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.
npx skills add cookieMonsterDev/agents-skills --skill qdrant-docsgit clone --depth 1 https://github.com/cookieMonsterDev/agents-skillsWrote 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.
[](https://agentmods.dev/skills/cookiemonsterdev/agents-skills/qdrant-docs)<a href="https://agentmods.dev/skills/cookiemonsterdev/agents-skills/qdrant-docs"><img src="https://agentmods.dev/badge/skills/cookiemonsterdev/agents-skills/qdrant-docs/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.
<a href="https://agentmods.dev/skills/cookiemonsterdev/agents-skills/qdrant-docs"><img src="https://agentmods.dev/badge/skills/cookiemonsterdev/agents-skills/qdrant-docs.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 5.1 | $0.00086 | $0.01231 |
| Opus 5.5 | $0.00034 | $0.00492 |
| Sonnet 5.5 | $0.00017 | $0.00246 |
| Haiku 4.5 | $0.00009 | $0.00123 |
Grade A, and why
qdrant-docs scanned grade A with 1 finding 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- Only provide client code or `curl` examples when the user asks for them or when the docs page makes an example necessary. How it starts
The opening of the file, as written. The whole thing — 103 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Qdrant questions are easy to answer from stale memory or from patterns borrowed from a different vector database. Use this skill to ground answers in the official Qdrant documentation and return the closest authoritative page instead of generic vector-search advice.
When to Use
Use this skill when the request is about:
- Qdrant concepts: collections, points, vectors, payloads, and named vectors
- Search, filtering, hybrid queries, recommendation, and discovery API behavior
- Indexing: HNSW parameters, payload indexes, sparse vectors, and quantization (scalar, product, binary)
- Storage, snapshots, backups, and the write-ahead log
- Distributed deployment, sharding, replication, and consistency guarantees
- Multitenancy and payload-based partitioning
- Client libraries (Python, JavaScript/TypeScript, Go, Rust, Java, .NET) and the REST/gRPC APIs
- FastEmbed, built-in inference, and hybrid text search (BM25 plus dense vectors)
- Qdrant Cloud provisioning, RBAC, and Qdrant Edge/on-device deployments
- Security: API keys, TLS, and role-based access control
Do not use this skill for:
- Pinecone-specific concepts (serverless index architecture, namespaces-as-tenants). Use
pinecone-docsinstead. - Weaviate-specific concepts (GraphQL API, modules, classes as collections). Use
weaviate-docsinstead. - Redis vector sets or RediSearch vector fields. Use
redis-docsinstead. - General embedding model selection or RAG architecture unless the question is specifically about Qdrant configuration or behavior.
Workflow
1. Classify the request
Decide which bucket the question belongs to before searching:
- Getting started, quickstart, and local/cloud setup
- Data model: collections, points, vectors, payload
- Search: similarity search, filtering, hybrid queries, recommendation
- Indexing and performance: HNSW, quantization, payload indexes
- Storage and operations: snapshots, backups, distributed deployment
- Client libraries and API reference
- Qdrant Cloud and Qdrant Edge
- Security and access control
What ships with it
2 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.
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.
- 1mo ago First seen · 103 lines · 86 tokens per session scan A c3dfc1d9f478
qdrant-docs is a skill published in the GitHub repository cookieMonsterDev/agents-skills (6 stars, last pushed today), licensed MIT. It adds 86 tokens to every session and 1,231 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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