tanstack-query

ICGNU3/werhiz-identity

TanStack Query v5 patterns for data fetching, caching, and server state management. Use when working with useQuery, useMutation, useSuspenseQuery, query invalidation, cache management, or React Query patterns.

0 stars

0 forks

9 views

View on GitHub Add to Favorites

Installation

Option 1: Use slash command in Claude Code

/install-skill https://github.com/ICGNU3/werhiz-identity/tree/main/.claude/skills/tanstack-query

Option 2: Clone to skills directory

# Global (all projects)

git clone https://github.com/ICGNU3/werhiz-identity/tree/main/.claude/skills/tanstack-query ~/.claude/skills/werhiz-identity

# Project-specific

git clone https://github.com/ICGNU3/werhiz-identity/tree/main/.claude/skills/tanstack-query .claude/skills/werhiz-identity

Add MCP server to .cursor/mcp.json:

{
  "mcpServers": {
    "skillz": {
      "command": "npx",
      "args": ["-y", "skillz-mcp", "https://github.com/ICGNU3/werhiz-identity/tree/main/.claude/skills/tanstack-query"]
    }
  }
}

Restart Cursor after adding the configuration.

Option 1: Use Gemini CLI command

gemini extensions install https://github.com/ICGNU3/werhiz-identity/tree/main/.claude/skills/tanstack-query

Option 2: Clone to extensions directory

git clone https://github.com/ICGNU3/werhiz-identity/tree/main/.claude/skills/tanstack-query ~/.gemini/extensions/werhiz-identity

Related Skills

skill-writer

Tensors and Dynamic neural networks in Python with strong GPU acceleration

docstring

Tensors and Dynamic neural networks in Python with strong GPU acceleration

at-dispatch-v2

Tensors and Dynamic neural networks in Python with strong GPU acceleration

add-uint-support

Tensors and Dynamic neural networks in Python with strong GPU acceleration