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 fuzzy-rankinggit 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/fuzzy-ranking)<a href="https://agentmods.dev/skills/tanstack/table/fuzzy-ranking"><img src="https://agentmods.dev/badge/skills/tanstack/table/fuzzy-ranking/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/fuzzy-ranking"><img src="https://agentmods.dev/badge/skills/tanstack/table/fuzzy-ranking.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 | $0.00062 | $0.00833 |
| Opus 5.5 | $0.00025 | $0.00333 |
| Sonnet 5.5 | $0.00012 | $0.00167 |
| Haiku 4.5 | $0.00006 | $0.00083 |
Grade A, and why
fuzzy-ranking 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 11d 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 — 96 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Setup
import { compareItems, rankItem, rankings } from '@tanstack/match-sorter-utils'
const query = 'tan'
const values = ['table', 'tanner', 'router']
export const ranked = values
.map((value) => ({
value,
info: rankItem(value, query, { threshold: rankings.MATCHES }),
}))
.filter((entry) => entry.info.passed)
.sort((a, b) => compareItems(a.info, b.info))
Core Patterns
Separate ranking, filtering, and sorting
Call rankItem once, filter on info.passed, retain the RankingInfo, then order matching results with compareItems.
Rank object fields through accessors
type Person = { name: string; email: string }
const person: Person = { name: 'Ada Lovelace', email: '[email protected]' }
const info = rankItem(person, 'lov', {
accessors: [(item) => item.name, (item) => item.email],
})
Accessor options can set a per-accessor threshold plus minRanking/maxRanking bounds.
Store ranking as Table filter metadata
In a Table filterFn, call addMeta?.({ itemRank }). Register the corresponding
meta shape with filterMeta: metaHelper<{ itemRank: RankingInfo }>(). A related
sortFn reads row.columnFiltersMeta[columnId]?.itemRank and uses compareItems,
falling back to an ordinary comparator for ties or absent metadata. For the
primary Table composition, load @tanstack/table-core#table-features and read
its global-filtering reference. Register the fuzzy filter under filterFns
for globalFilterFn: 'fuzzy'.
Common Mistakes
HIGH Numeric rank used as pass flag
Wrong: if (rankItem(value, query).rank) include(value).
Correct: test rankItem(value, query).passed.
Ranks below the configured threshold can still be nonzero; passed records the threshold decision.
Source: TanStack/table:packages/match-sorter-utils/src/index.ts
HIGH Ranking recomputed during sorting
Wrong: call rankItem for both rows on every comparator invocation.
Correct: retain RankingInfo during filtering and pass the stored values to compareItems.
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.
- 11d ago Changed · +1 lines 4933cb8507b5
- 1mo ago First seen · 95 lines · 62 tokens per session scan A 1fd9ab0346ba
fuzzy-ranking is a skill published in the GitHub repository TanStack/table (28,491 stars, last pushed 4d ago), licensed MIT. It adds 62 tokens to every session and 833 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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