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-selectionnpx skills add TanStack/table --skill cell-selectiongit 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 | $0.00083 | $0.02188 |
| Opus 5 | $0.00042 | $0.01094 |
| Sonnet 5 | $0.00017 | $0.00438 |
| Haiku 4.5 | $0.00008 | $0.00219 |
Grade A, and why
cell-selection 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 — 201 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. cellSelection is an ordered operation log of rectangles, each stored as two corner cells identified by row and column id. It is not a per-cell map, and it is not positional. Table resolves the log into disjoint positive rectangles for membership and derived reads.
Setup
import { cellSelectionFeature, tableFeatures } from '@tanstack/table-core'
type Person = { id: string; name: string }
export const features = tableFeatures({ cellSelectionFeature })
export const options = {
getRowId: (row: Person) => row.id,
}
State shape:
type CellSelectionRange = {
anchorRowId: string
anchorColumnId: string
focusRowId: string
focusColumnId: string
operation?: 'include' | 'exclude'
}
type CellSelectionState = Array<CellSelectionRange>
The anchor corner stays put; the focus corner moves during a drag or Shift-extend. Two corners are what make Shift-extend possible, and they keep a drag across thousands of cells to a two-field write. Operations apply in array order. An omitted operation means include for backward compatibility; exclude subtracts from the selection produced by preceding entries.
Core Patterns
Bind both mouse handlers
const onMouseDown = cell.getSelectionStartHandler()
const onMouseEnter = cell.getSelectionExtendHandler()
The start handler attaches its own document-level mouseup listener and removes it when the drag ends, so a pointer released outside the table still finishes correctly. Pass a document explicitly (cell.getSelectionStartHandler(iframeDocument)) only when the table renders into another document.
With the default event predicates, Shift extends the active operation. Ctrl/Cmd starts an inclusion when the starting cell is unselected and an exclusion when it is selected. That choice remains fixed for the drag, so shrinking an exclusion restores cells that leave its rectangle. Set enableMultiCellRangeSelection: false to disable both modifier behaviors.
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 · 201 lines · 83 tokens per session scan A 68a4b51e7291
cell-selection is a skill published in the GitHub repository TanStack/table (28,393 stars, last pushed 2d ago), licensed MIT. It adds 83 tokens to every session and 2,188 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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