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 Public Administration Review (PAR) manuscript so it survives expert, double-blind review and supports honest Evidence for…
Install Pubar Data Analysis
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill pubar-data-analysis -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills pubar-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/Public-Administration-Review-Skills/skills/pubar-data-analysis .claude/skills/pubar-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 "pubar-data-analysis" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Public-Administration-Review-Skills/skills/pubar-data-analysis into .claude/skills/pubar-data-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pubar-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/Public-Administration-Review-Skills/skills/pubar-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 pubar-data-analysis -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills pubar-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/Public-Administration-Review-Skills/skills/pubar-data-analysis .agents/skills/pubar-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 "pubar-data-analysis" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Public-Administration-Review-Skills/skills/pubar-data-analysis into .agents/skills/pubar-data-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pubar-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 pubar-data-analysis -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills pubar-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/Public-Administration-Review-Skills/skills/pubar-data-analysis .cursor/skills/pubar-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 "pubar-data-analysis" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Public-Administration-Review-Skills/skills/pubar-data-analysis into .cursor/skills/pubar-data-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pubar-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 Public-Administration-Review-Skills/skills/pubar-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 pubar-data-analysis -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills pubar-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/Public-Administration-Review-Skills/skills/pubar-data-analysis .gemini/skills/pubar-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 "pubar-data-analysis" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Public-Administration-Review-Skills/skills/pubar-data-analysis into .gemini/skills/pubar-data-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pubar-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 pubar-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 pubar-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/Public-Administration-Review-Skills/skills/pubar-data-analysis .github/skills/pubar-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 "pubar-data-analysis" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Public-Administration-Review-Skills/skills/pubar-data-analysis into .github/skills/pubar-data-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pubar-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 pubar-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 pubar-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/Public-Administration-Review-Skills/skills/pubar-data-analysis .opencode/skills/pubar-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 "pubar-data-analysis" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Public-Administration-Review-Skills/skills/pubar-data-analysis into .opencode/skills/pubar-data-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pubar-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
pubar-data-analysis- GitHub stars
- 1.2k
- Token cost
- ~1.8k tokens
- SKILL.md length
- 748 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 Public Administration Review (PAR) manuscript so it survives expert, double-blind review and supports honest Evidence for…
- Works in 6 steps: Report uncertainty honestly.… → Robustness that probes, not decorates.… → Heterogeneity with discipline.… → …
- Executing and reporting the analysis for a Public Administration Review (PAR) manuscript so it survives expert
- SKILL.md covers When to trigger, Analysis norms PAR expects, Mixed-methods integration and Reproducibility while you work…, plus 7 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Pubar Data Analysis is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when executing and reporting the analysis for a Public Administration Review (PAR) manuscript so it survives expert, double-blind review and supports honest Evidence for Practice — uncertainty, robustness, and triangulation appropriate to quantitative, experimental, or mixed work. Guides analysis norms; it does not fabricate results.
Its SKILL.md is about 1.8k 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 Public Administration Review (PAR) manuscript so it survives expert
- Double-blind review and supports honest Evidence for Practice — uncertainty
- Triangulation appropriate to quantitative
Example prompts
- “/pubar-data-analysis”
Workflow steps
6 steps, taken from the first numbered list in SKILL.md.
- Report uncertainty honestly. Confidence/credible intervals, not just stars; the **magnitude and
- Robustness that probes, not decorates. Show specifications that could break the result
- Heterogeneity with discipline. Pre-specify subgroups where possible (agency type, jurisdiction
- Right inference. Cluster at the assignment/sampling level (agency, district); wild-cluster
- Preregistration discipline. Clearly separate registered from exploratory analyses;
- Measurement. Validate constructs (red tape, PSM, performance); report reliability; show results
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
Pubar Data Analysis loads about 1.8k tokens when it runs. Until then it costs about 90 tokens; SKILL.md has 748 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). 748 words, ~1,817 tokens.
Download SKILL.mdSave it as
.claude/skills/pubar-data-analysis/SKILL.md (or your agent's skills folder).- name
- pubar-data-analysis
- description
- Use when executing and reporting the analysis for a Public Administration Review (PAR) manuscript so it survives expert, double-blind review and supports honest Evidence for Practice — uncertainty, robustness, and triangulation appropriate to quantitative, experimental, or mixed work. Guides analysis norms; it does not fabricate results.
Data Analysis (pubar-data-analysis)
PAR reviewers are methodologically capable public-management scholars, and the journal endorses the
TOP transparency guidelines — so analyses should be reproducible and documented (see
pubar-transparency-and-data). Because PAR articles carry Evidence for Practice, every estimate
that drives a managerial takeaway must be analyzed honestly enough to bear that weight. This skill
covers execution and reporting; design decisions live in pubar-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 deposit
Analysis norms PAR expects
- Report uncertainty honestly. Confidence/credible intervals, not just stars; the magnitude and substantive/managerial meaning of the estimate, not just its significance. A practitioner needs effect size, not a p-value.
- Robustness that probes, not decorates. Show specifications that could break the result (alternative measures, samples, estimators, fixed effects), and say what you learn.
- Heterogeneity with discipline. Pre-specify subgroups where possible (agency type, jurisdiction size, sector); correct for multiple comparisons; don't mine an interaction and theorize it post hoc.
- Right inference. Cluster at the assignment/sampling level (agency, district); wild-cluster bootstrap when clusters are few — a common public-management data situation.
- Preregistration discipline. Clearly separate registered from exploratory analyses; reconcile and justify deviations.
- Measurement. Validate constructs (red tape, PSM, performance); report reliability; show results are not an artifact of a coding/scaling choice — measurement debates are central in PA.
Mixed-methods integration
- State explicitly where the qualitative evidence corroborates, refines, or contradicts the quantitative estimate; do not present them in parallel silos with no integration.
Reproducibility while you work (not at the end)
- One master script regenerates every table and figure from the (raw or 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; document design/prep decisions in the supplementary document PAR recommends.
Execution bridge (StatsPAI / Stata MCP)
Run the battery, don't just enumerate it. Full map:
execution-with-mcp. PAR is public administration — survey/observational and some experimental work; identification + clustered/multilevel inference, magnitude for practice.
- 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.
Anti-patterns
- Stars-only tables with no effect sizes or intervals (a practitioner can't act on stars)
- "Robustness" that only reruns near-identical specs to manufacture stability
- p-hacking / fishing for a significant interaction; HARKing exploratory results into hypotheses
- Clustering at the wrong level or ignoring few-cluster problems
- An Evidence-for-Practice point that the analysis does not actually support
Show full SKILL.md (292 more words)Show less
Output format
【Main estimate】magnitude + interval + managerial meaning
【Identification check】(per research-design) result
【Robustness】specs that could break it → what held
【Heterogeneity】pre-specified? MHT-adjusted?
【Registered vs exploratory】clearly separated?
【Reproducible】master script + seeds + pinned versions? [Y/N]
【Next】pubar-tables-figuresWhat PAR reviewers probe, by analytic tradition
| Analytic tradition | The check a PAR referee runs first | The fix that earns the benefit of the doubt |
|---|---|---|
| Survey / managerial experiment | Is inference randomization-based and pre-registered? | Randomization inference, pre-registered estimand, MDE reported |
| Observational causal (reform) | Is the "causal" word (and the policy advice) doing more than the design licenses? | State estimand + assumption; sensitivity to an unobserved confounder |
| Performance / administrative data | Are measures validated, and is gaming/selection ruled out? | Construct validation, reliability, selection checks |
| Mixed methods | Do quant and qual estimates actually corroborate? | Show where they agree, and own where they diverge |
Worked micro-example (illustrative numbers)
A hypothetical PAR survey experiment tests whether a performance-feedback framing raises frontline managers' willingness to adopt a new reporting tool. The pre-registered ATE is +7.4 points (95% CI 3.0 to 11.8) on a 0–100 willingness scale, randomization-inference p = 0.006. An exploratory subgroup ("low-tenure managers") shows +13 points, but it was not pre-registered and after a Bonferroni adjustment across five exploratory subgroups its interval crosses zero. The disciplined write-up reports the +7.4 confirmatory effect with its interval and a managerial interpretation, flags the +13 figure as exploratory and not multiplicity-robust, and frames it as a hypothesis — so the Evidence-for-Practice point rests on the confirmatory estimate only. (All numbers illustrative.)
Calibration anchors (hedged)
- The bar is field-wide PA significance plus honest practice relevance; an effect only a specialist values, or a takeaway the data can't support, rarely clears PAR review.
- PAR practices methodological breadth — a rigorous mixed-methods or case analysis is not second-class to a regression. Match the inference standard to the design.
- TOP transparency expectations evolve; confirm the current data-policy wording on the journal's page (检索于 2026-06；以官网为准).
Supplementary resources
../../resources/external_tools.md— estimation, inference, and survey packages../../resources/official-source-map.md— TOP transparency 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 Public-Administration-Review-Skills/skills/pubar-data-analysis of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Pubar 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 |
|---|---|---|---|---|---|---|
| Pubar Data Analysis this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.8k | Automated safety check: Pass | MIT | |
| Exploratory Data Analysisspacering-net/codeg | 3.8k | 15 repos | ~3.6k | Automated safety check: Pass | MIT | |
| Excel and CSV Data Analysisbytedance/deer-flow | 83k | 4 repos | ~2.2k | Automated safety check: Pass | MIT | |
| Exploratory Data AnalysisOleafly/Oleafly | 206 | 3 repos | ~3.4k | Automated safety check: Notes | MIT | |
| Python Executorcortega26/chile-hub | 113 | 2 repos | ~1.5k | Automated safety check: Pass | MIT | |
| Agentic Kaggle WorkflowFrankS-IntelLab/agentic-kaggle-skill | 188 | — | ~4k | Automated safety check: Pass | MIT |
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Categories
Questions about Pubar Data Analysis
What does Pubar Data Analysis do?
A skill your agent uses when executing and reporting the analysis for a Public Administration Review (PAR) manuscript so it survives expert, double-blind review and supports honest Evidence for…. Pubar Data Analysis is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when executing and reporting the analysis for a Public Administration Review (PAR) manuscript so it survives expert, double-blind review and supports honest Evidence for Practice — uncertainty, robustness, and triangulation appropriate to quantitative, experimental, or mixed work.
When should I use Pubar Data Analysis?
Pubar Data Analysis fits situations like: executing and reporting the analysis for a Public Administration Review (PAR) manuscript so it survives expert; double-blind review and supports honest Evidence for Practice — uncertainty; triangulation appropriate to quantitative.
How do I install Pubar Data Analysis in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill pubar-data-analysis -a claude-code`. Or copy the skill folder (Public-Administration-Review-Skills/skills/pubar-data-analysis in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/pubar-data-analysis in your project. Claude Code loads it when a task matches its description.
How do I install Pubar Data Analysis in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill pubar-data-analysis -a codex`. Or copy the skill folder (Public-Administration-Review-Skills/skills/pubar-data-analysis in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/pubar-data-analysis in your project. Codex loads it when a task matches its description.
Can I use Pubar 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 pubar-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/pubar-data-analysis, .gemini/skills/pubar-data-analysis, .github/skills/pubar-data-analysis and .opencode/skills/pubar-data-analysis in your project.
What does Pubar Data Analysis need to run?
SKILL.md names no scripts, command-line tools or credentials: Pubar Data Analysis is instructions for the agent only.
Does Pubar 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 Pubar 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 Pubar Data Analysis use?
Pubar 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 Pubar Data Analysis use?
About 1.8k tokens (SKILL.md is roughly 7.3k 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 Pubar Data Analysis?
Skills that share tags, products or a category with Pubar Data Analysis: Exploratory Data Analysis (spacering-net/codeg, 3.8k stars), Excel and CSV Data Analysis (bytedance/deer-flow, 83k stars), Exploratory Data Analysis (Oleafly/Oleafly, 206 stars) and Python Executor (cortega26/chile-hub, 113 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Pubar Data Analysis?
brycewang-stanford (a GitHub user) maintains it in brycewang-stanford/Awesome-Journal-Skills, which has 1,219 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.