Headless data tables with server-side pagination, filtering, sorting, and virtualization for Cloudflare Workers + D1.
Works with
Supports three rendering modes: client-side with core row model, server-side with manual state management, and virtualized rendering for 1000+ row datasets
Includes column/row pinning, row expanding with nested data, and row grouping with built-in aggregation functions (sum, min, max, mean, etc.)
Integrates with TanStack Query for coordinated data fetching and state s
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versiontanstack-tableExecute the skills CLI command in your project's root directory to begin installation:
Fetches tanstack-table from jezweb/claude-skills 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:
Restart Cursor to activate tanstack-table. Access via /tanstack-table in your agent's command palette.
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.
Submit your Claude Code skill and start earning
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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Headless data tables with server-side pagination, filtering, sorting, and virtualization for Cloudflare Workers + D1
Last Updated: 2026-01-09 Versions: @tanstack/[email protected], @tanstack/[email protected]
npm install @tanstack/react-table@latest
npm install @tanstack/react-virtual@latest # For virtualization
Basic Setup (CRITICAL: memoize data/columns to prevent infinite re-renders):
import { useReactTable, getCoreRowModel, ColumnDef } from '@tanstack/react-table'
import { useMemo } from 'react'
const columns: ColumnDef<User>[] = [
{ accessorKey: 'name', header: 'Name' },
{ accessorKey: 'email', header: 'Email' },
]
function UsersTable() {
const data = useMemo(() => [...users], []) // Stable reference
const table = useReactTable({ data, columns, getCoreRowModel: getCoreRowModel() })
return (
<table>
<thead>
{table.getHeaderGroups().map(group => (
<tr key={group.id}>
{group.headers.map(h => <th key={h.id}>{h.column.columnDef.header}</th>)}
</tr>
))}
</thead>
<tbody>
{table.getRowModel().rows.map(row => (
<tr key={row.id}>
{row.getVisibleCells().map(cell => <td key={cell.id}>{cell.renderValue()}</td>)}
</tr>
))}
</tbody>
</table>
)
}
Cloudflare D1 API (pagination + filtering + sorting):
// Workers API: functions/api/users.ts
export async function onRequestGet({ request, env }) {
const url = new URL(request.url)
const page = Number(url.searchParams.get('page')) || 0
const pageSize = 20
const search = url.searchParams.get('search') || ''
const sortBy = url.searchParams.get('sortBy') || 'created_at'
const sortOrder = url.searchParams.get('sortOrder') || 'DESC'
const { results } = await env.DB.prepare(`
SELECT * FROM users
WHERE name LIKE ? OR email LIKE ?
ORDER BY ${sortBy} ${sortOrder}
LIMIT ? OFFSET ?
`).bind(`%${search}%`, `%${search}%`, pageSize, page * pageSize).all()
const { total } = await env.DB.prepare('SELECT COUNT(*) as total FROM users').first()
return Response.json({
data: results,
pagination: { page, pageSize, total, pageCount: Math.ceil(total / pageSize) },
})
}
Client-Side (TanStack Query + Table):
const [pagination, setPagination] = useState({ pageIndex: 0, pageSize: 20 })
const [columnFilters, setColumnFilters] = useState([])
const [sorting, setSorting] = useState([])
// CRITICAL: Include ALL state in query key
const { data, isLoading } = useQuery({
queryKey: ['users', pagination, columnFilters, sorting],
queryFn: async () => {
const params = new URLSearchParams({
page: pagination.pageIndex,
search: columnFilters.find(f => f.id === 'search')?.value || '',
sortBy: sorting[0]?.id || 'created_at',
sortOrder: sorting[0]?.desc ? 'DESC' : 'ASC',
})
return fetch(`/api/users?${params}`).then(r => r.json())
},
<Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
jezweb/claude-skills
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
ailabs-393/ai-labs-claude-skills
I recommend tanstack-table for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
tanstack-table has been reliable in day-to-day use. Documentation quality is above average for community skills.
Solid pick for teams standardizing on skills: tanstack-table is focused, and the summary matches what you get after install.
tanstack-table fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
tanstack-table has been reliable in day-to-day use. Documentation quality is above average for community skills.
tanstack-table reduced setup friction for our internal harness; good balance of opinion and flexibility.
I recommend tanstack-table for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
tanstack-table is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Useful defaults in tanstack-table — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
tanstack-table reduced setup friction for our internal harness; good balance of opinion and flexibility.
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