TanStack Table is a headless table library: it supplies table and datagrid logic while leaving the markup, styling, and components to the application. Developers use it with JavaScript and TypeScript frameworks to build customizable tables with features such as sorting, filtering, grouping, aggregation, and row selection. Its catalogue skills provide version-matched guidance for coding agents working with the library.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add TanStack/table --skill coregit clone --depth 1 https://github.com/TanStack/tableWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/tanstack/table/core)<a href="https://agentmods.dev/skills/tanstack/table/core"><img src="https://agentmods.dev/badge/skills/tanstack/table/core/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/tanstack/table/core"><img src="https://agentmods.dev/badge/skills/tanstack/table/core.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk pass
- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00038 | $0.01004 |
| Opus 5.5 | $0.00015 | $0.00402 |
| Sonnet 5.5 | $0.00008 | $0.00201 |
| Haiku 4.5 | $0.00004 | $0.00100 |
Grade A, and why
core scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 19d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 85 lines — stays where its author put it; the contents beside it link to each section on GitHub.
TanStack Table core
TanStack Table coordinates state and row processing. The renderer owns markup, styles, semantics, and interaction accessibility. Use the installed framework adapter in UI code; use constructTable for framework-neutral integrations.
Minimal setup
import {
constructTable,
createColumnHelper,
tableFeatures,
} from '@tanstack/table-core'
import { storeReactivityBindings } from '@tanstack/table-core/store-reactivity-bindings'
type Person = { id: string; name: string }
const features = tableFeatures({
coreReactivityFeature: storeReactivityBindings(),
})
const helper = createColumnHelper<typeof features, Person>()
const columns = helper.columns([helper.accessor('name', { header: 'Name' })])
const data: Person[] = [{ id: '1', name: 'Ada' }]
const table = constructTable({
features,
columns,
data,
getRowId: (row) => row.id,
})
for (const row of table.getRowModel().rows) {
console.log(row.getAllCells().map((cell) => cell.getValue()))
}
Essential constraints
- The core row model is automatic. Optional APIs and state exist only after their feature is registered.
- Keep
features,data, andcolumnsstable between meaningful changes. Derive changing arrays with the adapter's memo/computed mechanism; an inline.map(),.filter(), column factory, or fresh[]fallback invalidates model work and can cause render loops. - Let
createColumnHelperandhelper.columns()preserve accessor value types. Derive feature types from the concrete registry when an explicit boundary is needed. - Call row, cell, column, and header methods on their instance. Use
row.getValue('name')or a callback that calls it; extractingconst { getValue } = rowloses itsthisreceiver. - Render the final
table.getRowModel().rows. The table object itself is not a DOM component.
Read for the current task
| Task | Read |
|---|---|
| Add or repair optional features, processing slots, or prerequisites | Feature architecture, then its relevant feature reference |
| Choose state ownership, initialize or reset state | Shared state |
Fix ColumnDef inference, reusable options, or scoped meta |
TypeScript inference |
| Diagnose a missing export, option, state slice, or instance method | API discovery |
| Implement row numbers, identity, or display-index behavior | Rows |
| Author behavior beyond built-ins or typed meta | Custom features |
| Migrate an existing v8 table | Migration audit |
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 19d ago Changed 9d106484fa18
- 23d ago Changed dc182082d361
- 27d ago Changed · -91 lines · -16 tokens per session 4419d840a43d
- 2mo ago First seen · 176 lines · 54 tokens per session scan A 14ed7647981b
core is a skill published in the GitHub repository TanStack/table (28,537 stars, last pushed 10d ago), licensed MIT. It adds 38 tokens to every session and 1,004 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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