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 yoanbernabeu/grepai-skills --skill grepai-storage-qdrantgit clone --depth 1 https://github.com/yoanbernabeu/grepai-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/yoanbernabeu/grepai-skills/grepai-storage-qdrant)<a href="https://agentmods.dev/skills/yoanbernabeu/grepai-skills/grepai-storage-qdrant"><img src="https://agentmods.dev/badge/skills/yoanbernabeu/grepai-skills/grepai-storage-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.
<a href="https://agentmods.dev/skills/yoanbernabeu/grepai-skills/grepai-storage-qdrant"><img src="https://agentmods.dev/badge/skills/yoanbernabeu/grepai-skills/grepai-storage-qdrant.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
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 | $0.00027 | $0.02024 |
| Opus 5 | $0.00014 | $0.01012 |
| Sonnet 5 | $0.00005 | $0.00405 |
| Haiku 4.5 | $0.00003 | $0.00202 |
Grade A, and why
grepai-storage-qdrant 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 11d 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.
curl http://localhost:6333/health How it starts
The opening of the file, as written. The whole thing — 365 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GrepAI Storage with Qdrant
This skill covers using Qdrant as the storage backend for GrepAI, offering high-performance vector search.
When to Use This Skill
- Need fastest possible search performance
- Very large codebases (50K+ files)
- Already using Qdrant infrastructure
- Want advanced vector search features
What is Qdrant?
Qdrant is a purpose-built vector database offering:
- ⚡ Extremely fast vector similarity search
- 📏 Excellent scalability
- 🔧 Advanced filtering capabilities
- 🐳 Easy Docker deployment
Prerequisites
- Qdrant server running
- Network access to Qdrant
Advantages
| Benefit | Description |
|---|---|
| ⚡ Performance | Fastest vector search |
| 📏 Scalability | Handles millions of vectors |
| 🔍 Advanced | Filtering, payloads, sharding |
| 🐳 Easy deploy | Docker-ready |
| ☁️ Cloud option | Qdrant Cloud available |
Setting Up Qdrant
Option 1: Docker (Recommended)
# Run Qdrant with persistent storage
docker run -d \
--name grepai-qdrant \
-p 6333:6333 \
-p 6334:6334 \
-v qdrant_storage:/qdrant/storage \
qdrant/qdrant
Ports:
6333: REST API6334: gRPC API (used by GrepAI)
Option 2: Docker Compose
# docker-compose.yml
version: '3.8'
services:
qdrant:
image: qdrant/qdrant
ports:
- "6333:6333"
- "6334:6334"
volumes:
- qdrant_storage:/qdrant/storage
environment:
- QDRANT__SERVICE__GRPC_PORT=6334
volumes:
qdrant_storage:
docker-compose up -d
Option 3: Qdrant Cloud
- Sign up at cloud.qdrant.io
- Create a cluster
- Get your endpoint and API key
Configuration
Basic Configuration (Local)
# .grepai/config.yaml
store:
backend: qdrant
qdrant:
endpoint: localhost
port: 6334
With TLS (Production)
store:
backend: qdrant
qdrant:
endpoint: qdrant.company.com
port: 6334
use_tls: true
With API Key (Qdrant Cloud)
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.
- 11d ago First seen · 365 lines · 27 tokens per session scan A d4ac2e773404
grepai-storage-qdrant is a skill published in the GitHub repository yoanbernabeu/grepai-skills (20 stars, last pushed 7mo ago), licensed MIT. It adds 27 tokens to every session and 2,024 once invoked, about $0.0001 per session on Opus 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 2026-08-30.
Other skills, from other repositories
qdrant-clients-sdk
Qdrant provides client SDKs for various programming languages, allowing easy integration with Qdrant deployments.
qdrant-hybrid-search-prefetches
Constructing prefetch queries for hybrid retrieval, including sparse/dense and multi-field setups, and choosing a sparse embedding model. Use when someone asks 'dense and sparse in one search?', 'how to combine multiple fields for retrieval?', 'payloads or sparse vectors for lexical?', 'which sparse embedding model to…
qdrant-sizing
Sizes a Qdrant deployment before it is provisioned. Use when someone asks 'how much RAM do I need', 'how many nodes', 'how big should my cluster be', 'sizing', 'capacity planning', 'will N vectors fit', 'what instance type should I pick', or gives a vector count and dimensions and asks what to provision. Also use when…
qdrant-advisor
Diagnose, troubleshoot, and advise on any Qdrant deployment by loading the latest official Qdrant skills live from skills.qdrant.tech. Use this whenever someone raises a Qdrant problem or question — slow or degraded search, high or growing memory / OOM crashes, optimizer stuck or slow, indexing slowness, scaling and…
qdrant-model-migration
Guides embedding model migration in Qdrant without downtime. Use when someone asks 'how to switch embedding models', 'how to migrate vectors', 'how to update to a new model', 'zero-downtime model change', 'how to re-embed my data', or 'can I use two models at once'. Also use when upgrading model dimensions, switching…
qdrant-multitenancy
Guides tenant isolation architecture in Qdrant for multi-tenant or multi-user applications. Use when someone asks 'how to isolate customer data', 'how to build multi-tenant search/RAG', 'how many collections should I create', 'how to partition tenants by payload', 'a customer's data legally has to stay in a certain…