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/table-featuresnpx skills add TanStack/table --skill table-featuresgit 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 | $0.00053 | $0.00971 |
| Opus 5 | $0.00026 | $0.00485 |
| Sonnet 5 | $0.00011 | $0.00194 |
| Haiku 4.5 | $0.00005 | $0.00097 |
Grade A, and why
table-features 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 yesterday.
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 — 160 lines — stays where its author put it; the contents beside it link to each section on GitHub.
This skill builds on core. Read it first for the headless model and stable inputs.
Setup
import {
rowAggregationFeature,
aggregationFn_sum,
columnGroupingFeature,
createFilteredRowModel,
createSortedRowModel,
columnFilteringFeature,
filterFn_includesString,
rowSortingFeature,
sortFn_alphanumeric,
tableFeatures,
} from '@tanstack/table-core'
export const features = tableFeatures({
columnFilteringFeature,
filteredRowModel: createFilteredRowModel(),
filterFns: { includesString: filterFn_includesString },
rowAggregationFeature,
columnGroupingFeature,
aggregationFns: { sum: aggregationFn_sum },
rowSortingFeature,
sortedRowModel: createSortedRowModel(),
sortFns: { alphanumeric: sortFn_alphanumeric },
})
Core Patterns
Register feature before its dependent slot
const features = tableFeatures({
rowSortingFeature,
sortedRowModel: createSortedRowModel(),
})
tableFeatures checks slot prerequisites and its inferred type gates APIs throughout the table.
Register named function slots with their features
const features = tableFeatures({
columnFilteringFeature,
filterFns: { includesString: filterFn_includesString },
rowSortingFeature,
sortFns: { alphanumeric: sortFn_alphanumeric },
rowAggregationFeature,
columnGroupingFeature,
aggregationFns: { sum: aggregationFn_sum },
})
filterFns, sortFns, and aggregationFns are feature slots, not table
options. They respectively require columnFilteringFeature,
rowSortingFeature, and rowAggregationFeature. Import individual built-ins
(filterFn_*, sortFn_*, aggregationFn_*) and register them under their
conventional keys; the full registry objects (filterFns, sortFns,
aggregationFns exports) still work but bundle every built-in. A registered
key can be used as a typed string name, and 'auto' resolves only registered
functions; pass a function directly when no registry name is needed.
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.
- yesterday First seen · 160 lines · 53 tokens per session scan A da6b8cbbc5df
table-features is a skill published in the GitHub repository TanStack/table (28,393 stars, last pushed 2d ago), licensed MIT. It adds 53 tokens to every session and 971 once invoked, about $0.0003 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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