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 roedyrustam/vibes-plug --skill vector-db-rag-expertgit clone --depth 1 https://github.com/roedyrustam/vibes-plugWrote 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/roedyrustam/vibes-plug/vector-db-rag-expert)<a href="https://agentmods.dev/skills/roedyrustam/vibes-plug/vector-db-rag-expert"><img src="https://agentmods.dev/badge/skills/roedyrustam/vibes-plug/vector-db-rag-expert/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/roedyrustam/vibes-plug/vector-db-rag-expert"><img src="https://agentmods.dev/badge/skills/roedyrustam/vibes-plug/vector-db-rag-expert.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00077 | $0.02734 |
| Opus 5.5 | $0.00031 | $0.01094 |
| Sonnet 5.5 | $0.00015 | $0.00547 |
| Haiku 4.5 | $0.00008 | $0.00273 |
Grade A, and why
vector-db-rag-expert 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 2d 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 — 276 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Vector DB & Deep RAG Expert (2026 Edition)
English
Orchestration & Integration
- Upstream AI Models & Embeddings:
ai-llm-integration-expert,frontier-ai-models-expert,vercel-ai-sdk-expert - Database & Storage Engines:
database-orm-expert,graph-rag-knowledge-expert,search-engine-expert - Retrieval & Pipeline Tooling:
xerj-search-expert,llm-finops-router,context-window-engineer - Security & Quality:
prompt-injection-firewall,ai-safety-governance-expert,anti-slop - Lifecycle & Governance:
zero-to-prod-orchestrator,session-memory-manager
Purpose & Overview
Production-grade architectural guide for Vector Databases (PostgreSQL pgvector 0.8+, Qdrant, LanceDB, Pinecone), Deep RAG indexing strategies, HNSW iterative search, Contextual Retrieval (Anthropic 2026 standard), Reciprocal Rank Fusion (RRF) hybrid retrieval, Cross-Encoder Re-ranking (FlashRank, BGE-Reranker-v2), and Late Chunking to eliminate context fragmentation.
Key Capabilities
- pgvector 0.8+ & Halfvec / Sparsevec: High-dimensional vector storage with 50% RAM savings using
halfvec, native BM25 sparse vectors viasparsevec, and iterative HNSW index scans with metadata filtering. - Contextual Retrieval (Anthropic 2026 Standard): Prepend 50-100 token LLM-generated document context to each chunk before embedding and BM25 indexing, reducing failed retrieval rates by up to 67%.
- Reciprocal Rank Fusion (RRF): Combining sparse keyword BM25 ranks with dense semantic vector ranks using $RRF(d) = \sum \frac{1}{k + rank(d)}$ ($k = 60$).
- Cross-Encoder Re-ranking: Two-stage retrieval pipeline: retrieve Top-50 candidates via fast hybrid search, then re-rank down to Top-5 using a cross-encoder model to maximize NDCG@10.
- Late Chunking: Embed long-context documents in full before pooling token embeddings into individual chunks, preserving document-level semantics across boundaries.
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.
- 2d ago Changed · +65 lines · +77 tokens per session 51053a64604c
- 3d ago Changed · +2 lines · -77 tokens per session b7542ced7ea8
- 6d ago Changed 4b98afbd1ea6
- 15d ago Changed 2cd82d048a13
- 20d ago Changed 0f1eca542b60
- 21d ago Changed f15ba4bcd57b
- 23d ago Changed · -6 lines · +63 tokens per session e51ab98e1e39
- 25d ago Changed · +6 lines · -63 tokens per session dc989b594e15
- 1mo ago Changed 8e83ffa0e065
- 1mo ago First seen · 209 lines · 77 tokens per session scan A d61454bf1a15
vector-db-rag-expert is a skill published in the GitHub repository roedyrustam/vibes-plug (113 stars, last pushed yesterday), licensed MIT. It adds 77 tokens to every session and 2,734 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.
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