Individual skill

jru-tables-figures

Use when exhibits for a Journal of Risk and Uncertainty (JRU) manuscript must make choice patterns and risk-parameter estimates legible. Improves tables and figures for a risk/uncertainty audience; it does not invent evidence or citations.

Journal-of-Risk-and-Uncertainty-Skills/skills/jru-tables-figures/SKILL.md

Read pinned source

Source commit 932eb23331721b16e967c59f093781be1928b498

What was checked

Matches the observed repository HEAD
Yes
Scanner rules are current
Yes
Repository scan complete
No
Skill scan complete
Yes
Observed behavior
Network or broad shell
Last checked
Nov 9, 2026

These are observations at the recorded commit, not a guarantee of safety. Review the source before running it.

Install this skill

Choose an agent in your Stack or copy a command below. Commands fetch the pinned commit and copy only this skill directory. Existing destinations are refused. Copying a command does not install anything.

An install command is unavailable: current rules, complete repository and skill scans, an eligible behavior level and a permitted license are required.

Repository context

Repository stars (not skill ratings)
1,397
Repository last updated
Sep 27, 2026
License
MIT

Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.

Files and excerpt

File metadata and permitted, redacted excerpts come from the recorded scan. Source links are pinned to that commit.

Browse files
Read excerpt

# Tables and Figures (jru-tables-figures)

## When to trigger

- Choice data are buried in a wall of cells when a figure could show the *pattern* (the fourfold pattern, an Allais reversal, an Ellsberg gap)
- A structural table lists parameters with no sense of which moment or task identifies them
- A probability-weighting function or value function is described in prose instead of plotted
- Standard errors, confidence intervals, or the estimation method behind each parameter are missing or inconsistent

## What JRU exhibits must do

JRU audiences read for **decision-theoretic content**: they want to see the shape of the weighting function, the location of the reference point, the size of the ambiguity premium, and how precisely each parameter is pinned down. The exhibit's job is to make the *behavioral pattern* and the *parameter estimate* immediately legible, with honest uncertainty.

### Figures — the workhorses of this field
Back to top