Exploratory Data Analysis
spacering-net/codeg
Perform comprehensive exploratory data analysis on scientific data files across 200+ file formats.
A skill your agent uses when handling numerical, computational, or empirical content in a Journal of Economic Theory (JET) paper — JET is theory-first, so examples, simulations, and computed…
Install Jet Data Analysis
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jet-data-analysis -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills jet-data-analysis --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/Journal-of-Economic-Theory-Skills/skills/jet-data-analysis .claude/skills/jet-data-analysis && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "jet-data-analysis" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Journal-of-Economic-Theory-Skills/skills/jet-data-analysis into .claude/skills/jet-data-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jet-data-analysis", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Journal-of-Economic-Theory-Skills/skills/jet-data-analysisType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jet-data-analysis -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills jet-data-analysis --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/Journal-of-Economic-Theory-Skills/skills/jet-data-analysis .agents/skills/jet-data-analysis && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "jet-data-analysis" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Journal-of-Economic-Theory-Skills/skills/jet-data-analysis into .agents/skills/jet-data-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jet-data-analysis", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jet-data-analysis -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills jet-data-analysis --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/Journal-of-Economic-Theory-Skills/skills/jet-data-analysis .cursor/skills/jet-data-analysis && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "jet-data-analysis" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Journal-of-Economic-Theory-Skills/skills/jet-data-analysis into .cursor/skills/jet-data-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jet-data-analysis", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/brycewang-stanford/Awesome-Journal-Skills.git --path Journal-of-Economic-Theory-Skills/skills/jet-data-analysis--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jet-data-analysis -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills jet-data-analysis --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/Journal-of-Economic-Theory-Skills/skills/jet-data-analysis .gemini/skills/jet-data-analysis && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "jet-data-analysis" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Journal-of-Economic-Theory-Skills/skills/jet-data-analysis into .gemini/skills/jet-data-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jet-data-analysis", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills jet-data-analysisInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jet-data-analysis -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/Journal-of-Economic-Theory-Skills/skills/jet-data-analysis .github/skills/jet-data-analysis && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "jet-data-analysis" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Journal-of-Economic-Theory-Skills/skills/jet-data-analysis into .github/skills/jet-data-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jet-data-analysis", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jet-data-analysis -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills jet-data-analysis --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/Journal-of-Economic-Theory-Skills/skills/jet-data-analysis .opencode/skills/jet-data-analysis && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "jet-data-analysis" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Journal-of-Economic-Theory-Skills/skills/jet-data-analysis into .opencode/skills/jet-data-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jet-data-analysis", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
jet-data-analysis- GitHub stars
- 1.2k
- Token cost
- ~1.3k tokens
- SKILL.md length
- 558 words
- Files
- 1
- Skills in repo
- 2,387
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when handling numerical, computational, or empirical content in a Journal of Economic Theory (JET) paper — JET is theory-first, so examples, simulations, and computed…
- Handling numerical
- SKILL.md covers When to trigger, The JET rule: theory-first,…, How to present numerical content and Picking the smallest…, plus 4 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Empirical content in a Journal of Economic Theory (JET) paper — JET is theory-first
What it does
Jet Data Analysis is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when handling numerical, computational, or empirical content in a Journal of Economic Theory (JET) paper — JET is theory-first, so examples, simulations, and computed equilibria must stay subordinate to the theorem and reproducible.
Its SKILL.md is about 1.3k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Data & Analytics, covering Data analysis. The repository describes itself as: Journal-specific Claude Code/Codex skill packs covering mainstream journals — AER, QJE, Nature, Cell, 管理世界, 经济研究 & 200+ more — your fast track to getting published. ｜ 覆盖主流期刊的… The licence is MIT.
When your agent uses it
- Handling numerical
- Empirical content in a Journal of Economic Theory (JET) paper — JET is theory-first
- Computed equilibria must stay subordinate to the theorem and reproducible
Example prompts
- “/jet-data-analysis”
Requirements
- Python 3
What it can do on your machine
Read from SKILL.md and the folder at commit 932eb23. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md (its code samples are python).
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Jet Data Analysis loads about 1.3k tokens when it runs. Until then it costs about 64 tokens; SKILL.md has 558 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from brycewang-stanford/Awesome-Journal-Skills at commit 932eb23, republished under its MIT licence (© brycewang-stanford). 558 words, ~1,332 tokens.
Download SKILL.mdSave it as
.claude/skills/jet-data-analysis/SKILL.md (or your agent's skills folder).- name
- jet-data-analysis
- description
- Use when handling numerical, computational, or empirical content in a Journal of Economic Theory (JET) paper — JET is theory-first, so examples, simulations, and computed equilibria must stay subordinate to the theorem and reproducible.
Numerical & Computational Content (jet-data-analysis)
When to trigger
- Your theory paper includes a worked numerical example, a simulation, a computed equilibrium, or (rarely) empirical/experimental evidence
- You want to know how much computational content JET will accept and how to present it
- You need to keep a computation from overshadowing the theorem
The JET rule: theory-first, computation subordinate
JET publishes rigorous, original theoretical results. Empirical, experimental, quantitative, and computational work is welcome only when firmly grounded in theory — i.e., as the illustration or test of a theoretical contribution that is itself the paper's point, never as a stand-alone empirical or computational paper. This skill is deliberately light: most JET papers are pure theory, so the default is minimal numerical content.
How to present numerical content
- Make it serve the theorem. A numerical example should make an assumption bite, exhibit the characterized object, or show tightness of a bound — not stand alone as a finding.
- Keep examples small and transparent. A 2x2 game, a two-type screening problem, or a three-agent matching market usually communicates more than a large simulation.
- Use computation to probe necessity. A computed counterexample is the cleanest way to show an assumption cannot be dropped (feeds jet-identification-strategy and jet-rebuttal).
- Reproducibility. Provide a small self-contained script (SymPy/
numpy/scipy, Julia, MATLAB/Octave) that regenerates every reported number and figure; pin versions and set/report seeds for anything stochastic. If the paper uses research data, Elsevier Option C requires a repository citation/link or a cannot-share explanation; if it only has computation, share enough code for the referee to reproduce the numerical claim (see jet-replication-and-data-policy). - If genuinely empirical/experimental: state the theoretical prediction first, then test it; the prediction is the contribution.
Show full SKILL.md (284 more words)Show less
Picking the smallest environment that makes the point
| Theoretical claim | Smallest honest illustration | Why it convinces a JET referee |
|---|---|---|
| An assumption cannot be dropped | a 2x2 game or two-type screening problem violating only that assumption | the failure is checkable by hand in minutes |
| A bound is tight | an environment attaining the bound exactly | tightness becomes a verifiable statement, not a plot |
| A characterized mechanism is implementable | computed transfers/allocations for two or three types | the numbers confirm the closed form line by line |
| The equilibrium set has the claimed shape | a three-agent matching market or a two-state ambiguity example | the entire set can be enumerated and inspected |
| A dynamic characterization is operational | one computed path of the recursive contract | the recursion is seen to close |
If the smallest environment that exhibits the phenomenon needs more than a page to describe, reconsider whether the example belongs in the body or in an appendix.
Minimal verification script (template)
# verify_example_1.py — regenerates every number in Example 1
# (tightness of the bound in Theorem 2 for the two-type screening problem)
import sympy as sp
v_H, v_L, p = sp.symbols("v_H v_L p", positive=True)
rent = (v_H - v_L) * p # information rent at the optimum, matches eq. (7)
bound = sp.Rational(1, 2) * (v_H - v_L) # the Theorem 2 bound
print(sp.simplify(rent.subs(p, sp.Rational(1, 2)) - bound)) # 0 → bound attained at p = 1/2
# Nothing here is stochastic; if an example is FOUND by random search,
# fix the seed, report it, and ship the search script too.One short script per numbered Example, named after the theorem it serves, beats one monolithic notebook — referees check examples against statements, not pipelines.
Where computation sits in an accepted JET paper
- As a numbered Example placed immediately after the theorem it illustrates, or as a short "Numerical illustration" subsection — almost never as a stand-alone section competing with the results. Conventions drift across subfields; check recent JET papers in yours.
- Figures generated from computation follow jet-tables-figures: vector output, notation identical to the body, the generating script named in the note.
Anti-patterns
- A large simulation presented as the result, with theory as decoration (off-fit for JET)
- A numerical figure whose underlying values cannot be reproduced
- Calibration/estimation with no theorem behind it (send elsewhere)
- Stochastic illustration with no seed reported
Output format
【Content type】worked example | simulation | computed equilibrium | empirical test | none
【Role】illustrates / tests / counterexample to <theorem/assumption>
【Subordinate to theory?】[Y/N] ← must be Y for JET
【Reproducible】script + pinned env + seed? [Y/N]
【Next】jet-tables-figures / jet-replication-and-data-policy© brycewang-stanford, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in Journal-of-Economic-Theory-Skills/skills/jet-data-analysis of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Jet Data Analysis next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Jet Data Analysis this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.3k | Automated safety check: Pass | MIT | |
| Exploratory Data Analysisspacering-net/codeg | 3.9k | 14 repos | ~3.6k | Automated safety check: Pass | MIT | |
| Excel and CSV Data Analysisbytedance/deer-flow | 84k | 4 repos | ~2.2k | Automated safety check: Pass | MIT | |
| Exploratory Data AnalysisOleafly/Oleafly | 209 | 2 repos | ~3.4k | Automated safety check: Notes | MIT | |
| Pandas ProJeffallan/claude-skills | 12k | 1 repos | ~1.5k | Automated safety check: Pass | MIT | |
| Python Executorcortega26/chile-hub | 113 | 2 repos | ~1.5k | Automated safety check: Pass | MIT |
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Categories
Questions about Jet Data Analysis
What does Jet Data Analysis do?
A skill your agent uses when handling numerical, computational, or empirical content in a Journal of Economic Theory (JET) paper — JET is theory-first, so examples, simulations, and computed…. Jet Data Analysis is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when handling numerical, computational, or empirical content in a Journal of Economic Theory (JET) paper — JET is theory-first, so examples, simulations, and computed equilibria must stay subordinate to the theorem and reproducible.
When should I use Jet Data Analysis?
Jet Data Analysis fits situations like: handling numerical; empirical content in a Journal of Economic Theory (JET) paper — JET is theory-first; computed equilibria must stay subordinate to the theorem and reproducible.
How do I install Jet Data Analysis in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jet-data-analysis -a claude-code`. Or copy the skill folder (Journal-of-Economic-Theory-Skills/skills/jet-data-analysis in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/jet-data-analysis in your project. Claude Code loads it when a task matches its description.
How do I install Jet Data Analysis in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jet-data-analysis -a codex`. Or copy the skill folder (Journal-of-Economic-Theory-Skills/skills/jet-data-analysis in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/jet-data-analysis in your project. Codex loads it when a task matches its description.
Can I use Jet Data Analysis in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jet-data-analysis -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/jet-data-analysis, .gemini/skills/jet-data-analysis, .github/skills/jet-data-analysis and .opencode/skills/jet-data-analysis in your project.
What does Jet Data Analysis need to run?
SKILL.md names no scripts, command-line tools or credentials: Jet Data Analysis is instructions for the agent only. Our summary lists: Python 3.
Does Jet Data Analysis access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Jet Data Analysis safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Jet Data Analysis use?
Jet Data Analysis is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Jet Data Analysis use?
About 1.3k tokens (SKILL.md is roughly 5.3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Jet Data Analysis?
Skills that share tags, products or a category with Jet Data Analysis: Exploratory Data Analysis (spacering-net/codeg, 3.9k stars), Excel and CSV Data Analysis (bytedance/deer-flow, 84k stars), Exploratory Data Analysis (Oleafly/Oleafly, 209 stars) and Pandas Pro (Jeffallan/claude-skills, 12k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Jet Data Analysis?
brycewang-stanford (a GitHub user) maintains it in brycewang-stanford/Awesome-Journal-Skills, which has 1,228 GitHub stars. The repository holds 2,387 skills in this directory. The repository was last updated on September 27, 2026.
Source: brycewang-stanford/Awesome-Journal-Skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.