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 Uxcel-Lab/product-skills --skill tablesgit clone --depth 1 https://github.com/Uxcel-Lab/product-skillsWrote 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/uxcel-lab/product-skills/tables)<a href="https://agentmods.dev/skills/uxcel-lab/product-skills/tables"><img src="https://agentmods.dev/badge/skills/uxcel-lab/product-skills/tables/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/uxcel-lab/product-skills/tables"><img src="https://agentmods.dev/badge/skills/uxcel-lab/product-skills/tables.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00081 | $0.02276 |
| Opus 5 | $0.00041 | $0.01138 |
| Sonnet 5 | $0.00016 | $0.00455 |
| Haiku 4.5 | $0.00008 | $0.00228 |
Grade A, and why
ux-tables 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 10d 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 — 125 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Tables & Lists Skill
How this skill behaves (read first)
This is a generative skill. The default failure modes: decoration over data (heavy borders, zebra everything, centered text, mixed formats), missing manipulation (no sorting, no customization — fatal at real data volumes), and density extremes (cramped 32px rows or wasteful spacing). A table's only job is letting users scan, compare, and act on data — every visual choice either serves that or fights it. This skill gates:
- Establish the data shape and tasks — 2-D comparison (table) vs. 1-D scanning (list), volume, and what users do with rows.
- Apply the always-true core — alignment rules, hierarchy, formats, interaction states.
- Surface the context-dependent decisions (density, pagination type, customization, list variants) with trade-offs.
Then it hands off to ux-heuristics-audit, ux-accessibility-audit, ux-aesthetics-audit, and ux-microcopy-audit for validation.
Step 0 — Establish context before designing
Ask if not known; state the assumption if proceeding without an answer:
- Table or list? Multi-attribute comparison across columns → table. One item per row scanned linearly (emails, results, settings) → list. Simple-table content often reads better as a list — and lists are easier for screen-reader users.
- Data volume and growth — tens of rows need none of the machinery; thousands need sorting, filtering, pagination, items-per-page, search.
- What do users do with rows? Read-only, single-row actions (inline ⋮ menu), bulk operations (multi-select checkboxes), or editing — each adds specific UI.
- Density tolerance — data-dense ops tools (condensed 40px rows) vs. consumer comfort (regular 48px / relaxed 56px).
The always-apply core (correct for almost every case)
Alignment and formatting (the silent rules everyone breaks)
- Text left-aligned (LTR), matching the reading direction.
- Numbers right-aligned, in tabular figures (equal-width digits) — that's what makes columns of numbers comparable at a glance.
- One consistent format per column — never "22.04.2018" next to "4.6.2018"; one date, currency, and unit format throughout.
- Legible sans-serif typeface; column margins wide enough to scan, compare, and edit comfortably.
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.
- 10d ago First seen · 125 lines · 81 tokens per session scan A b881ed2b4845
ux-tables is a skill published in the GitHub repository Uxcel-Lab/product-skills (10 stars, last pushed 2mo ago), licensed MIT. It adds 81 tokens to every session and 2,276 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 2026-08-31.
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