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 executing and reporting the analysis for a Journal of Public Administration Research and Theory (JPART) manuscript so it survives expert, double-blind review and the…
Install Jpart Data Analysis
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jpart-data-analysis -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills jpart-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-Public-Administration-Research-and-Theory-Skills/skills/jpart-data-analysis .claude/skills/jpart-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 "jpart-data-analysis" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Journal-of-Public-Administration-Research-and-Theory-Skills/skills/jpart-data-analysis into .claude/skills/jpart-data-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jpart-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-Public-Administration-Research-and-Theory-Skills/skills/jpart-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 jpart-data-analysis -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills jpart-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-Public-Administration-Research-and-Theory-Skills/skills/jpart-data-analysis .agents/skills/jpart-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 "jpart-data-analysis" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Journal-of-Public-Administration-Research-and-Theory-Skills/skills/jpart-data-analysis into .agents/skills/jpart-data-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jpart-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 jpart-data-analysis -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills jpart-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-Public-Administration-Research-and-Theory-Skills/skills/jpart-data-analysis .cursor/skills/jpart-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 "jpart-data-analysis" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Journal-of-Public-Administration-Research-and-Theory-Skills/skills/jpart-data-analysis into .cursor/skills/jpart-data-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jpart-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-Public-Administration-Research-and-Theory-Skills/skills/jpart-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 jpart-data-analysis -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills jpart-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-Public-Administration-Research-and-Theory-Skills/skills/jpart-data-analysis .gemini/skills/jpart-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 "jpart-data-analysis" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Journal-of-Public-Administration-Research-and-Theory-Skills/skills/jpart-data-analysis into .gemini/skills/jpart-data-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jpart-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 jpart-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 jpart-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-Public-Administration-Research-and-Theory-Skills/skills/jpart-data-analysis .github/skills/jpart-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 "jpart-data-analysis" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Journal-of-Public-Administration-Research-and-Theory-Skills/skills/jpart-data-analysis into .github/skills/jpart-data-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jpart-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 jpart-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 jpart-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-Public-Administration-Research-and-Theory-Skills/skills/jpart-data-analysis .opencode/skills/jpart-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 "jpart-data-analysis" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Journal-of-Public-Administration-Research-and-Theory-Skills/skills/jpart-data-analysis into .opencode/skills/jpart-data-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jpart-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
jpart-data-analysis- GitHub stars
- 1.2k
- Token cost
- ~1.9k tokens
- SKILL.md length
- 802 words
- Files
- 1
- Skills in repo
- 2,387
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when executing and reporting the analysis for a Journal of Public Administration Research and Theory (JPART) manuscript so it survives expert, double-blind review and the…
- Works in 6 steps: Report uncertainty and magnitude.… → Robustness that probes, not decorates.… → Confront the PA-specific threats.… → …
- Executing and reporting the analysis for a Journal of Public Administration Research and Theory (JPART) manuscript so it survives expert
- SKILL.md covers When to trigger, Analysis norms JPART expects, Measurement (a perennial JPART… and Reproducibility while you work…, plus 7 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Jpart Data Analysis is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when executing and reporting the analysis for a Journal of Public Administration Research and Theory (JPART) manuscript so it survives expert, double-blind review and the journal's mandatory data-and-code release. Covers honest uncertainty, robustness, and the PA-specific traps (common-method bias, selection). Guides analysis norms; it does not fabricate results.
Its SKILL.md is about 1.9k 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
- Executing and reporting the analysis for a Journal of Public Administration Research and Theory (JPART) manuscript so it survives expert
- Double-blind review and the journals mandatory data-and-code release
Example prompts
- “/jpart-data-analysis”
Requirements
- Python 3
Workflow steps
6 steps, taken from the first numbered list in SKILL.md.
- Report uncertainty and magnitude. Confidence/credible intervals and the substantive size of the
- Robustness that probes, not decorates. Show specifications that could break the result
- Confront the PA-specific threats. Common-method/common-source bias, social desirability, and
- Heterogeneity with discipline. Pre-specify subgroups where possible; correct for multiple
- Right inference. Cluster at the assignment/agency level; randomization inference for experiments;
- Preregistration discipline. Separate confirmatory from exploratory analyses; reconcile any
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.
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
Jpart Data Analysis loads about 1.9k tokens when it runs. Until then it costs about 97 tokens; SKILL.md has 802 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). 802 words, ~1,891 tokens.
Download SKILL.mdSave it as
.claude/skills/jpart-data-analysis/SKILL.md (or your agent's skills folder).- name
- jpart-data-analysis
- description
- Use when executing and reporting the analysis for a Journal of Public Administration Research and Theory (JPART) manuscript so it survives expert, double-blind review and the journal's mandatory data-and-code release. Covers honest uncertainty, robustness, and the PA-specific traps (common-method bias, selection). Guides analysis norms; it does not fabricate results.
Data Analysis (jpart-data-analysis)
JPART reviewers are methodologically sophisticated public-management scholars, and the journal requires
authors to release the data and software code underlying the paper as a condition of publication (see
jpart-transparency-and-data). Analyze as if a referee will re-run the code — because the materials are
public. This skill covers execution and reporting; design lives in jpart-research-design.
When to trigger
- Running main and supporting analyses; building the results section
- A reviewer asked for robustness, heterogeneity, or alternative specifications
- Reconciling preregistered vs. exploratory analyses
- Making the analysis reproducible before the mandatory data/code deposit
Analysis norms JPART expects
- Report uncertainty and magnitude. Confidence/credible intervals and the substantive size of the effect (e.g., a fraction of an SD of PSM), not stars alone.
- Robustness that probes, not decorates. Show specifications that could break the result (alternative measures of red tape/PSM, samples, estimators, fixed effects), and say what you learned.
- Confront the PA-specific threats. Common-method/common-source bias, social desirability, and self-selection into public service are the objections raised first — address them, don't ignore them.
- Heterogeneity with discipline. Pre-specify subgroups where possible; correct for multiple comparisons; do not mine for a significant interaction and theorize it post hoc.
- Right inference. Cluster at the assignment/agency level; randomization inference for experiments; small-cluster corrections (wild-cluster bootstrap) when agencies are few.
- Preregistration discipline. Separate confirmatory from exploratory analyses; reconcile any deviation from the plan and justify it.
Measurement (a perennial JPART referee focus)
- Validate constructs (PSM, red tape, goal ambiguity); report reliability; show the result is not an
artifact of a single scale or coding choice. Concept defined in
jpart-theory-buildingmust match the measure used here.
Reproducibility while you work (not at the end)
- One master script regenerates every table and figure from raw/constructed data.
- Set and report seeds for bootstrap, randomization inference, simulation, any stochastic step.
- Pin software/package versions (
renv.lock,requirements.txt, recordedssc/netinstalls). - Keep table/figure numbers matched to script outputs — the materials are public and will be checked.
What JPART reviewers probe, by design
| Design | The check a JPART referee runs first | The fix that earns benefit of the doubt |
|---|---|---|
| Survey of public employees | Are X and Y from the same self-report (common-method)? | separate sources / objective Y / marker variable + Harman caution |
| Survey/field experiment | Is it pre-registered, powered, on the right population? | preregistered estimand, MDE reported, public-employee sample |
| Observational causal | Is "effect" really selection into public service? | state estimand + assumption; sensitivity to an unobserved confounder |
| Multilevel | Is the agency-level nesting modeled? | random effects / clustered SEs, ICC reported |
| Mixed methods | Do quant and qual actually corroborate? | show agreement and own divergence |
Worked micro-example (illustrative numbers)
A hypothetical JPART field experiment tests whether a goal-clarity intervention raises frontline performance among real caseworkers. The pre-registered ITT is +0.18 SD (95% CI 0.06 to 0.30), randomization-inference p = 0.006. An exploratory split by tenure shows +0.41 SD for new hires, but it was not pre-registered and the interaction p = 0.03 before correction; after a Bonferroni adjustment across five exploratory subgroups it crosses 0.20. The disciplined write-up reports the confirmatory +0.18 SD effect with its interval and substantive meaning, flags the +0.41 figure as exploratory and not multiplicity-robust, and frames it as a hypothesis for future work. (All numbers illustrative.)
Show full SKILL.md (272 more words)Show less
Referee-pushback patterns and the JPART repair
- "This is common-method bias, not an effect." → Use a separate/objective outcome or a marker variable; report the sensitivity, don't wave it away with a single Harman test.
- "The robustness table only reruns near-identical specs." → Replace decorative checks with specs that could break the result (alternative PSM/red-tape measures, samples), and say what held.
- "This is selection into public service." → State the estimand and assumption; report how strong an unobserved confounder must be to overturn it.
- "I cannot tell confirmatory from exploratory." → Segregate them explicitly; the deposited code is public, so the split must survive a re-run.
Calibration anchors (hedged)
- The bar is a public-management theory payoff carried by credible numbers — an estimate with no mechanism rarely clears JPART review.
- JPART increasingly rewards experimental and causal designs, but a rigorous multilevel or mixed study is judged on its own terms.
- The data-and-code release is mandatory (where ethically possible) — write the analysis so the public package reproduces every printed number. Confirm exact wording on the live policy page.
Execution bridge (StatsPAI / Stata MCP)
Run the battery, don't just enumerate it. Full map:
execution-with-mcp. JPART is public management — observational and experimental designs on public organizations; identification + clustered/multilevel inference.
- Many outcomes / specifications:
romano_wolf(step-down FWER) orbenjamini_hochberg. - OVB sensitivity:
oster_delta/sensemakr. - Inference:
wild_cluster_bootstrap(few clusters),twoway_cluster/conley. - Re-fit off one handle:
audit_result(result_id)lists missing checks + the exactsuggest_functionfor each. - Exhibits:
etable/did_summary_to_latexfrom the handle — no retyped numbers.
Decisive checks in the body, exhaustive battery in the appendix. JF execution walkthrough.
Output format
【Main estimate】magnitude + interval + substantive meaning
【PA threat handled】common-method / selection — how?
【Robustness】specs that could break it → what held
【Heterogeneity】pre-specified? MHT-adjusted?
【Confirmatory vs exploratory】clearly separated?
【Reproducible】master script + seeds + pinned versions? [Y/N]
【Next】jpart-tables-figuresSupplementary resources
../../resources/code/— Stata + Python estimation/inference skeleton../../resources/external_tools.md— estimation, inference, and experiment packages../../resources/official-source-map.md— data-and-code release 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-Public-Administration-Research-and-Theory-Skills/skills/jpart-data-analysis of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Jpart 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 |
|---|---|---|---|---|---|---|
| Jpart Data Analysis this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.9k | 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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Questions about Jpart Data Analysis
What does Jpart Data Analysis do?
A skill your agent uses when executing and reporting the analysis for a Journal of Public Administration Research and Theory (JPART) manuscript so it survives expert, double-blind review and the…. Jpart Data Analysis is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when executing and reporting the analysis for a Journal of Public Administration Research and Theory (JPART) manuscript so it survives expert, double-blind review and the journal's mandatory data-and-code release.
When should I use Jpart Data Analysis?
Jpart Data Analysis fits situations like: executing and reporting the analysis for a Journal of Public Administration Research and Theory (JPART) manuscript so it survives expert; double-blind review and the journals mandatory data-and-code release.
How do I install Jpart Data Analysis in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jpart-data-analysis -a claude-code`. Or copy the skill folder (Journal-of-Public-Administration-Research-and-Theory-Skills/skills/jpart-data-analysis in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/jpart-data-analysis in your project. Claude Code loads it when a task matches its description.
How do I install Jpart Data Analysis in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jpart-data-analysis -a codex`. Or copy the skill folder (Journal-of-Public-Administration-Research-and-Theory-Skills/skills/jpart-data-analysis in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/jpart-data-analysis in your project. Codex loads it when a task matches its description.
Can I use Jpart 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 jpart-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/jpart-data-analysis, .gemini/skills/jpart-data-analysis, .github/skills/jpart-data-analysis and .opencode/skills/jpart-data-analysis in your project.
What does Jpart Data Analysis need to run?
SKILL.md names no scripts, command-line tools or credentials: Jpart Data Analysis is instructions for the agent only. Our summary lists: Python 3.
Does Jpart 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 Jpart 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 Jpart Data Analysis use?
Jpart 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 Jpart Data Analysis use?
About 1.9k tokens (SKILL.md is roughly 7.6k 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 Jpart Data Analysis?
Skills that share tags, products or a category with Jpart 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 Jpart 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.