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 agentmods add skills/tanstack/table/cell-spanningnpx skills add TanStack/table --skill cell-spanninggit clone --depth 1 https://github.com/TanStack/tableWhat 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 | $0.00081 | $0.01418 |
| Opus 5 | $0.00041 | $0.00709 |
| Sonnet 5 | $0.00016 | $0.00284 |
| Haiku 4.5 | $0.00008 | $0.00142 |
Grade A, and why
cell-spanning 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 2d 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 — 159 lines — stays where its author put it; the contents beside it link to each section on GitHub.
This skill builds on core and table-features. Cell spanning is stateless and fully derived: a memoized table-level span index is rebuilt from the rows that are actually rendered, and per-cell reads are O(1) lookups against it. Sorting, filtering, pagination, and row pinning only change adjacency; spans follow automatically and never persist.
Setup
import { cellSpanningFeature, tableFeatures } from '@tanstack/table-core'
export const features = tableFeatures({ cellSpanningFeature })
Core Patterns
Opt columns into value-based row spanning
const columns = [
{ accessorKey: 'region', spanRows: true },
{
accessorKey: 'createdAt',
spanRows: ({
anchorValue,
value,
}: {
anchorValue: unknown
value: unknown
}) => sameMonth(anchorValue as Date, value as Date),
},
]
spanRows: true merges adjacent rendered rows whose values match under Object.is. Nullish values never merge under the default comparison; a predicate can opt in. Predicates are anchored: every candidate row is tested against the run's first row (anchorRow/anchorValue), which keeps runs transitive.
Declare column spans per row
const columns = [
{
accessorKey: 'label',
spanColumns: ({ row }: { row: { original: { isSummary?: boolean } } }) =>
row.original.isSummary ? Infinity : 1,
},
]
spanColumns counts visible columns in render order and clamps to the end of the cell's pinned region, so Infinity means "the rest of my region" and a span never crosses a start/center/end pinning boundary. Cells only join a vertical run when their column spans match, so a full-width summary row never merges into the data run above it.
Render with the skip-covered-cells loop
{
row.getVisibleCells().map((cell) => {
const rowSpan = cell.getRowSpan()
const colSpan = cell.getColSpan()
if (rowSpan === 0 || colSpan === 0) return null
return (
<td key={cell.id} rowSpan={rowSpan} colSpan={colSpan}>
{flexRender(cell.column.columnDef.cell, cell.getContext())}
</td>
)
})
}
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.
- 2d ago First seen · 159 lines · 81 tokens per session scan A 89c4f3d8f704
cell-spanning is a skill published in the GitHub repository TanStack/table (28,393 stars, last pushed 2d ago), licensed MIT. It adds 81 tokens to every session and 1,418 once invoked, about $0.0004 per session on Opus 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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