vector-index-tuning
Guide to optimizing vector indexes for production performance.
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Installation Guide
How to use vector-index-tuning on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your machine
- ›Node.js 16+ with npm — verify with
node --version - ›Active project directory where you want to add
vector-index-tuning
Run the install command
Execute the skills CLI command in your project's root directory to begin installation:
Fetches vector-index-tuning from sickn33/antigravity-awesome-skills and configures it for Cursor.
Select Cursor when prompted
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate vector-index-tuning. Access via /vector-index-tuning in your agent's command palette.
Security Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Documentation
Vector Index Tuning
Guide to optimizing vector indexes for production performance.
Use this skill when
- Tuning HNSW parameters
- Implementing quantization
- Optimizing memory usage
- Reducing search latency
- Balancing recall vs speed
- Scaling to billions of vectors
Do not use this skill when
- You only need exact search on small datasets (use a flat index)
- You lack workload metrics or ground truth to validate recall
- You need end-to-end retrieval system design beyond index tuning
Instructions
- Gather workload targets (latency, recall, QPS), data size, and memory budget.
- Choose an index type and establish a baseline with default parameters.
- Benchmark parameter sweeps using real queries and track recall, latency, and memory.
- Validate changes on a staging dataset before rolling out to production.
Refer to resources/implementation-playbook.md for detailed patterns, checklists, and templates.
Safety
- Avoid reindexing in production without a rollback plan.
- Validate changes under realistic load before applying globally.
- Track recall regressions and revert if quality drops.
Resources
resources/implementation-playbook.mdfor detailed patterns, checklists, and templates.
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Use Cases
Task Automation & Efficiency
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Knowledge Enhancement
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Quality Improvement
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
Implementation Guide
Prerequisites
- ›Claude Desktop or compatible AI client with skill support
- ›Clear understanding of task or problem to solve
- ›Willingness to iterate and refine outputs
Time Estimate
15-45 minutes depending on use case complexity
Steps
- 1Install skill using provided installation command
- 2Test with simple use case relevant to your work
- 3Evaluate output quality and relevance
- 4Iterate on prompts to improve results
- 5Integrate into regular workflow if valuable
Common Pitfalls
- ⚠Expecting perfect results without iteration
- ⚠Not providing enough context in prompts
- ⚠Using skill for tasks outside its intended scope
- ⚠Accepting outputs without review and validation
Best Practices
✓ Do
- +Start with clear, specific prompts
- +Provide relevant context and constraints
- +Review and refine all outputs before using
- +Iterate to improve output quality
- +Document successful prompt patterns
✗ Don't
- −Don't use without understanding skill limitations
- −Don't skip validation of outputs
- −Don't share sensitive information in prompts
- −Don't expect skill to replace human judgment
💡 Pro Tips
- ★Be specific about desired format and style
- ★Ask for multiple options to choose from
- ★Request explanations to understand reasoning
- ★Combine AI efficiency with human expertise
When to Use This
✓ 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.
Learning Path
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
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Reviews
- DDiya Kim★★★★★Dec 28, 2024
Useful defaults in vector-index-tuning — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- KKabir Reddy★★★★★Dec 24, 2024
Solid pick for teams standardizing on skills: vector-index-tuning is focused, and the summary matches what you get after install.
- YYusuf Malhotra★★★★★Dec 24, 2024
I recommend vector-index-tuning for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- CChaitanya Patil★★★★★Dec 16, 2024
vector-index-tuning fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ZZaid Lopez★★★★★Dec 16, 2024
vector-index-tuning reduced setup friction for our internal harness; good balance of opinion and flexibility.
- AAmina Ghosh★★★★★Dec 4, 2024
vector-index-tuning has been reliable in day-to-day use. Documentation quality is above average for community skills.
- NNikhil Park★★★★★Nov 19, 2024
Registry listing for vector-index-tuning matched our evaluation — installs cleanly and behaves as described in the markdown.
- KKofi Kim★★★★★Nov 15, 2024
vector-index-tuning reduced setup friction for our internal harness; good balance of opinion and flexibility.
- PPiyush G★★★★★Nov 7, 2024
vector-index-tuning is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- KKabir Anderson★★★★★Nov 7, 2024
I recommend vector-index-tuning for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
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