Objects
```bash
curl -X POST "http://{{WEAVIATE_HOST}}:{{WEAVIATE_PORT}}/v1/objects" \
-H "Content-Type: application/json" \
-d '{"class": "Document", "properties": {"content": "Hello world"}, "vector": [0.1, 0.2, 0.3]}'
```
## Search
```bash
curl -X POST "http://{{WEAVIATE_HOST}}:{{WEAVIATE_PORT}}/v1/graphql" \
-H "Content-Type: application/json" \
-d '{"query": "{Get {Document(nearVector: {vector: [0.1, 0.2, 0.3]}, limit: 5) {content}}}"}'
```
## Tips for AI Agents
- Weaviate supports hybrid search combining vector and BM25 keyword search.
- Use GraphQL for complex queries with filters and aggregations.
- Multi-tenancy enables isolated datasets per tenant.Related Skills
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