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 Knuckles-Team/vector-mcp --skill vector-hybrid-searchgit clone --depth 1 https://github.com/Knuckles-Team/vector-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/knuckles-team/vector-mcp/vector-hybrid-search)<a href="https://agentmods.dev/skills/knuckles-team/vector-mcp/vector-hybrid-search"><img src="https://agentmods.dev/badge/skills/knuckles-team/vector-mcp/vector-hybrid-search/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/knuckles-team/vector-mcp/vector-hybrid-search"><img src="https://agentmods.dev/badge/skills/knuckles-team/vector-mcp/vector-hybrid-search.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 | $0.00113 | $0.00931 |
| Opus 5 | $0.00056 | $0.00465 |
| Sonnet 5 | $0.00023 | $0.00186 |
| Haiku 4.5 | $0.00011 | $0.00093 |
Grade A, and why
vector-hybrid-search 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 8d 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 — 84 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Vector Hybrid Search
Retrieval over vector-store collections through the vector-mcp MCP server.
One condensed tool exposes three retrieval strategies: dense semantic search,
sparse lexical BM25 search, and a hybrid that fuses both rankings.
When to use
- Answer a natural-language question from an indexed corpus (grab RAG context).
- Fetch the top-k most relevant chunks for a query.
- Do keyword/term-exact retrieval where BM25 beats embeddings (codes, IDs, rare tokens).
- Balance recall (semantic) against precision on exact terms (lexical) via hybrid fusion.
When NOT to use
- Creating / populating / deleting collections →
vector-collection-management. - Selecting or configuring the backend engine →
vector-backend-operations. - General web search or KG queries — this only searches the named collection.
Prerequisites & environment
Connect via the mcp-client skill against the vector-mcp MCP server. The
collection must already exist and be populated (see
vector-collection-management). Connection params (db_type, db_path,
host/port, db_name, username, password, collection_name) are passed
inline, matching the collection you want to query.
Tools & actions
| Condensed tool | Actions |
|---|---|
vector_search |
semantic_search, lexical_search, search (hybrid) |
Key parameters
question— the query text (required for every action).number_results— top-k to return.semantic_weight/bm25_weight— leg weights for the hybridsearchaction.rrf_k— the Reciprocal Rank Fusion constant that merges the two rankings.
Recipes
Semantic (vector) search:
{"action":"semantic_search","db_type":"chromadb","db_path":"./chroma","collection_name":"handbook","question":"how do I connect to the VPN?","number_results":5}
Lexical BM25 search (term-exact):
{"action":"lexical_search","db_type":"chromadb","db_path":"./chroma","collection_name":"handbook","question":"gw-hq","number_results":5}
Hybrid search, semantic-leaning fusion:
{"action":"search","db_type":"chromadb","db_path":"./chroma","collection_name":"handbook","question":"vpn gateway hostname","number_results":8,"semantic_weight":0.7,"bm25_weight":0.3,"rrf_k":60}
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.
- 8d ago First seen · 84 lines · 113 tokens per session scan A f650605483de
vector-hybrid-search is a skill published in the GitHub repository Knuckles-Team/vector-mcp (15 stars, last pushed 11d ago), licensed MIT. It adds 113 tokens to every session and 931 once invoked, about $0.0006 per session on Opus 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 2026-08-30.
Other skills, from other repositories
smart-search
Use this for hybrid Vector+BM25 search to find specific existing info in the knowledge base—use only for factual, pre-existing content (skip guidance on creating/structuring docs, formatting, external queries, or search method questions).
knowledge
Knowledge systems authority — RAG pipelines, vector search with Qdrant, embedding generation, semantic chunking, knowledge graph construction, retrieval evaluation, and citation-aware QA patterns.
vector-store
Vector store authority — Qdrant operations, collection management, embedding submission, filtered semantic search, index optimization, payload indexing, and multi-tenant isolation patterns.
ingest-knowledge
Ingest a source path into a specified collection and report file, chunk, vector, and BM25 counts.
evaluate-retrieval
Evaluate retrieval quality for a specified collection with a golden test set.
query-enhancer
RAG query optimization - rewrite, expand, decompose, and analyze search queries for better retrieval results.