Agent 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.

brycew6m4,252★ · +31/wk · 3 repos on radarProfile →
claude-codeMIT
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jru-tables-figures --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 1
SKILL.md size: 7 KB
Bundled scripts: none
Path: Journal-of-Risk-and-Uncertainty-Skills/skills/jru-tables-figures/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 984 · +31 this week
Language: Stata
Read our review of the source →

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# 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 - **Plot the estimated functions, not just their parameters.** A Prelec or Tversky–Kahneman w(p) and a reference-dependent value function are far more informative drawn than tabulated; overlay the EU benchmark (the 45° line for w(p)) so the deviation is visible.

What's inside
Steps it walks through
  1. When to trigger
  2. What JRU exhibits must do
  3. Figures — the workhorses of this field
  4. Tables — discipline
  5. Execution bridge (StatsPAI / Stata MCP)
  6. Checklist
  7. Reading the exhibit as a referee would
  8. Anti-patterns
  9. The exhibit each archetype needs
  10. Caption and note discipline
  11. Worked vignette (illustrative)
  12. Output format
More from Awesome-Journal-Skills
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About this skill
What does the jru-tables-figures skill do?

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.

How do I install it?

Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jru-tables-figures --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From brycewang-stanford/Awesome-Journal-Skills, a repository with 984 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

Keep going