agentmods.dev

qdrant-search

qdrant-search is a skill for Claude Code from dipseth/google_workspace_fastmcp2. It costs 29 tokens per session (149 once invoked), scanned A, original, Apache-2.0.

A guide for semantic search, which finds related information by meaning rather than only matching exact words, across saved Google Workspace tool responses using Qdrant.

In plain words
What is it for?
Use it to search response history, apply filters, build queries, and find recommendations from past Google Workspace operations.
Why use it?
It helps you retrieve relevant past results when you do not remember the exact wording or stored response.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the google-workspace-unlimited plugin — 3 skills, 1 MCP server shipped together

not rated 25 2d ago A scan 29 tokens original Apache-2.0
View the source file dipseth/google_workspace_fastmcp2
Install

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.

agentmods
npx agentmods add skills/dipseth/google_workspace_fastmcp2/qdrant-search
Any agent
npx skills add dipseth/google_workspace_fastmcp2 --skill qdrant-search
Clone the repo
git clone --depth 1 https://github.com/dipseth/google_workspace_fastmcp2

Made for: Claude Code.

Or install google-workspace-unlimited, the plugin that ships this one along with the rest of its 3 skills, 1 MCP server.

Wrote 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.

agentmods badge for qdrant-search

README.md
[![agentmods](https://agentmods.dev/badge/skills/dipseth/google_workspace_fastmcp2/qdrant-search.svg)](https://agentmods.dev/skills/dipseth/google_workspace_fastmcp2/qdrant-search)
Your own site
<a href="https://agentmods.dev/skills/dipseth/google_workspace_fastmcp2/qdrant-search"><img src="https://agentmods.dev/badge/skills/dipseth/google_workspace_fastmcp2/qdrant-search.svg" alt="Measured on agentmods" height="20"></a>
Per session 29 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 149 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00029 $0.00149
Opus 5 $0.00015 $0.00075
Sonnet 5 $0.00006 $0.00030
Haiku 4.5 $0.00003 $0.00015

Measured 6d ago against content hash d8cc146c26e8, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-06, from the pricing page.

Security

Grade A, and why

qdrant-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 6d 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.

plugins/google-workspace-unlimited/skills/qdrant-search/SKILL.md · 19 lines

What it actually says

Quick Start

This skill provides documentation for the qdrant_client.models module.

Available Resources

  • symbols.md - Complete symbol reference table
  • containment-rules.md - What components can contain what
  • qdrant-dsl-syntax.md - Custom guide: qdrant-dsl-syntax
  • qdrant-dsl-params.md - Custom guide: qdrant-dsl-params
  • components/ - Per-component documentation (392 components)
Files

What ships with it

60 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

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.

  1. 6d ago First seen · 19 lines · 29 tokens per session scan A d8cc146c26e8

Subscribe to this mod's changes

qdrant-search is a skill published in the GitHub repository dipseth/google_workspace_fastmcp2 (25 stars, last pushed 2d ago), licensed Apache-2.0. It adds 29 tokens to every session and 149 once invoked, about $0.0001 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.

Data and AIRAG and vectors
Related

Other skills, from other repositories

embeddings

Vector embeddings with HNSW indexing, sql.js persistence, and hyperbolic support. 75x faster with agentic-flow integration. Use when: semantic search, pattern matching, similarity queries, knowledge retrieval. Skip when: exact text matching, simple lookups, no semantic understanding needed.

ruvnet/ruflo · 62 tokens

agent-platform-rag-engine-management

Manage and query Agent Platform RAG Engine Corpora and retrieve grounded contexts using the Google GenAI SDK. Use when listing RAG corpora or files, inspecting a corpus, retrieving contexts, or generating content grounded in a RAG corpus. Do not use for standard database queries (use SQL/Spanner skills), Google…

google/skills · 85 tokens

llm-app-patterns

Production-ready patterns for building LLM applications. Covers RAG pipelines, agent architectures, prompt IDEs, and LLMOps monitoring. Use when designing AI applications, implementing RAG, building agents, or setting up LLM observability.

davila7/claude-code-templates · 54 tokens

9router-embeddings

Generate vector embeddings via 9Router /v1/embeddings using OpenAI / Gemini / Mistral / Voyage / Nvidia / GitHub embedding models for RAG, semantic search, similarity. Use when the user wants embeddings, vectors, RAG, semantic search, or to embed text.

decolua/9router · 66 tokens

azure-search-documents-dotnet

Azure AI Search SDK for .NET (Azure.Search.Documents). Use for building search applications with full-text, vector, semantic, and hybrid search. Covers SearchClient (queries, document CRUD), SearchIndexClient (index management), and SearchIndexerClient (indexers, skillsets). Triggers: "Azure Search .NET"…

microsoft/skills · 102 tokens

similarity-search-patterns

Implement efficient similarity search with vector databases. Use when building semantic search, implementing nearest neighbor queries, or optimizing retrieval performance.

foryourhealth111-pixel/Vibe-Skills · 30 tokens