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qdrant

qdrant is a skill for Claude Code, Codex from itechmeat/llm-code. It costs 80 tokens per session (1,422 once invoked), scanned A, original, MIT.

A router and operations guide for Qdrant, a vector database used to store and search numerical representations of content. It covers collections, filtering, indexing, compression, security, monitoring, snapshots, and cluster deployment.

In plain words
What is it for?
Use it to manage collections and points, filter searches, configure HNSW indexes or quantization, monitor clusters, handle snapshots, and deploy with Docker or Kubernetes.
Why use it?
It directs you to the relevant guidance for storing searchable vectors, tuning retrieval, securing the service, and operating it reliably.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

not rated 24repo changed 1mo ago A scan SkillSpector: pass 80 tokens original MIT

Good fit Use it to manage collections and points, filter searches, configure HNSW indexes or quantization, monitor clusters, handle snapshots, and deploy with Docker or Kubernetes.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/itechmeat/llm-code/qdrant
View source ↗ itechmeat/llm-code
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.

Any agent
npx skills add itechmeat/llm-code --skill qdrant
Clone the repo
git clone --depth 1 https://github.com/itechmeat/llm-code

Made for: Claude Code, Codex.

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

README.md
[![agentmods](https://agentmods.dev/badge/skills/itechmeat/llm-code/qdrant/github.svg)](https://agentmods.dev/skills/itechmeat/llm-code/qdrant)
Your own site
<a href="https://agentmods.dev/skills/itechmeat/llm-code/qdrant"><img src="https://agentmods.dev/badge/skills/itechmeat/llm-code/qdrant/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.

agentmods 80×15 button for qdrant

Your own site · 80×15
<a href="https://agentmods.dev/skills/itechmeat/llm-code/qdrant"><img src="https://agentmods.dev/badge/skills/itechmeat/llm-code/qdrant.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 80 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,422 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
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 $0.00080 $0.01422
Opus 5.5 $0.00032 $0.00569
Sonnet 5.5 $0.00016 $0.00284
Haiku 4.5 $0.00008 $0.00142

Measured 1mo ago against content hash 1ab12ace224c, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

qdrant 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.

skills/qdrant/SKILL.md · 109 lines

How it starts

The opening of the file, as written. The whole thing — 109 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Qdrant (Skill Router)

This file is intentionally introductory.

It acts as a router: based on your situation, open the right note under references/.

Release Highlights (1.19.0 → 1.19.1)

  • TurboQuant 4-bit as primary storage: the "turbo4" datatype stores only 4-bit quantized vectors, sparing disk space on originals.
  • Per-component memory strategy: collection components now take "memory": "cold" / "cached" / "pinned" for fine-grained control over memory vs performance. Deprecates max_resident_memory_percent of strict mode in favor of the new global quota API.
  • Keyword prefix match: {"match": {"prefix": "..."}} in filters matches keywords by prefix; must be enabled in the keyword index.
  • Sparse search: per-query IDF corpus for better per-tenant/sparse ranking.
  • Slice filtering: sliced scroll / deterministic sampling via a slice filter condition.
  • Global quota API: central place to cap cluster resource usage (supersedes the strict-mode memory ceiling).
  • Routing token: deterministic read routes when the read affinity option is in use.
  • 1.19.1: 4-bit TurboQuant SIMD rework and batched HNSW searches (faster scoring), quantized scoring prefetch, plus input hardening — rejects empty dense vectors, vectors larger than 65536, and ./.. in collection names (security).

Release Highlights (1.16.3 → 1.18.0)

  • Monitoring + ops: new APIs for optimization progress/stages and cluster-wide telemetry, plus a dedicated HTTP port option for /metrics.
  • Security: audit access logging and secondary API key support (rotation).
  • Retrieval: relevance feedback and Weighted RRF for hybrid ranking.
  • Write semantics: update_mode for upserts (upsert / update / insert).
  • 1.18.0: TurboQuant adds an aggressive vector-compression path, collections can add/delete named vectors in place, and operators get low-memory/strict-memory knobs plus deeper memory reporting.

Patch Notes (1.18.1 → 1.18.2)

Read the full file on GitHub · 109 lines

Files

What ships with it

16 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. 1mo ago Changed · +11 lines 1ab12ace224c
  2. 1mo ago First seen · 98 lines · 80 tokens per session scan A 4d33c3194832

Subscribe to this mod's changes

qdrant is a skill published in the GitHub repository itechmeat/llm-code (24 stars, last pushed 24d ago), licensed MIT. It adds 80 tokens to every session and 1,422 once invoked, about $0.0003 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 2026-08-30.

Data and AIDatabasesRAG and vectors
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