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.

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

It directs you to the relevant guidance for storing searchable vectors, tuning retrieval, securing the service, and operating it reliably.

qdrant — Skill by itechmeat

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/itechmeat/llm-code/qdrant

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.

Per session 80 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 1,142 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.

Model	Per session	Once invoked
Fable 5	$0.00080	$0.01142
Opus 5	$0.00040	$0.00571
Sonnet 5	$0.00016	$0.00228
Haiku 4.5	$0.00008	$0.00114

Measured 2d ago against content hash 4d33c3194832, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, 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 2d 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 · 98 lines

How it starts

The opening of the file, as written. The whole thing — 98 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 (1.16.3 → 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)

1.18.2 security: fixes a REST auth whitelist bypass on specially crafted paths and a heap-read vulnerability with malformed snapshots. Upgrade promptly if Qdrant is exposed with auth/whitelisting or accepts uploaded snapshots.
1.18.2: logs slow operations during shard WAL recovery and clears the ID-tracker cache after building segments.
Filter behavior is corrected for indexed integer range filters that receive float values and for {match: {except: []}} on payload-indexed fields.
Empty vector requests no longer trigger a panic path; treat them as invalid input and validate caller-side before sending them to Qdrant.
TurboQuant heap-memory reporting is more accurate, so operators should trust current metrics over older baselines when checking compression impact.
Snapshot upload authorization is tightened; do not assume restore/upload endpoints are safe without the same auth review you apply to the main API surface.

Breaking / Upgrade Notes (1.17.0)

gRPC clients: response format for vector fields changed in gRPC. Upgrade official Qdrant client libraries and validate any custom gRPC integrations.
Storage upgrades: RocksDB is removed in favor of gridstore. If you are on v1.15.x, do not upgrade directly to v1.17.x — upgrade one minor version at a time.

Read the full file on GitHub · 98 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.

2d 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 (22 stars, last pushed 1mo ago), licensed MIT. It adds 80 tokens to every session and 1,142 once invoked, about $0.0004 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.

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