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 jnPiyush/AgentX --skill vector-databasesgit clone --depth 1 https://github.com/jnPiyush/AgentXWrote 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/jnpiyush/agentx/vector-databases)<a href="https://agentmods.dev/skills/jnpiyush/agentx/vector-databases"><img src="https://agentmods.dev/badge/skills/jnpiyush/agentx/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/jnpiyush/agentx/vector-databases"><img src="https://agentmods.dev/badge/skills/jnpiyush/agentx/vector-databases.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 | $0.00089 | $0.01470 |
| Opus 5.5 | $0.00036 | $0.00588 |
| Sonnet 5.5 | $0.00018 | $0.00294 |
| Haiku 4.5 | $0.00009 | $0.00147 |
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 — 151 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Vector Databases
Purpose: Pick the right vector store, configure it for production, and avoid the failure modes that wreck retrieval quality.
When to Use This Skill
- Choosing a vector store (existing infra reuse vs purpose-built)
- Sizing index, picking distance metric, configuring HNSW/IVF/DiskANN parameters
- Implementing hybrid search (keyword + vector) with reranking
- Adding metadata filters, namespaces, multi-tenancy
- Operating at scale (sharding, replication, cost)
Selection Decision Tree
Already running PostgreSQL? -> Start with pgvector + HNSW. Migrate later if you outgrow it.
Already running Elastic / OpenSearch / Mongo? -> Use their native vector index.
Pure managed vector DB needed? -> Pinecone (serverless), Qdrant Cloud, Weaviate Cloud
Need Microsoft / Azure ecosystem fit? -> Azure AI Search (hybrid + reranker built-in)
Local / embedded / OSS-only? -> LanceDB or Qdrant local
Massive scale (>500M vectors), dense recall? -> Milvus + DiskANN, or Pinecone enterprise
Comparison (April 2026)
| Store | Strength | Trade-off |
|---|---|---|
| Azure AI Search | Built-in hybrid + semantic reranker, integrated with Foundry | Azure-only |
| Pinecone (Serverless) | Zero-ops, auto-scaling | Cost at low volume; vendor lock-in |
| Qdrant | Fast, rich filters, OSS or cloud, payload search | Self-host ops |
| Weaviate | Modules (rerankers, generative), GraphQL | Heavier footprint |
| Milvus | Largest scale, many index types | Operational complexity |
pgvector (with pgvectorscale) |
Reuses Postgres ops, transactions | Lags on >50M vectors unless tuned |
| LanceDB | Embedded, file-based, fast for laptops + edge | Smaller ecosystem |
| MongoDB Atlas Vector Search | Combines with document data | Atlas-only |
| Elasticsearch / OpenSearch | Mature search + kNN in one | Vector perf lags purpose-built |
Index Types
| Index | Use When | Pros | Cons |
|---|---|---|---|
| HNSW | Default, <100M vectors | Fast, tunable | RAM-heavy |
| IVF / IVF_PQ | Memory-constrained | Compact | Recall tuning needed |
| DiskANN / Vamana | >100M vectors, SSD-served | Disk-friendly | Slower writes |
| ScaNN | Google ecosystem | Strong recall/latency | Less common standalone |
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 Changed d600bd3723d2
- 1mo ago First seen · 151 lines · 89 tokens per session scan A 7cf2df5139b9
vector-databases is a skill published in the GitHub repository jnPiyush/AgentX (16 stars, last pushed 3d ago), licensed Apache-2.0. It adds 89 tokens to every session and 1,470 once invoked, about $0.0004 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
pgvector-semantic-search
Use this skill for setting up vector similarity search with pgvector for AI/ML embeddings, RAG applications, or semantic search. Trigger when user asks to: Store or search vector embeddings in PostgreSQL Set up semantic search, similarity search, or nearest neighbor search Create HNSW or IVFFlat indexes for vectors…
postgres-hybrid-text-search
Use this skill to implement hybrid search combining BM25 keyword search with semantic vector search using Reciprocal Rank Fusion (RRF). Trigger when user asks to: Combine keyword and semantic search Implement hybrid search or multi-modal retrieval Use BM25/pgtextsearch with pgvector together Implement RRF (Reciprocal…
vector-db-rag-expert
Expert guide for high-performance Vector Databases, Deep RAG architectures, pgvector 0.8+ HNSW, Reciprocal Rank Fusion (RRF), Cross-Encoder Re-ranking, and Late Chunking / Panduan ahli Vector DB, arsitektur Deep RAG, pgvector HNSW, RRF, dan Re-ranking.
postgres-semantic-search
Builds and tunes PostgreSQL retrieval with pgvector, full-text search, pgtrgm, hybrid fusion, ParadeDB BM25 and reranking. Use for search/RAG implementation, weak results, filtered ANN scans, non-English corpora or retrieval evaluation. For general schema, RLS and query tuning unrelated to retrieval, use…
ai-vector-brain
Creates reusable vector-brain implementations: repo, docs, compliance. Use when building pgvector SQL, manifests, ingest scripts. Not for context design (dev-context-engineering).
laravel-vector-search
Use when implementing semantic/vector search in Laravel 13 with PostgreSQL + pgvector.