Score breakdown0 Version history1 Source
Category Weight Category score Contribution
Security prompt, exec, net, exfil, eval
35%
100
35.0 pts
Supply chain hash, typosquat, maintainer, lockfile
20%
100
20.0 pts
Maintenance staleness, pinning, CI
15%
100
15.0 pts
Transparency SKILL.md, perms, README
15%
100
15.0 pts
Community installs, verify, response
15%
100
15.0 pts
Findings & checks · 0 flagged
Security score 100 · 0 findings
✓ — All security checks passedNo findings in this category for the latest scan. pass
Supply chain score 100 · 0 findings
✓ — All supply chain checks passedNo findings in this category for the latest scan. pass
Maintenance score 100 · 0 findings
✓ — All maintenance checks passedNo findings in this category for the latest scan. pass
Transparency score 100 · 0 findings
✓ — All transparency checks passedNo findings in this category for the latest scan. pass
Community score 100 · 0 findings
✓ — All community checks passedNo findings in this category for the latest scan. pass
Every scanned point with the score it earned and what moved between them.
1 scans · 90 days d97a2e4 latest
Jun 23, 2026 100 d97a2e4
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
library/specializations/ai-agents-conversational/skills/qdrant-integration/SKILL.md · 1 file library/specializations/ai-agents-conversational/skills/qdrant-integration/SKILL.md 1.3 KB · Markdown Rendered Raw ⧉ Copy
name : qdrant-integration
description : Qdrant vector database with filtering, payloads, and quantization support
allowed-tools :
- Read
- Write
- Edit
- Bash
- Glob
- Grep
Qdrant Integration Skill Capabilities Set up Qdrant (local, cloud, self-hosted) Create collections with configuration Implement advanced filtering with payloads Configure quantization for efficiency Set up sparse vectors for hybrid search Implement batch operations and optimization Target Processes vector-database-setup rag-pipeline-implementation Implementation Details Deployment Modes Local Memory : For testingLocal Disk : Persistent local storageQdrant Cloud : Managed serviceSelf-Hosted : Docker/Kubernetes deploymentCore Operations Collection management with parameters Point upsert with vectors and payloads Search with filters (must, should, must_not) Scroll for pagination Batch operations Configuration Options Vector parameters (size, distance) Quantization (scalar, product) Sparse vector configuration Payload indexes Replication and sharding Best Practices Use quantization for large collections Design payload indexes for filters Implement proper batch sizes Configure appropriate distance metrics Dependencies qdrant-client langchain-qdrant