Agent skill

Vector Search

Vector search via embeddings (large-scale HNSW) and ruvllmhnsw (WASM router for ≤11 hot patterns), with RaBitQ 1-bit quantization for 32× memory reduction

MITAuto-check: notesAI & LLM Engineering

Install Vector Search

skills CLI

$ npx skills add ruvnet/ruflo --skill vector-search -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI

$ gh skill install ruvnet/ruflo vector-search --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy

$ git clone --depth 1 https://github.com/ruvnet/ruflo.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugins/ruflo-agentdb/skills/vector-search .claude/skills/vector-search && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Paste into Claude Code

Install the "vector-search" agent skill from https://github.com/ruvnet/ruflo/tree/main/plugins/ruflo-agentdb/skills/vector-search into .claude/skills/vector-search/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "vector-search", then confirm the skill loads.

Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

$skill-installer

$skill-installer install https://github.com/ruvnet/ruflo/tree/main/plugins/ruflo-agentdb/skills/vector-search

Type this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.

skills CLI

$ npx skills add ruvnet/ruflo --skill vector-search -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install ruvnet/ruflo vector-search --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/ruvnet/ruflo.git skills-src && mkdir -p .agents/skills && cp -r skills-src/plugins/ruflo-agentdb/skills/vector-search .agents/skills/vector-search && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "vector-search" agent skill from https://github.com/ruvnet/ruflo/tree/main/plugins/ruflo-agentdb/skills/vector-search into .agents/skills/vector-search/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "vector-search", then confirm the skill loads.

Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add ruvnet/ruflo --skill vector-search -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install ruvnet/ruflo vector-search --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/ruvnet/ruflo.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/plugins/ruflo-agentdb/skills/vector-search .cursor/skills/vector-search && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "vector-search" agent skill from https://github.com/ruvnet/ruflo/tree/main/plugins/ruflo-agentdb/skills/vector-search into .cursor/skills/vector-search/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "vector-search", then confirm the skill loads.

Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

gemini skills install

$ gemini skills install https://github.com/ruvnet/ruflo.git --path plugins/ruflo-agentdb/skills/vector-search

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add ruvnet/ruflo --skill vector-search -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install ruvnet/ruflo vector-search --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/ruvnet/ruflo.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/plugins/ruflo-agentdb/skills/vector-search .gemini/skills/vector-search && rm -rf skills-src

Use ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.

Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/

Paste into Gemini CLI

Install the "vector-search" agent skill from https://github.com/ruvnet/ruflo/tree/main/plugins/ruflo-agentdb/skills/vector-search into .gemini/skills/vector-search/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "vector-search", then confirm the skill loads.

Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

GitHub CLI

$ gh skill install ruvnet/ruflo vector-search

Installs for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).

skills CLI

$ npx skills add ruvnet/ruflo --skill vector-search -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/ruvnet/ruflo.git skills-src && mkdir -p .github/skills && cp -r skills-src/plugins/ruflo-agentdb/skills/vector-search .github/skills/vector-search && rm -rf skills-src

Use ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.

GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/

Paste into GitHub Copilot

Install the "vector-search" agent skill from https://github.com/ruvnet/ruflo/tree/main/plugins/ruflo-agentdb/skills/vector-search into .github/skills/vector-search/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "vector-search", then confirm the skill loads.

GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add ruvnet/ruflo --skill vector-search -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install ruvnet/ruflo vector-search --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/ruvnet/ruflo.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/plugins/ruflo-agentdb/skills/vector-search .opencode/skills/vector-search && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "vector-search" agent skill from https://github.com/ruvnet/ruflo/tree/main/plugins/ruflo-agentdb/skills/vector-search into .opencode/skills/vector-search/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "vector-search", then confirm the skill loads.

OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

Facts

Skill name: vector-search
GitHub stars: 74k
Token cost: ~1.5k tokens
SKILL.md length: 504 words
Files: 1
Skills in repo: 264
Repo updated: Oct 7, 2026
First seen: Oct 7, 2026
Licence: MIT

At a glance

Vector search via embeddings (large-scale HNSW) and ruvllmhnsw (WASM router for ≤11 hot patterns), with RaBitQ 1-bit quantization for 32× memory reduction

Works in 6 steps: Check status —… → Initialize —… → Generate —… → …
Tasks that involve Vector databases
SKILL.md covers When to use, Standard search, Quantized search (32× memory… and Tuning, plus 4 more sections
Calls npx

What it does

Vector Search is an agent skill from ruvnet/ruflo. Vector search via embeddings (large-scale HNSW) and ruvllmhnsw (WASM router for ≤11 hot patterns), with RaBitQ 1-bit quantization for 32× memory reduction

Its SKILL.md is about 1.5k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in AI & LLM Engineering, covering Vector databases and Embeddings. It works with WebAssembly and Model Context Protocol. The repository describes itself as: 🌊 The original agent harness. Deploy intelligent multi-player swarms, coordinate autonomous workflows, and build conversational AI systems. Features adaptive memory…. The licence is MIT.

When your agent uses it

Tasks that involve Vector databases
Tasks that involve Embeddings

Example prompts

“/vector-search”

Requirements

Node.js
Pre-approved tools (allowed-tools): mcp__plugin_ruflo-core_ruflo__embeddings_generate, mcp__plugin_ruflo-core_ruflo__embeddings_search, mcp__plugin_ruflo-core_ruflo__embeddings_compare, mcp__plugin_ruflo-core_ruflo__embeddings_init, mcp__plugin_ruflo-core_ruflo__embeddings_status, mcp__plugin_ruflo-core_ruflo__embeddings_hyperbolic, mcp__plugin_ruflo-core_ruflo__embeddings_neural, mcp__plugin_ruflo-core_ruflo__embeddings_rabitq_build, mcp__plugin_ruflo-core_ruflo__embeddings_rabitq_search, mcp__plugin_ruflo-core_ruflo__embeddings_rabitq_status, mcp__plugin_ruflo-core_ruflo__ruvllm_hnsw_create, mcp__plugin_ruflo-core_ruflo__ruvllm_hnsw_add, mcp__plugin_ruflo-core_ruflo__ruvllm_hnsw_route, mcp__plugin_ruflo-core_ruflo__memory_search_unified, Bash

Workflow steps

6 steps, taken from the first numbered list in SKILL.md.

Check status — mcpplugin_ruflo-core_rufloembeddings_status to verify the embedding engine.
Initialize — mcpplugin_ruflo-core_rufloembeddings_init if not active.
Generate — mcpplugin_ruflo-core_rufloembeddings_generate for text input.
Search — mcpplugin_ruflo-core_rufloembeddings_search with the query.
Compare — mcpplugin_ruflo-core_rufloembeddings_compare to measure similarity.
Unified search — mcpplugin_ruflo-core_ruflomemory_search_unified for cross-namespace.

What it can do on your machine

Read from SKILL.md and the folder at commit de590e1. It shows what the files ask for, not the result of running them.

Tool permissions
Pre-approves these tools, so the agent can use them without asking each time:
- mcp__plugin_ruflo-core_ruflo__embeddings_generate
- mcp__plugin_ruflo-core_ruflo__embeddings_search
- mcp__plugin_ruflo-core_ruflo__embeddings_compare
- mcp__plugin_ruflo-core_ruflo__embeddings_init
- mcp__plugin_ruflo-core_ruflo__embeddings_status
- mcp__plugin_ruflo-core_ruflo__embeddings_hyperbolic
- mcp__plugin_ruflo-core_ruflo__embeddings_neural
- mcp__plugin_ruflo-core_ruflo__embeddings_rabitq_build
- mcp__plugin_ruflo-core_ruflo__embeddings_rabitq_search
- mcp__plugin_ruflo-core_ruflo__embeddings_rabitq_status
…and 5 more on the same allowed-tools line.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Shell commands in SKILL.md call:
- npx
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md. Its commands use npx, which can reach the network depending on how they are called.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Vector Search loads about 1.5k tokens when it runs. Until then it costs about 43 tokens; SKILL.md has 504 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~43
When it runs · the whole SKILL.md, loaded when a task matches: ~1.5k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

NotePre-approves every shell command (allowed-tools: Bash)SKILL.md

allowed-tools: mcp__plugin_ruflo-core_ruflo__embeddings_generate, mcp__plugin_ruflo-core_ruflo__embeddings_search,

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from ruvnet/ruflo at commit de590e1, republished under its MIT licence (© ruvnet). 504 words, ~1,478 tokens.

Download SKILL.mdSave it as .claude/skills/vector-search/SKILL.md (or your agent's skills folder).

name: vector-search
description: Vector search via embeddings_* (large-scale HNSW) and ruvllm_hnsw_* (WASM router for ≤11 hot patterns), with RaBitQ 1-bit quantization for 32× memory reduction
allowed-tools: mcp__plugin_ruflo-core_ruflo__embeddings_generate, mcp__plugin_ruflo-core_ruflo__embeddings_search, mcp__plugin_ruflo-core_ruflo__embeddings_compare, mcp__plugin_ruflo-core_ruflo__embeddings_init, mcp__plugin_ruflo-core_ruflo__embeddings_status, mcp__plugin_ruflo-core_ruflo__embeddings_hyperbolic, mcp__plugin_ruflo-core_ruflo__embeddings_neural, mcp__plugin_ruflo-core_ruflo__embeddings_rabitq_build, mcp__plugin_ruflo-core_ruflo__embeddings_rabitq_search, mcp__plugin_ruflo-core_ruflo__embeddings_rabitq_status, mcp__plugin_ruflo-core_ruflo__ruvllm_hnsw_create, mcp__plugin_ruflo-core_ruflo__ruvllm_hnsw_add, mcp__plugin_ruflo-core_ruflo__ruvllm_hnsw_route, mcp__plugin_ruflo-core_ruflo__memory_search_unified, Bash
argument-hint: <query> [--limit N] [--quantized]

Vector Search

Two distinct vector-search paths live in this plugin. Pick the right one — they're not interchangeable.

Path	Tool family	Backing	Capacity	Latency
Large-scale corpus	`embeddings_*`	`@claude-flow/memory` HNSW (Rust/Native)	up to millions of vectors	~1.9× at N=20k, ~3.2×–4.7× at N=5k vs brute-force (measured; recall@10 ≈ 0.99). ANN wins above the crossover
Hot-path router	`ruvllm_hnsw_*`	WASM-backed router (v2.0.1)	~11 patterns max (`ruvllm-tools.ts:58`)	sub-ms; designed for high-priority routing, not corpus search

The "12,500×" headline applies to the large-scale embeddings_search path. The WASM router is not that path.

When to use

Need	Path
Search a corpus of N ≥ 500 documents	`embeddings_search`
Memory-constrained corpus (≥5,000 vectors)	RaBitQ quantized — see "Quantized search" below
Compare two strings	`embeddings_compare`
Hierarchical / taxonomic data	`embeddings_hyperbolic` (Poincare ball)
Route a query to one of ≤11 hot patterns	`ruvllm_hnsw_route`
Cross-namespace search	`memory_search_unified`

Standard search

Check status — mcp__plugin_ruflo-core_ruflo__embeddings_status to verify the embedding engine.
Initialize — mcp__plugin_ruflo-core_ruflo__embeddings_init if not active.
Generate — mcp__plugin_ruflo-core_ruflo__embeddings_generate for text input.
Search — mcp__plugin_ruflo-core_ruflo__embeddings_search with the query.
Compare — mcp__plugin_ruflo-core_ruflo__embeddings_compare to measure similarity.
Unified search — mcp__plugin_ruflo-core_ruflo__memory_search_unified for cross-namespace.

Quantized search (32× memory reduction)

For corpora ≥5,000 vectors and/or memory-constrained environments, use the RaBitQ 1-bit quantization workflow. Below 5,000 vectors the rebuild cost outweighs the savings — use the standard path instead.

Step	Tool	Purpose
1	`embeddings_init`	Engine warm
2	`embeddings_rabitq_build`	One-time build of the 1-bit index after corpus is loaded
3	`embeddings_rabitq_search`	Hamming-prefilter returns top-N candidate IDs (cheap)
4	`embeddings_search`	Optional exact rerank on the candidate set (full-precision)
5	`embeddings_rabitq_status`	Index health, memory footprint, build time

Note: embeddings_rabitq_search returns candidate IDs only — the rerank in step 4 is the user's responsibility (mirrors the docstring at embeddings-tools.ts:911). Without rerank, results are approximate; with rerank, you get full-precision quality at 32× lower memory.

Show full SKILL.md (221 more words)Show less

Tuning

HNSW exposes three knobs that trade recall against latency. The "12,500×" headline assumes defaults; tune deliberately for your workload:

Profile	`efSearch`	`M`	When to use
`recall-first`	200	32	Pattern recall during planning; quality matters more than ms
`balanced` (default)	64	16	General-purpose semantic recall
`latency-first`	16	8	Hot-path routing where p99 latency matters

efSearch is passed via ruvllm_hnsw_create (ruvllm-tools.ts:64). M is registry-level today; raise as a follow-up if it should be MCP-tunable. efConstruction defaults to 200 in the lite index (hnsw-index.ts:537).

HNSW pattern router (WASM, ≤11 patterns)

For routing a small number of high-priority patterns:

mcp__plugin_ruflo-core_ruflo__ruvllm_hnsw_create — create the WASM index (cap ~11)
mcp__plugin_ruflo-core_ruflo__ruvllm_hnsw_add — add a pattern
mcp__plugin_ruflo-core_ruflo__ruvllm_hnsw_route — route an incoming query

This is not a corpus index. Treat it as a fast classifier over a curated set of patterns.

Hyperbolic embeddings

For hierarchical data (code trees, org charts), use mcp__plugin_ruflo-core_ruflo__embeddings_hyperbolic which maps to Poincare ball space. Distance is geodesic, not cosine.

CLI alternative

bash

npx @claude-flow/cli@latest embeddings search --query "authentication patterns"
npx @claude-flow/cli@latest embeddings init
npx @claude-flow/cli@latest memory search --query "your query"

Performance

Measured numbers (source: scripts/benchmark-intelligence.mjs, ruvector NAPI backend; recall@10 ≈ 0.99). The older "150×–12,500×" figures were brute-force-fallback artifacts and have been retired — see project CLAUDE.md "V3 Performance Targets".

Method	Measured speedup vs brute-force
Brute-force scan	Baseline
HNSW (N=5,000)	~3.2×–4.7× faster
HNSW (N=20,000)	~1.9× faster
HNSW (below crossover, small N)	ties/loses vs brute-force
RaBitQ quantization	32× memory reduction; 0.60 ms/query at N≈14.7k
`ruvllm_hnsw_route` (n≤11)	sub-ms per route, fixed cost

Files

Just SKILL.md in plugins/ruflo-agentdb/skills/vector-search of ruvnet/ruflo.

Open the folder on GitHubat commit de590e1

Compare with similar skills

Vector Search next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Vector Search compared with similar skills
Skill	Stars	Used in	Tokens	Auto-check	Licence	Repo updated
Vector Search this skillruvnet/ruflo	74k	—	~1.5k	Automated safety check: Notes	MIT	today
Codebase Managementgiancarloerra/SocratiCode	3.3k	1 repos	~1.8k	Automated safety check: Pass	AGPL-3.0	4 days ago
Pgvector Semantic Searchtimescale/pg-aiguide	1.9k	1 repos	~3.8k	Automated safety check: Pass	Apache-2.0	5 days ago
Cookbook Aimldatabricks-solutions/databricks-apps-cookbook	183	—	~1.7k	Automated safety check: Pass	Custom licence	2 days ago
Frontmcp Extensibilityagentfront/frontmcp	146	—	~3.2k	Automated safety check: Pass	Apache-2.0	today
Cognee Integrations Setuptopoteretes/cognee	32k	—	~1k	Automated safety check: Notes	Apache-2.0	today

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Questions about Vector Search

What does Vector Search do?

Vector search via embeddings (large-scale HNSW) and ruvllmhnsw (WASM router for ≤11 hot patterns), with RaBitQ 1-bit quantization for 32× memory reduction. Vector Search is an agent skill from ruvnet/ruflo.

When should I use Vector Search?

Vector Search fits situations like: tasks that involve Vector databases; tasks that involve Embeddings.

How do I install Vector Search in Claude Code?

Run `npx skills add ruvnet/ruflo --skill vector-search -a claude-code`. Or copy the skill folder (plugins/ruflo-agentdb/skills/vector-search in ruvnet/ruflo) into .claude/skills/vector-search in your project. Claude Code loads it when a task matches its description.

How do I install Vector Search in Codex?

Run `npx skills add ruvnet/ruflo --skill vector-search -a codex`. Or copy the skill folder (plugins/ruflo-agentdb/skills/vector-search in ruvnet/ruflo) into .agents/skills/vector-search in your project. Codex loads it when a task matches its description.

Can I use Vector Search in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add ruvnet/ruflo --skill vector-search -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/vector-search, .gemini/skills/vector-search, .github/skills/vector-search and .opencode/skills/vector-search in your project.

What does Vector Search need to run?

Going by SKILL.md and its folder, Vector Search needs the command-line tools its instructions call (npx). Our summary lists: Node.js. Its frontmatter pre-approves these tools: mcp__plugin_ruflo-core_ruflo__embeddings_generate, mcp__plugin_ruflo-core_ruflo__embeddings_search, mcp__plugin_ruflo-core_ruflo__embeddings_compare, mcp__plugin_ruflo-core_ruflo__embeddings_init, mcp__plugin_ruflo-core_ruflo__embeddings_status, mcp__plugin_ruflo-core_ruflo__embeddings_hyperbolic, mcp__plugin_ruflo-core_ruflo__embeddings_neural, mcp__plugin_ruflo-core_ruflo__embeddings_rabitq_build, mcp__plugin_ruflo-core_ruflo__embeddings_rabitq_search, mcp__plugin_ruflo-core_ruflo__embeddings_rabitq_status, mcp__plugin_ruflo-core_ruflo__ruvllm_hnsw_create, mcp__plugin_ruflo-core_ruflo__ruvllm_hnsw_add, mcp__plugin_ruflo-core_ruflo__ruvllm_hnsw_route, mcp__plugin_ruflo-core_ruflo__memory_search_unified, Bash.

Does Vector Search access the network?

SKILL.md contains no URLs. Its commands use npx, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Vector Search safe to install?

Our automated static check of SKILL.md found notes only (pre-approves every shell command (allowed-tools: bash)), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.

What licence does Vector Search use?

Vector Search is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Vector Search use?

About 1.5k tokens (SKILL.md is roughly 5.9k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Vector Search?

Skills that share tags, products or a category with Vector Search: Codebase Management (giancarloerra/SocratiCode, 3.3k stars), Pgvector Semantic Search (timescale/pg-aiguide, 1.9k stars), Cookbook Aiml (databricks-solutions/databricks-apps-cookbook, 183 stars) and Frontmcp Extensibility (agentfront/frontmcp, 146 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Vector Search?

ruvnet (a GitHub user) maintains it in ruvnet/ruflo, which has 74,012 GitHub stars. The repository holds 264 skills in this directory. The repository was last updated on October 7, 2026.

Source: ruvnet/ruflo on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Install Vector Search

Facts

At a glance

What it does

When your agent uses it

Example prompts

Requirements

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

Vector Search

When to use

Standard search

Quantized search (32× memory reduction)

Tuning

HNSW pattern router (WASM, ≤11 patterns)

Hyperbolic embeddings

CLI alternative

Performance

Files

Compare with similar skills

Similar skills

More from ruvnet/ruflo

Related

Questions about Vector Search