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 running and documenting the empirical analysis for a Journal of Financial and Quantitative Analysis (JFQA) paper — finance data construction (CRSP/Compustat/TAQ/IBES)…
Install Jfqa Data Analysis
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jfqa-data-analysis -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills jfqa-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-Financial-and-Quantitative-Analysis-Skills/skills/jfqa-data-analysis .claude/skills/jfqa-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 "jfqa-data-analysis" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Journal-of-Financial-and-Quantitative-Analysis-Skills/skills/jfqa-data-analysis into .claude/skills/jfqa-data-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jfqa-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-Financial-and-Quantitative-Analysis-Skills/skills/jfqa-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 jfqa-data-analysis -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills jfqa-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-Financial-and-Quantitative-Analysis-Skills/skills/jfqa-data-analysis .agents/skills/jfqa-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 "jfqa-data-analysis" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Journal-of-Financial-and-Quantitative-Analysis-Skills/skills/jfqa-data-analysis into .agents/skills/jfqa-data-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jfqa-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 jfqa-data-analysis -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills jfqa-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-Financial-and-Quantitative-Analysis-Skills/skills/jfqa-data-analysis .cursor/skills/jfqa-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 "jfqa-data-analysis" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Journal-of-Financial-and-Quantitative-Analysis-Skills/skills/jfqa-data-analysis into .cursor/skills/jfqa-data-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jfqa-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-Financial-and-Quantitative-Analysis-Skills/skills/jfqa-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 jfqa-data-analysis -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills jfqa-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-Financial-and-Quantitative-Analysis-Skills/skills/jfqa-data-analysis .gemini/skills/jfqa-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 "jfqa-data-analysis" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Journal-of-Financial-and-Quantitative-Analysis-Skills/skills/jfqa-data-analysis into .gemini/skills/jfqa-data-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jfqa-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 jfqa-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 jfqa-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-Financial-and-Quantitative-Analysis-Skills/skills/jfqa-data-analysis .github/skills/jfqa-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 "jfqa-data-analysis" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Journal-of-Financial-and-Quantitative-Analysis-Skills/skills/jfqa-data-analysis into .github/skills/jfqa-data-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jfqa-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 jfqa-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 jfqa-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-Financial-and-Quantitative-Analysis-Skills/skills/jfqa-data-analysis .opencode/skills/jfqa-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 "jfqa-data-analysis" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Journal-of-Financial-and-Quantitative-Analysis-Skills/skills/jfqa-data-analysis into .opencode/skills/jfqa-data-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jfqa-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
jfqa-data-analysis- GitHub stars
- 1.2k
- Token cost
- ~1.5k tokens
- SKILL.md length
- 683 words
- Files
- 1
- Skills in repo
- 2,387
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when running and documenting the empirical analysis for a Journal of Financial and Quantitative Analysis (JFQA) paper — finance data construction (CRSP/Compustat/TAQ/IBES)…
- Clustered and Newey-West standard errors
- SKILL.md covers Data construction…, Estimation & inference, Robustness & heterogeneity and Reproducibility discipline, plus 6 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Heterogeneity — so results survive double-anonymous JFQA review and reproduce from the archived code
What it does
Jfqa Data Analysis is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when running and documenting the empirical analysis for a Journal of Financial and Quantitative Analysis (JFQA) paper — finance data construction (CRSP/Compustat/TAQ/IBES), winsorizing, fixed effects, clustered and Newey-West standard errors, robustness, and heterogeneity — so results survive double-anonymous JFQA review and reproduce from the archived code. For theory papers, lighten this and document numerical examples instead.
Its SKILL.md is about 1.5k 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
- Clustered and Newey-West standard errors
- Heterogeneity — so results survive double-anonymous JFQA review and reproduce from the archived code
Example prompts
- “/jfqa-data-analysis”
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
Jfqa Data Analysis loads about 1.5k tokens when it runs. Until then it costs about 114 tokens; SKILL.md has 683 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). 683 words, ~1,540 tokens.
Download SKILL.mdSave it as
.claude/skills/jfqa-data-analysis/SKILL.md (or your agent's skills folder).- name
- jfqa-data-analysis
- description
- Use when running and documenting the empirical analysis for a Journal of Financial and Quantitative Analysis (JFQA) paper — finance data construction (CRSP/Compustat/TAQ/IBES), winsorizing, fixed effects, clustered and Newey-West standard errors, robustness, and heterogeneity — so results survive double-anonymous JFQA review and reproduce from the archived code. For theory papers, lighten this and document numerical examples instead.
JFQA Data Analysis (jfqa-data-analysis)
Use this skill to execute and document the estimation for a JFQA empirical finance paper so it is both credible and reproducible from the code you will archive (see jfqa-replication-and-data-policy).
Data construction (finance-specific)
- Build from standard sources (CRSP, Compustat, CRSP/Compustat Merged, TAQ, IBES, TRACE, OptionMetrics) and document every filter (share codes, exchanges, financials/utilities exclusions, delisting returns).
- Winsorize or trim outliers and disclose the cutoffs; finance variables (ratios, returns) have heavy tails.
- Report the sample period, the number of firms and observations, and the unit of analysis.
Estimation & inference
- Use fixed effects appropriate to the question; justify the clustering dimension (firm, time, or two-way) — finance referees will ask.
- For asset-pricing tests, use Fama-MacBeth with Newey-West or the appropriate correction; for panels, cluster-robust SEs.
- Report economic magnitudes (e.g., effect of a one-SD change, basis points, alpha per month), not just significance stars.
Robustness & heterogeneity
- Alternative samples, alternative variable definitions, alternative fixed effects and clustering.
- Subsample/heterogeneity cuts motivated by the mechanism, not fishing.
- Placebo or falsification tests where the design allows.
Reproducibility discipline
- One master script regenerating every table/figure from raw (or pseudo) data.
- Pin software/package versions; set and report seeds for any bootstrap/simulation.
- Keep the pipeline archive-ready as you go — JFQA may run random external code verification.
Theory papers
If the paper is theoretical, lighten this skill: replace empirical estimation with reproducible numerical examples / calibrations that illustrate the propositions, and document the computation so a reader can rerun it.
Standard-error decision grid (the first thing a JFQA referee checks)
| Setting | Inference JFQA referees expect | Also show |
|---|---|---|
| Firm panel, persistent outcome | two-way cluster (firm and year), or firm cluster with year FE | robustness to the other clustering choice |
| Fama-MacBeth on monthly returns | Newey-West with the lag count stated and justified | plain FMB SEs for comparison |
| Staggered policy adoption | cluster at the level of treatment assignment (e.g., state) | event-study leads/lags |
| Few clusters (roughly < 50) | wild cluster bootstrap p-values | the cluster count itself |
| Overlapping long-horizon returns | Newey-West/Hodrick lags matched to the horizon | non-overlapping subsample check |
| Generated regressors (betas, fitted values) | bootstrap or an errors-in-variables correction | the uncorrected SEs flagged as such |
An unjustified clustering choice is among the most common JFQA referee complaints; pre-empt it in the table notes, not just the text.
Show full SKILL.md (311 more words)Show less
Worked pass: a corporate-finance panel (numbers illustrative)
Hypothetical study of cash holdings and supplier concentration. Sample: Compustat 1990-2023, financials (SIC 6000-6999) and utilities (4900-4999) dropped, ratios winsorized at the 1st/99th percentiles. With firm and year fixed effects and two-way clustering, the standardized coefficient is 0.021 (t = 3.4): a one-SD rise in concentration moves cash/assets by 2.1 pp, about 12% of the 17.5 pp sample mean. The JFQA-grade write-up reports the 12%-of-mean line next to the t-stat, names the clustering in the note, and adds a falsification on firms with nationally diversified suppliers where the mechanism predicts nothing.
Filter log the referee will try to reconstruct
- CRSP: share codes 10/11; the exchange universe stated; delisting returns merged and the treatment of missing delisting returns disclosed.
- Compustat: accounting data lagged so it was publicly available at the return date; duplicate gvkey-period rows resolved.
- Linking: CCM link table with valid link-date ranges — never name matching.
- Any price or size screens (e.g., penny-stock exclusions) disclosed and shown not to drive the result.
- Each filter's observation loss tracked so the sample-construction table sums from raw pulls to the final N.
Execution bridge (StatsPAI / Stata MCP)
Run the battery, don't just enumerate it. Full map:
execution-with-mcp. JFQA is empirical finance (asset pricing + corporate) — the DiD / IV / RDD chain for corporate causal claims, the factor-zoo haircut for cross-sectional pricing.
- Many outcomes / specifications:
romano_wolf(step-down FWER, accounts for cross-test correlation) orbenjamini_hochberg— report the adjusted threshold. - OVB sensitivity:
oster_delta/sensemakr— the confounder strength that would overturn the headline. - Inference:
wild_cluster_bootstrap(few clusters),twoway_cluster/conley. - Re-fit off one handle:
audit_result(result_id)lists the missing checks and the exactsuggest_functionfor each — no guessing the battery. - Exhibits:
etable/did_summary_to_latexfrom the handle — no retyped numbers.
Keep the decisive checks in the body and the exhaustive (now actually-run) battery in the appendix. See the executed chain in the JF execution walkthrough.
Output format
【Sample】sources, filters, period, N firms/obs
【Estimator】FE / FMB / DID / IV + clustering justified
【Magnitudes】economic effect sizes reported
【Robustness】samples / definitions / placebos
【Next step】jfqa-tables-figures© 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-Financial-and-Quantitative-Analysis-Skills/skills/jfqa-data-analysis of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Jfqa 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 |
|---|---|---|---|---|---|---|
| Jfqa Data Analysis this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.5k | 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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Questions about Jfqa Data Analysis
What does Jfqa Data Analysis do?
A skill your agent uses when running and documenting the empirical analysis for a Journal of Financial and Quantitative Analysis (JFQA) paper — finance data construction (CRSP/Compustat/TAQ/IBES)…. Jfqa Data Analysis is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when running and documenting the empirical analysis for a Journal of Financial and Quantitative Analysis (JFQA) paper — finance data construction (CRSP/Compustat/TAQ/IBES), winsorizing, fixed effects, clustered and Newey-West standard errors, robustness, and heterogeneity — so results survive double-anonymous JFQA review and reproduce from the archived code.
When should I use Jfqa Data Analysis?
Jfqa Data Analysis fits situations like: clustered and Newey-West standard errors; heterogeneity — so results survive double-anonymous JFQA review and reproduce from the archived code.
How do I install Jfqa Data Analysis in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jfqa-data-analysis -a claude-code`. Or copy the skill folder (Journal-of-Financial-and-Quantitative-Analysis-Skills/skills/jfqa-data-analysis in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/jfqa-data-analysis in your project. Claude Code loads it when a task matches its description.
How do I install Jfqa Data Analysis in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jfqa-data-analysis -a codex`. Or copy the skill folder (Journal-of-Financial-and-Quantitative-Analysis-Skills/skills/jfqa-data-analysis in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/jfqa-data-analysis in your project. Codex loads it when a task matches its description.
Can I use Jfqa 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 jfqa-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/jfqa-data-analysis, .gemini/skills/jfqa-data-analysis, .github/skills/jfqa-data-analysis and .opencode/skills/jfqa-data-analysis in your project.
What does Jfqa Data Analysis need to run?
SKILL.md names no scripts, command-line tools or credentials: Jfqa Data Analysis is instructions for the agent only.
Does Jfqa 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 Jfqa 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 Jfqa Data Analysis use?
Jfqa 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 Jfqa Data Analysis use?
About 1.5k tokens (SKILL.md is roughly 6.2k 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 Jfqa Data Analysis?
Skills that share tags, products or a category with Jfqa 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 Jfqa 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.