vLLM achieves 24x higher throughput than standard transformers through PagedAttention (block-based KV cache) and continuous batching (mixing prefill/decode requests).
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 serving-llms-vllmCompatible with
Supported agents
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionserving-llms-vllmExecute the skills CLI command in your project's root directory to begin installation:
epx add davila7/claude-code-templates --skill serving-llms-vllmFetches serving-llms-vllm 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/serving-llms-vllm
Restart Cursor to activate serving-llms-vllm. Access via /serving-llms-vllmin 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.
excalidraw
davila7/claude-code-templates · Productivity · same repo
axolotl
davila7/claude-code-templates · Productivity · same repo
scientific-slides
davila7/claude-code-templates · Productivity · same repo
backend-to-frontend-handoff-docs
davila7/claude-code-templates · Frontend · same repo
manim
davila7/claude-code-templates · Productivity · same repo
idor-vulnerability-testing
davila7/claude-code-templates · Testing · same repo
I recommend serving-llms-vllm for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
serving-llms-vllm fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
serving-llms-vllm fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
serving-llms-vllm reduced setup friction for our internal harness; good balance of opinion and flexibility.
Solid pick for teams standardizing on skills: serving-llms-vllm is focused, and the summary matches what you get after install.
serving-llms-vllm is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Keeps context tight: serving-llms-vllm is the kind of skill you can hand to a new teammate without a long onboarding doc.
serving-llms-vllm has been reliable in day-to-day use. Documentation quality is above average for community skills.
serving-llms-vllm fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
serving-llms-vllm reduced setup friction for our internal harness; good balance of opinion and flexibility.
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