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Grok Bot+18 agentsTable Design: Publication-Ready Results Tables
This skill turns your agent into a careful table maker.
$5
Table Design: Publication-Ready Results Tables
Example session with this skill installed
Make Table 2 from results.csv for the paper (LaTeX booktabs) and a Markdown version for the README.
- Read your context and instructions
- Compiled the table design
- Generated the data export
I've written Table 2 for the paper (table2.tex) and a Markdown version for the README (table2.md). I rendered the LaTeX version to previews/table2.png and checked it: the columns line up, the missing cell shows a dash, and nothing is cut off.
One thing to check before you use it: ProtoMix is not the best method on every dataset. On Seeds, ViT-S has the higher mean (95.4 ± 1.6 against 95.2 ± 0.7). That gap is well within one SD, so the caption says ProtoMix "is the most accurate method on three of the four datasets; on Seeds it is within one SD of ViT-S."
- Layout: methods are rows and datasets are columns. Each cell is test accuracy as mean ± SD over 5 seeds.
Rounding: percentages with one decimal. Each SD is rounded to one significant digit, or two when it starts with 1, and the mean to the same decimal place.
- Bold: the highest mean in each dataset column, not a significant difference. A note under the table says this.
- Missing cell: ViT-S on Plankton shows "–". The CSV marks that run as out of memory, and a note says so.
Fewer seeds: ResNet-18 on Pollen was run with 3 seeds, not 5. A note says so, but it's worth checking that this was intended.
The LaTeX file needs \usepackage{booktabs} in your preamble.
table2.png
CSV · data export
Example file from a real run - the skill writes it into your workspace.
Connects securely to your tools. The creator never sees your data.
About this skill
What it does
A results table is where a reviewer checks your claims, and six-decimal script output pasted into LaTeX makes them work for it. This skill turns your agent into a careful table maker. Give it a CSV or JSON of results and it:
- Decides the form first: a sentence, a figure or a table, and whether the table is for looking values up (supplementary) or for showing one comparison (main text).
- Pivots long results (one row per method × dataset) into methods as rows and datasets as columns, and orders the rows by a rule it states.
- Rounds for the reader: two to three significant digits by default; for mean ± SD, the SD keeps one significant digit (two when it starts with 1) and the mean is rounded to match. Large and tiny values are scaled, with the factor in the header.
- Marks what matters, and only that: the best value per column in bold, or one highlighted row; units in headers; digits aligned; booktabs rules and no vertical lines.
- Never hides a gap: missing runs print as an en dash with a note that says what the data say; cells with a different n are flagged; when the best value is within one SD of the runner-up, it says so instead of implying a win.
- Writes a caption that leads with the takeaway, then n and what ± means.
- Renders the table and looks at it before handing over: LaTeX compiled to PNG, HTML screenshotted.
Also writes Markdown for a README and HTML for reports or web pages, and handles Spanish number formats (decimal comma, narrow-space or point thousands separator), including reading CSVs written that way.
What's included
SKILL.md: the workflow (form, purpose, shape, rounding, caption and notes, render and check) and the formatting rules, plus guidance for tables on slides and in spreadsheetsscripts/table_fmt.py: CSV or JSON to Markdown, booktabs LaTeX or HTML: pivot, mean ± SD, significant-digit rounding, scaling, scientific notation, best-value bold, row highlight, notes, totals, share of total, inline bars, decimal comma. Standard library only.scripts/preview.py: renders a .tex table (any LaTeX engine) or .html table (Chrome, Chromium, Edge or Brave, found on macOS, Linux and Windows) to PNGexamples/: a synthetic benchmark CSV and the LaTeX, Markdown, HTML and Spanish tables made from it
Part of the Aurora Lab research pipeline
Works entirely on its own. With a research/ folder it reads results from research/results/ and writes tables to research/tables/. Pairs well with Aurora Lab's figure skills (Paper Figures, Figure Audit), but needs none of them.
How to use
Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.
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Requires
Python
macOS