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/librefang/librefang-registry/vector-dbnpx skills add librefang/librefang-registry --skill vector-dbgit clone --depth 1 https://github.com/librefang/librefang-registryWrote 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/librefang/librefang-registry/vector-db)<a href="https://agentmods.dev/skills/librefang/librefang-registry/vector-db"><img src="https://agentmods.dev/badge/skills/librefang/librefang-registry/vector-db.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 | $0.00019 | $0.00816 |
| Opus 5 | $0.00010 | $0.00408 |
| Sonnet 5 | $0.00004 | $0.00163 |
| Haiku 4.5 | $0.00002 | $0.00082 |
Grade A, and why
vector-db 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.
This is a copy
92% identical to vector-db — 3 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 42 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Vector Database Expert
A retrieval systems specialist with deep expertise in embedding models, vector indexing algorithms, and Retrieval-Augmented Generation (RAG) architectures. This skill provides guidance for designing and operating vector search systems that power semantic search, recommendation engines, and LLM knowledge augmentation, covering embedding selection, indexing strategies, chunking, hybrid search, and production deployment.
Key Principles
- Choose the embedding model based on your domain and retrieval task; general-purpose models work well for broad use cases, but domain-specific fine-tuned embeddings significantly improve recall for specialized content
- Select the distance metric that matches your embedding model's training objective: cosine similarity for normalized embeddings, dot product for magnitude-aware comparisons, and L2 (Euclidean) for spatial distance
- Chunk documents thoughtfully; chunk size directly impacts retrieval quality because too-large chunks dilute relevance while too-small chunks lose context
- Index choice determines the trade-off between search speed, memory usage, and recall accuracy; understand HNSW, IVF, and flat index characteristics before choosing
- Combine dense vector search with sparse keyword search (hybrid retrieval) for production systems; neither approach alone handles all query types optimally
Techniques
- Generate embeddings with models like OpenAI text-embedding-3-small, Cohere embed-v3, or open-source sentence-transformers (all-MiniLM-L6-v2, BGE, E5) depending on cost and quality requirements
- Configure HNSW indexes with appropriate M (connections per node, typically 16-64) and efConstruction (build quality, typically 100-200) parameters; higher values improve recall at the cost of memory and build time
- Implement chunking strategies: fixed-size with overlap (e.g., 512 tokens with 50-token overlap), semantic chunking at paragraph or section boundaries, or recursive splitting that respects document structure
- Build hybrid search by executing both vector similarity and BM25/keyword queries, then combining results with Reciprocal Rank Fusion (RRF) or a learned reranker like Cohere Rerank or cross-encoder models
- Filter results using metadata (date ranges, categories, access permissions) at query time; most vector databases support pre-filtering or post-filtering with different performance characteristics
- Design the RAG pipeline: query embedding, retrieval (top-k candidates), optional reranking, context assembly with source citations, and LLM generation with the retrieved context in the prompt
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 · 42 lines · 19 tokens per session scan A eabfbfeefb2f
vector-db is a skill published in the GitHub repository librefang/librefang-registry (11 stars, last pushed 11d ago), licensed MIT. It adds 19 tokens to every session and 816 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 92% identical to vector-db, differing in 3 lines, and is treated as a copy.
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Guides retrieval-store design, indexing, and query behavior for embedding-backed systems without confusing storage with application truth.
vector-databases
Vector database operations — embed, store, search, and build RAG pipelines.