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 ashish7802/awesome-api-skills --skill qdrantgit clone --depth 1 https://github.com/ashish7802/awesome-api-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/ashish7802/awesome-api-skills/qdrant)<a href="https://agentmods.dev/skills/ashish7802/awesome-api-skills/qdrant"><img src="https://agentmods.dev/badge/skills/ashish7802/awesome-api-skills/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/ashish7802/awesome-api-skills/qdrant"><img src="https://agentmods.dev/badge/skills/ashish7802/awesome-api-skills/qdrant.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 | $0.00000 | $0.00652 |
| Opus 5 | $0.00000 | $0.00326 |
| Sonnet 5 | $0.00000 | $0.00130 |
| Haiku 4.5 | $0.00000 | $0.00065 |
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 yesterday.
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.
How it starts
The opening of the file, as written. The whole thing — 89 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Qdrant Vector Database API Skill
Overview
Qdrant is a production-grade vector similarity search engine with extended payload-based filtering, hybrid search (dense + sparse vectors), and multi-tenant collection partitioning.
Installation
npm install @qdrant/js-client-rest
pip install qdrant-client
Authentication
Connect using API keys for Qdrant Cloud or direct endpoint URLs for self-hosted instances.
import { QdrantClient } from '@qdrant/js-client-rest';
const client = new QdrantClient({
url: process.env.QDRANT_URL || 'http://localhost:6333',
apiKey: process.env.QDRANT_API_KEY,
});
Core API Operations
1. Create Collection
await client.createCollection('knowledge-base', {
vectors: {
size: 1536, // Match embedding model dimension
distance: 'Cosine',
},
optimizers_config: {
default_segment_number: 2,
},
replication_factor: 2,
});
2. Upsert Vector Points with Metadata Payload
await client.upsert('knowledge-base', {
wait: true,
points: [
{
id: 'doc-uuid-101',
vector: [0.012, -0.043, 0.089 /* 1536 dimensions */],
payload: {
document_id: 'doc-101',
title: 'Qdrant Architecture Overview',
tenant_id: 'team_alpha',
tags: ['vector-db', 'ai'],
created_at: Date.now(),
},
},
],
});
3. Vector Similarity Search with Payload Filtering
const searchResults = await client.search('knowledge-base', {
vector: queryEmbeddingVector,
limit: 5,
filter: {
must: [
{ key: 'tenant_id', match: { value: 'team_alpha' } },
{ key: 'tags', match: { any: ['ai'] } },
],
},
with_payload: true,
score_threshold: 0.75,
});
AI Pitfalls & Anti-Hallucination Guidelines
- Dimension Mismatches: Do not assume 1536 dimensions; verify if the embedding model outputs 768, 1536, or 3072 dimensions.
- Unindexed Payload Filters: Queries with
filteron non-indexed payload fields trigger full collection scans on large datasets. Always callcreatePayloadIndex. - Client Bundling: Never instantiate
QdrantClientwith write API keys in client-side React/Vue components.
What ships with it
1 file 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.
- yesterday First seen · 89 lines · 0 tokens per session scan A 7f62e3546d0c
qdrant is a skill published in the GitHub repository ashish7802/awesome-api-skills (13 stars, last pushed today), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 652 tokens. 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 2026-09-10.
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