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 LuuOW/meridian-mcp --skill vector-databasesgit clone --depth 1 https://github.com/LuuOW/meridian-mcpWrote 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/luuow/meridian-mcp/vector-databases)<a href="https://agentmods.dev/skills/luuow/meridian-mcp/vector-databases"><img src="https://agentmods.dev/badge/skills/luuow/meridian-mcp/vector-databases/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/luuow/meridian-mcp/vector-databases"><img src="https://agentmods.dev/badge/skills/luuow/meridian-mcp/vector-databases.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 | $0.00071 | $0.02659 |
| Opus 5.5 | $0.00028 | $0.01064 |
| Sonnet 5.5 | $0.00014 | $0.00532 |
| Haiku 4.5 | $0.00007 | $0.00266 |
Grade A, and why
vector-databases 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 1mo 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.
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 — 102 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Vector Databases
Vector databases are the retrieval layer for semantic search and RAG (Retrieval-Augmented Generation) systems, storing high-dimensional embedding vectors and enabling approximate nearest-neighbor (ANN) search at scale. This skill covers the full engineering stack: choosing and configuring a vector store, building embedding pipelines, tuning ANN indices for the accuracy/latency/memory trade-off, implementing hybrid retrieval, and wiring everything into production RAG architectures. It orbits the llm-integration and knowledge skills.
Core Concepts
Embedding Pipelines
Embeddings are the foundation — garbage in, garbage out. Model selection matters: text-embedding-3-large (OpenAI, 3072 dims, reducible via dimensions param), voyage-3 (Voyage AI, strong for code/technical), embed-english-v3.0 (Cohere, supports int8 quantization natively), nomic-embed-text-v1.5 (open-source, Matryoshka — supports variable output dims). For multilingual: multilingual-e5-large or Cohere's multilingual model.
Batching: always batch embedding calls. OpenAI allows up to 2048 inputs per call. Local models via sentence-transformers: model.encode(texts, batch_size=64, show_progress_bar=True, normalize_embeddings=True). Normalize to unit sphere if using cosine similarity (makes dot product equivalent, faster in FAISS/Qdrant).
Chunking strategy is retrieval-critical: fixed-size (512 tokens with 50-token overlap) works as a baseline. Semantic chunking (split on embedding similarity drops) improves coherence. For structured docs (PDFs, HTML), prefer element-aware chunking via unstructured library — respects headings, tables, lists. Store chunk_index and parent_doc_id as metadata to enable parent-document retrieval (fetch surrounding chunks at query time).
FAISS
FAISS is the reference ANN library (Meta). Index types:
IndexFlatL2/IndexFlatIP: exact brute-force, no approximation. Use for < 1M vectors or as ground truth for recall benchmarking.IndexIVFFlat: inverted file index.nlist= number of Voronoi cells (rule of thumb:sqrt(N)to4*sqrt(N)). At query time,nprobecells searched — trade recall for speed. Requires training:index.train(vectors).IndexIVFPQ: adds Product Quantization compression.msubquantizers (must divided),nbits=8standard. Dramatic memory reduction (32x withm=d/4, nbits=8) at modest recall cost.IndexHNSWFlat: HNSW graph index.M= edges per node (16-64, higher = better recall + more memory),efConstruction= search width during build (200-500). At query time:index.hnsw.efSearch(32-256). No training required.IndexIVFPQ+HNSWcoarse quantizer: best recall/memory/latency for large-scale production.
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.
- 1mo ago First seen · 102 lines · 71 tokens per session scan A 8609dc67ca0a
vector-databases is a skill published in the GitHub repository LuuOW/meridian-mcp (0 stars, last pushed 6d ago), licensed MIT. It adds 71 tokens to every session and 2,659 once invoked, about $0.0003 per session on Opus 5.5. 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 2026-09-03.
Other skills, from other repositories
vector-database-management
Comprehensive guide for managing vector databases including Pinecone, Weaviate, and Chroma for semantic search, RAG systems, and similarity-based applications.
chroma
Open-source embedding database for AI applications. Store embeddings and metadata, perform vector and full-text search, filter by metadata. Simple 4-function API. Scales from notebooks to production clusters. Use for semantic search, RAG applications, or document retrieval. Best for local development and open-source…
qdrant-vector-search
High-performance vector similarity search engine for RAG and semantic search. Use when building production RAG systems requiring fast nearest neighbor search, hybrid search with filtering, or scalable vector storage with Rust-powered performance.
pinecone
Managed vector database for production AI applications. Fully managed, auto-scaling, with hybrid search (dense + sparse), metadata filtering, and namespaces. Low latency (<100ms p95). Use for production RAG, recommendation systems, or semantic search at scale. Best for serverless, managed infrastructure.
chroma
Open-source embedding database for AI applications. Store embeddings and metadata, perform vector and full-text search, filter by metadata. Simple 4-function API. Scales from notebooks to production clusters. Use for semantic search, RAG applications, or document retrieval. Best for local development and open-source…
qdrant-vector-search
High-performance vector similarity search engine for RAG and semantic search. Use when building production RAG systems requiring fast nearest neighbor search, hybrid search with filtering, or scalable vector storage with Rust-powered performance.