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/global-filteringnpx skills add TanStack/table --skill global-filteringgit 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 | $0.00047 | $0.00621 |
| Opus 5 | $0.00023 | $0.00311 |
| Sonnet 5 | $0.00009 | $0.00124 |
| Haiku 4.5 | $0.00005 | $0.00062 |
Grade A, and why
global-filtering 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 — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
This skill builds on core, table-features, client-vs-server, and column-filtering. Global filtering reuses the column-filtering pipeline.
Setup
import {
columnFilteringFeature,
createFilteredRowModel,
filterFn_includesString,
globalFilteringFeature,
tableFeatures,
} from '@tanstack/table-core'
export const features = tableFeatures({
columnFilteringFeature,
globalFilteringFeature,
filteredRowModel: createFilteredRowModel(),
filterFns: { includesString: filterFn_includesString },
})
Core Patterns
const options = {
globalFilterFn: 'includesString' as const,
getColumnCanGlobalFilter: (column) => column.id !== 'actions',
}
Declare eligibility when product rules differ from default primitive-value detection.
Common Mistakes
[CRITICAL] Omitting column-filter prerequisite
Wrong: tableFeatures({ globalFilteringFeature })
Correct: tableFeatures({ columnFilteringFeature, globalFilteringFeature, filteredRowModel: createFilteredRowModel() })
Global filtering depends on column filtering and needs the filtered model for client processing.
Source: packages/table-core/src/types/TableFeatures.ts#FeatureSlotPrereqs
[HIGH] Assuming every column participates
Wrong: const options = { globalFilterFn: 'includesString' }
Correct: const options = { getColumnCanGlobalFilter: column => column.id !== 'actions' }
Default eligibility uses the first core row and accepts string or number values.
Source: packages/table-core/src/features/global-filtering/globalFilteringFeature.ts
[HIGH] Keeping manual filter local only
Wrong: table.setGlobalFilter(search); const options = { manualFiltering: true }
Correct: const page = await fetchRows({ search }); const options = { data: page.rows, manualFiltering: true }
Manual filtering bypasses the client model, so the value must drive the server request.
Source: docs/framework/react/guide/global-filtering.md#manual-server-side-global-filtering
API Discovery
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 · 81 lines · 47 tokens per session scan A 4cc40c79a4d0
global-filtering is a skill published in the GitHub repository TanStack/table (28,393 stars, last pushed 2d ago), licensed MIT. It adds 47 tokens to every session and 621 once invoked, about $0.0002 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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