Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/fortiumpartners/ensemble/using-weaviatenpx skills add FortiumPartners/ensemble --skill using-weaviategit clone --depth 1 https://github.com/FortiumPartners/ensembleWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/fortiumpartners/ensemble/using-weaviate)<a href="https://agentmods.dev/skills/fortiumpartners/ensemble/using-weaviate"><img src="https://agentmods.dev/badge/skills/fortiumpartners/ensemble/using-weaviate.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00039 | $0.03193 |
| Opus 5 | $0.00019 | $0.01597 |
| Sonnet 5 | $0.00008 | $0.00639 |
| Haiku 4.5 | $0.00004 | $0.00319 |
Grade A, and why
using-weaviate scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl http://localhost:8080/v1/.well-known/ready How it starts
The opening of the file, as written. The whole thing — 493 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Weaviate Vector Database Skill
Version: 1.0.0 | Target: <500 lines | Purpose: Fast reference for Weaviate operations
Overview
What is Weaviate: Open-source vector database for AI-native applications combining vector search with structured filtering and keyword search.
When to Use This Skill:
- Storing and querying vector embeddings
- Implementing semantic/similarity search
- Building RAG (Retrieval-Augmented Generation) pipelines
- Hybrid search (vector + keyword)
- Multi-tenant vector applications
Auto-Detection Triggers:
weaviate-clientinrequirements.txtorpyproject.tomlweaviate-clientorweaviate-ts-clientinpackage.jsonWEAVIATE_URL,WEAVIATE_API_KEY, orWCD_URLenvironment variablesdocker-compose.ymlwithsemitechnologies/weaviateimage
Progressive Disclosure:
- This file (SKILL.md): Quick reference for immediate use
- REFERENCE.md: Comprehensive patterns, modules, and advanced configuration
Table of Contents
- Core Concepts
- Quick Start
- CLI Decision Tree
- Collection Schema
- Data Operations
- Search Operations
- Generative Search (RAG)
- Multi-Tenancy
- Docker Setup
- Error Handling
- Best Practices
- Quick Reference Card
- Agent Integration
Core Concepts
| Concept | Description |
|---|---|
| Collection | Schema definition for a data type (formerly "Class") |
| Object | Individual data item with properties and vector |
| Vector | Numerical representation of data for similarity search |
| Module | Plugin for vectorization, generative AI, or reranking |
| Tenant | Isolated data partition for multi-tenant applications |
Quick Start
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 6d ago First seen · 493 lines · 39 tokens per session scan A a570abbaa457
using-weaviate is a skill published in the GitHub repository FortiumPartners/ensemble (11 stars, last pushed 1mo ago), licensed MIT. It adds 39 tokens to every session and 3,193 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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