New: 98,000+ skills indexed from GitHub repos with 100+ stars · every skill safety-checkedBrowse top skillsTop skills
Esc to closeBrowse all skills
Submit a skillBrowse skills
Submit a skillBrowse skills

Agent skill

Agentdb Vector Search

by aiskillstore in aiskillstore/marketplace

Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying.

No licenceAuto-check passedAI & LLM Engineering

Install Agentdb Vector Search

skills CLI
$ npx skills add aiskillstore/marketplace --skill agentdb-vector-search -a claude-code

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

GitHub CLI
$ gh skill install aiskillstore/marketplace agentdb-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/aiskillstore/marketplace.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/ruvnet/agentdb-vector-search .claude/skills/agentdb-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/

Facts

Skill name
agentdb-vector-search
GitHub stars
430
Used in
7 other repos
Token cost
~2.2k tokens
SKILL.md length
292 words
Files
2
Skills in repo
1,108
Repo updated
First seen
Licence
None found

At a glance

Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying.

  • Works in 3 steps: Vector Storage → Similarity Search → Hybrid Search (Vector + Metadata)
  • Building RAG systems
  • SKILL.md covers What This Skill Does, Prerequisites, Quick Start with CLI and Quick Start with API, plus 9 more sections
  • Calls npx and claude

What it does

Agentdb Vector Search is an agent skill from aiskillstore/marketplace. Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases.

Its SKILL.md is about 2.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 1 other file (for example `skill-report.json`).

It sits in AI & LLM Engineering, covering Vector databases and Retrieval-augmented generation. The repository describes itself as: Security-audited skills for Claude, Codex & Claude Code. One-click install, quality verified.

When your agent uses it

  • Building RAG systems
  • Semantic search engines
  • Intelligent knowledge bases

Example prompts

  • “/agentdb-vector-search”

Requirements

  • Node.js

Workflow steps

3 steps, taken from the step headings in SKILL.md.

  1. Vector Storage
  2. Similarity Search
  3. Hybrid Search (Vector + Metadata)

What it can do on your machine

Read from SKILL.md and the folder at commit ad8daf7. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • npx
    • claude

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • github.com
    • agentdb.ruv.io

    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

Agentdb Vector Search loads about 2.2k tokens when it runs. Until then it costs about 60 tokens; SKILL.md has 292 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~60
When it runs · the whole SKILL.md, loaded when a task matches
~2.2k

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 passed

The automated check found no risky patterns in SKILL.md.

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

Without a licence we can't republish the file, so here is its outline and opening line. It has 292 words (~2,236 tokens).

“Implements vector-based semantic search using AgentDB's high-performance vector database with 150x-12,500x faster operations than traditional solutions. Features HNSW indexing, quantization, and sub-millisecond search (<100µs).”

— opening of SKILL.md by aiskillstore
name
agentdb-vector-search

Read the full SKILL.md on GitHub

Files

SKILL.md and 1 other file in skills/ruvnet/agentdb-vector-search of aiskillstore/marketplace.

  • SKILL.md
  • skill-report.json

Open the folder on GitHubat commit ad8daf7

Used in 11 other repositories

We found 32 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 7 other GitHub owners. This page covers the copy in aiskillstore/marketplace, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Agentdb 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.

Agentdb Vector Search compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Agentdb Vector Search this skillaiskillstore/marketplace4307 repos~2.2kAutomated safety check: PassNone
Chroma Vector DatabaseOrchestra-Research/AI-Research-SKILLs13k8 repos~2.3kAutomated safety check: PassMIT
Ms Agent Framework RAGshuyu-labs/WebCode278—~1.1kAutomated safety check: PassCustom licence
Pgvector Semantic Searchtimescale/pg-aiguide1.9k1 repos~3.8kAutomated safety check: PassApache-2.0
Convex Agentswaynesutton/builder-skills404—~2.2kAutomated safety check: PassApache-2.0
RAG ArchitectJeffallan/claude-skills12k1 repos~2kAutomated safety check: PassMIT

Similar skills

  • Chroma Vector Database

    Orchestra-Research/AI-Research-SKILLs

    Shows how to store documents and embeddings in Chroma, query them by similarity with metadata filters, and persist them to disk for RAG and semantic search projects.

    13k GitHub starsUsed in 8 repos~2.3k tokens
    AI & LLM EngineeringAuto-check passed

  • Ms Agent Framework RAG

    shuyu-labs/WebCode

    Comprehensive guide for building Agentic RAG systems using Microsoft Agent Framework in C.

    278 GitHub stars~1.1k tokensUpdated 3 mo ago
    AI & LLM EngineeringAuto-check passed

  • Pgvector Semantic Search

    timescale/pg-aiguide

    A skill your agent uses for setting up vector similarity search with pgvector for AI/ML embeddings, RAG applications, or semantic search.

    1.9k GitHub starsUsed in 1 repo~3.8k tokens
    AI & LLM EngineeringAuto-check passed

  • Convex Agents

    waynesutton/builder-skills

    Builds AI agents on the Convex agent component: threads, messages, tools that call queries and mutations, streaming, RAG with vector search, and workflows for multi step jobs.

    404 GitHub stars~2.2k tokensUpdated 10 days ago
    AI & LLM EngineeringAuto-check passed

  • RAG Architect

    Jeffallan/claude-skills

    Designs retrieval-augmented generation systems: document chunking, embeddings, vector store setup, hybrid search, reranking and retrieval evaluation, with checks at each step.

    12k GitHub starsUsed in 1 repo~2k tokens
    AI & LLM EngineeringAuto-check passed

  • Postgres Hybrid Text Search

    timescale/pg-aiguide

    A skill your agent uses to implement hybrid search combining BM25 keyword search with semantic vector search using Reciprocal Rank Fusion (RRF).

    1.9k GitHub stars~3.1k tokensUpdated yesterday
    AI & LLM EngineeringAuto-check passed

More from aiskillstore/marketplace

All 1,108 skills in this repo

  • Code Stats

    aiskillstore/marketplace

    Analyze codebase with tokei (fast line counts by language) and difft (semantic AST-aware diffs).

    430 GitHub starsUsed in 2 repos~697 tokens
    Auto-check: notes

  • File Search

    aiskillstore/marketplace

    Modern file and content search using fd, ripgrep (rg), and fzf.

    430 GitHub starsUsed in 2 repos~598 tokens
    Auto-check: notes

  • Data Processing

    aiskillstore/marketplace

    Process JSON with jq and YAML/TOML with yq. An agent skill from aiskillstore/marketplace.

    430 GitHub starsUsed in 1 repo~720 tokens
    Auto-check: notes

  • Doc Scanner

    aiskillstore/marketplace

    Scans for project documentation files (AGENTS.md, CLAUDE.md, GEMINI.md, COPILOT.md, CURSOR.md, WARP.md, and 15+ other formats) and synthesizes guidance.

    430 GitHub starsUsed in 1 repo~644 tokens
    Auto-check: notes

  • Find Replace

    aiskillstore/marketplace

    Modern find-and-replace using sd (simpler than sed) and batch replacement patterns.

    430 GitHub starsUsed in 1 repo~527 tokens
    Auto-check: notes

  • Investigating Codebases

    aiskillstore/marketplace

    Automatically activated when user asks how something works, wants to understand unfamiliar code, needs to explore a new codebase, or asks questions like "where is X implemented?", "how does Y…

    430 GitHub starsUsed in 1 repo~2.7k tokens
    Auto-check: notes

Categories

Questions about Agentdb Vector Search

What does Agentdb Vector Search do?

Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Agentdb Vector Search is an agent skill from aiskillstore/marketplace. Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying.

When should I use Agentdb Vector Search?

Agentdb Vector Search fits situations like: building RAG systems; semantic search engines; intelligent knowledge bases.

How do I install Agentdb Vector Search in Claude Code?

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

How do I install Agentdb Vector Search in Codex?

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

Can I use Agentdb 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 aiskillstore/marketplace --skill agentdb-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/agentdb-vector-search, .gemini/skills/agentdb-vector-search, .github/skills/agentdb-vector-search and .opencode/skills/agentdb-vector-search in your project.

What does Agentdb Vector Search need to run?

Going by SKILL.md and its folder, Agentdb Vector Search needs the command-line tools its instructions call (npx and claude). Our summary lists: Node.js.

Does Agentdb Vector Search access the network?

SKILL.md names 2 domains. As links in the text: github.com and agentdb.ruv.io. This is read from the text; nothing was executed.

Is Agentdb Vector Search safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Agentdb Vector Search use?

No licence was found for Agentdb Vector Search or its repository. Without one, default copyright applies: ask the author before reusing or redistributing it.

How many tokens does Agentdb Vector Search use?

About 2.2k tokens (SKILL.md is roughly 8.9k 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 Agentdb Vector Search?

Skills that share tags, products or a category with Agentdb Vector Search: Chroma Vector Database (Orchestra-Research/AI-Research-SKILLs, 13k stars), Ms Agent Framework RAG (shuyu-labs/WebCode, 278 stars), Pgvector Semantic Search (timescale/pg-aiguide, 1.9k stars) and Convex Agents (waynesutton/builder-skills, 404 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Agentdb Vector Search?

aiskillstore (a GitHub organization) maintains it in aiskillstore/marketplace, which has 430 GitHub stars. The repository holds 1,108 skills in this directory. The repository was last updated on October 7, 2026.

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