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 skills add ashish7802/awesome-api-skills --skill weaviategit clone --depth 1 https://github.com/ashish7802/awesome-api-skillsWrote 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/ashish7802/awesome-api-skills/weaviate)<a href="https://agentmods.dev/skills/ashish7802/awesome-api-skills/weaviate"><img src="https://agentmods.dev/badge/skills/ashish7802/awesome-api-skills/weaviate/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/ashish7802/awesome-api-skills/weaviate"><img src="https://agentmods.dev/badge/skills/ashish7802/awesome-api-skills/weaviate.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00000 | $0.00571 |
| Opus 5 | $0.00000 | $0.00285 |
| Sonnet 5 | $0.00000 | $0.00114 |
| Haiku 4.5 | $0.00000 | $0.00057 |
Grade A, and why
weaviate scanned grade A with 0 findings 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 yesterday.
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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 74 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Weaviate Vector Database API Skill
Overview
Weaviate is an open-source AI vector search engine designed for scalable semantic search, multi-modal embeddings, and Retrieval-Augmented Generation (RAG).
Installation
npm install weaviate-client
pip install weaviate-client
Connecting to Weaviate
import weaviate, { type WeaviateClient } from 'weaviate-client';
const client: WeaviateClient = await weaviate.connectToWeaviateCloud(
process.env.WEAVIATE_URL!,
{
authCredentials: new weaviate.ApiKey(process.env.WEAVIATE_API_KEY!),
headers: {
'X-OpenAI-Api-Key': process.env.OPENAI_API_KEY!,
},
}
);
Core API Operations
1. Create Collection with Vectorizer
const articles = await client.collections.create({
name: 'Article',
vectorizers: weaviate.configure.vectorizer.text2vecOpenAI({
model: 'text-embedding-3-small',
}),
generative: weaviate.configure.generative.openAI(),
});
2. Hybrid Search (Vector + BM25 Keyword)
const myCollection = client.collections.get('Article');
const response = await myCollection.query.hybrid('neural search and indexing', {
limit: 5,
alpha: 0.75, // 0.75 vector, 0.25 keyword BM25
returnProperties: ['title', 'content', 'category'],
});
for (const obj of response.objects) {
console.log(obj.properties.title, 'Score:', obj.metadata?.score);
}
3. Generative Search (RAG in single query)
const ragResponse = await myCollection.generate.nearText('distributed vector database', {
singlePrompt: 'Summarize key benefits of {title} in two sentences.',
limit: 3,
});
AI Pitfalls & Anti-Hallucination Guidelines
- v3 Collections API: Modern
weaviate-clientusesclient.collections.get('Name')instead ofclient.graphql.get(). - Alpha Parameter: In
hybridqueries,alpha=0performs pure BM25 search,alpha=1performs pure vector search, andalpha=0.5balances both equally.
Production Verification Checklist
- Connect credentials validated against Weaviate Cloud cluster
- Hybrid search alpha tuned for domain terminology
- API key headers passed for text2vec/generative providers
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.
- yesterday First seen · 74 lines · 0 tokens per session scan A 23477243690e
weaviate is a skill published in the GitHub repository ashish7802/awesome-api-skills (13 stars, last pushed today), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 571 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-10.
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vector-db-ops
Use when vector database operations — Pinecone, Weaviate, Qdrant, ChromaDB. Indexing, querying, filtering, and managing vector embeddings for RAG and similarity search. Use when working with vector db ops.
astra-vector-backend
Design Astra DB Data API and vector-search backends for retrieval, metadata filtering, and LangChain-compatible stores.
wshobson-rag-implementation
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