agents/find-skills/SKILL.md
Helps users discover and install agent skills when they ask questions like "how do I do X", "find a skill for X", "is there a skill that can...", or express interest in extending capabilities. This skill should be used when the user is looking for functionality that might exist as an installable skill.
npx skillsauth add adminlove520/xiaoxi-skills find-skillsInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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This skill helps you discover and install skills from the open agent skills ecosystem.
Use this skill when the user:
The Skills CLI (npx skills) is the package manager for the open agent skills ecosystem. Skills are modular packages that extend agent capabilities with specialized knowledge, workflows, and tools.
Key commands:
npx skills find [query] - Search for skills interactively or by keywordnpx skills add <package> - Install a skill from GitHub or other sourcesnpx skills check - Check for skill updatesnpx skills update - Update all installed skillsBrowse skills at: https://skills.sh/
When a user asks for help with something, identify:
Before running a CLI search, check the skills.sh leaderboard to see if a well-known skill already exists for the domain. The leaderboard ranks skills by total installs, surfacing the most popular and battle-tested options.
For example, top skills for web development include:
vercel-labs/agent-skills — React, Next.js, web design (100K+ installs each)anthropics/skills — Frontend design, document processing (100K+ installs)If the leaderboard doesn't cover the user's need, run the find command:
npx skills find [query]
For example:
npx skills find react performancenpx skills find pr reviewnpx skills find changelogDo not recommend a skill based solely on search results. Always verify:
vercel-labs, anthropics, microsoft) are more trustworthy than unknown authors.When you find relevant skills, present them to the user with:
Example response:
I found a skill that might help! The "react-best-practices" skill provides
React and Next.js performance optimization guidelines from Vercel Engineering.
(185K installs)
To install it:
npx skills add vercel-labs/agent-skills@react-best-practices
Learn more: https://skills.sh/vercel-labs/agent-skills/react-best-practices
If the user wants to proceed, you can install the skill for them:
npx skills add <owner/repo@skill> -g -y
The -g flag installs globally (user-level) and -y skips confirmation prompts.
When searching, consider these common categories:
| Category | Example Queries | | --------------- | ---------------------------------------- | | Web Development | react, nextjs, typescript, css, tailwind | | Testing | testing, jest, playwright, e2e | | DevOps | deploy, docker, kubernetes, ci-cd | | Documentation | docs, readme, changelog, api-docs | | Code Quality | review, lint, refactor, best-practices | | Design | ui, ux, design-system, accessibility | | Productivity | workflow, automation, git |
vercel-labs/agent-skills or ComposioHQ/awesome-claude-skillsIf no relevant skills exist:
npx skills initExample:
I searched for skills related to "xyz" but didn't find any matches.
I can still help you with this task directly! Would you like me to proceed?
If this is something you do often, you could create your own skill:
npx skills init my-xyz-skill
data-ai
Spaced-repetition flashcard system. Create cards from facts or text, chat with flashcards using free-text answers graded by the agent, generate quizzes from YouTube transcripts, review due cards with adaptive scheduling, and export/import decks as CSV.
development
Canvas LMS integration — fetch enrolled courses and assignments using API token authentication.
development
Provides PyTorch-native distributed LLM pretraining using torchtitan with 4D parallelism (FSDP2, TP, PP, CP). Use when pretraining Llama 3.1, DeepSeek V3, or custom models at scale from 8 to 512+ GPUs with Float8, torch.compile, and distributed checkpointing.
devops
Optimizes LLM inference with NVIDIA TensorRT for maximum throughput and lowest latency. Use for production deployment on NVIDIA GPUs (A100/H100), when you need 10-100x faster inference than PyTorch, or for serving models with quantization (FP8/INT4), in-flight batching, and multi-GPU scaling.