Rust-powered vector database for production RAG with hybrid search and distributed scaling.
Automate repetitive workflows and reduce manual effort
Example: Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Learn new skills, understand complex topics, get expert guidance
Example: Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Enhance output quality through reviews, suggestions, and refinements
Example: Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
Time estimate: 15-45 minutes depending on use case complexity
Prerequisites
Submit your Claude Code skill and start earning
epx add davila7/claude-code-templates --skill qdrant-vector-searchCompatible with
Supported agents
Supports dense, sparse, and multi-vector storage per point with four distance metrics (COSINE, EUCLID, DOT, MANHATTAN) and HNSW indexing for fast nearest-neighbor search
Rich filtering during search across any payload field, with optional payload indexing for performance and support for complex boolean queries
Quantization options (scalar, product, binary) and on-disk storage for memory effici
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionqdrant-vector-searchExecute the skills CLI command in your project's root directory to begin installation:
epx add davila7/claude-code-templates --skill qdrant-vector-searchFetches qdrant-vector-search from davila7/claude-code-templates and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
.cursor/skills/qdrant-vector-search
Restart Cursor to activate qdrant-vector-search. Access via /qdrant-vector-searchin your agent's command palette.
Learn more:View source on GitHubSkills CLI docsAbout CursorWhat are agent skills?
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
ml-paper-writing
davila7/claude-code-templates · AI/ML · same repo
ethical-hacking-methodology
davila7/claude-code-templates · Productivity · same repo
plotly
davila7/claude-code-templates · Productivity · same repo
axolotl
davila7/claude-code-templates · Productivity · same repo
boss-job-search
agentbay-ai/agentbay-skills · Productivity · tag: search
web-search
inference-sh/skills · Productivity · tag: search
qdrant-vector-search reduced setup friction for our internal harness; good balance of opinion and flexibility.
qdrant-vector-search fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
qdrant-vector-search has been reliable in day-to-day use. Documentation quality is above average for community skills.
I recommend qdrant-vector-search for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Useful defaults in qdrant-vector-search — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
qdrant-vector-search has been reliable in day-to-day use. Documentation quality is above average for community skills.
We added qdrant-vector-search from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
qdrant-vector-search reduced setup friction for our internal harness; good balance of opinion and flexibility.
Solid pick for teams standardizing on skills: qdrant-vector-search is focused, and the summary matches what you get after install.
I recommend qdrant-vector-search for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
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