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 designing or auditing the numerical-experiments part of a Mathematical Finance (Wiley) manuscript — at this theory-first venue that means illustrative computation that…
Install Mathfin Data Analysis
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill mathfin-data-analysis -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills mathfin-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/Mathematical-Finance-Skills/skills/mathfin-data-analysis .claude/skills/mathfin-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 "mathfin-data-analysis" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Mathematical-Finance-Skills/skills/mathfin-data-analysis into .claude/skills/mathfin-data-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mathfin-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/Mathematical-Finance-Skills/skills/mathfin-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 mathfin-data-analysis -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills mathfin-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/Mathematical-Finance-Skills/skills/mathfin-data-analysis .agents/skills/mathfin-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 "mathfin-data-analysis" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Mathematical-Finance-Skills/skills/mathfin-data-analysis into .agents/skills/mathfin-data-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mathfin-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 mathfin-data-analysis -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills mathfin-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/Mathematical-Finance-Skills/skills/mathfin-data-analysis .cursor/skills/mathfin-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 "mathfin-data-analysis" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Mathematical-Finance-Skills/skills/mathfin-data-analysis into .cursor/skills/mathfin-data-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mathfin-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 Mathematical-Finance-Skills/skills/mathfin-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 mathfin-data-analysis -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills mathfin-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/Mathematical-Finance-Skills/skills/mathfin-data-analysis .gemini/skills/mathfin-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 "mathfin-data-analysis" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Mathematical-Finance-Skills/skills/mathfin-data-analysis into .gemini/skills/mathfin-data-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mathfin-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 mathfin-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 mathfin-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/Mathematical-Finance-Skills/skills/mathfin-data-analysis .github/skills/mathfin-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 "mathfin-data-analysis" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Mathematical-Finance-Skills/skills/mathfin-data-analysis into .github/skills/mathfin-data-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mathfin-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 mathfin-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 mathfin-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/Mathematical-Finance-Skills/skills/mathfin-data-analysis .opencode/skills/mathfin-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 "mathfin-data-analysis" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Mathematical-Finance-Skills/skills/mathfin-data-analysis into .opencode/skills/mathfin-data-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mathfin-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
mathfin-data-analysis- GitHub stars
- 1.2k
- Token cost
- ~1.3k tokens
- SKILL.md length
- 620 words
- Files
- 1
- Skills in repo
- 2,387
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when designing or auditing the numerical-experiments part of a Mathematical Finance (Wiley) manuscript — at this theory-first venue that means illustrative computation that…
- Works in 5 steps: Tie every experiment to a result. Each… → State the method precisely.… → Report error, not just output. Where the… → …
- Qualitative behavior)
- SKILL.md covers Note on framing, When to trigger, How to keep numerics… and Reproducibility (light but real), plus 5 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Mathfin Data Analysis is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when designing or auditing the numerical-experiments part of a Mathematical Finance (Wiley) manuscript — at this theory-first venue that means illustrative computation that SUPPORTS a proof (convergence, error bounds, qualitative behavior), never empirical data analysis. Keeps numerics rigorous, reproducible, and subordinate to the theorems.
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
- Qualitative behavior)
- Never empirical data analysis
Example prompts
- “/mathfin-data-analysis”
Workflow steps
5 steps, taken from the first numbered list in SKILL.md.
- Tie every experiment to a result. Each figure/table should illustrate a specific
- State the method precisely. Discretization scheme (Euler–Maruyama, Milstein, PDE
- Report error, not just output. Where the theory gives a rate or bound, show the
- Choose parameters with financial meaning (volatilities, maturities, strikes) so the
- Keep numerics subordinate. They support the theory; they are never the contribution.
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
Mathfin Data Analysis loads about 1.3k tokens when it runs. Until then it costs about 92 tokens; SKILL.md has 620 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). 620 words, ~1,326 tokens.
Download SKILL.mdSave it as
.claude/skills/mathfin-data-analysis/SKILL.md (or your agent's skills folder).- name
- mathfin-data-analysis
- description
- Use when designing or auditing the numerical-experiments part of a Mathematical Finance (Wiley) manuscript — at this theory-first venue that means illustrative computation that SUPPORTS a proof (convergence, error bounds, qualitative behavior), never empirical data analysis. Keeps numerics rigorous, reproducible, and subordinate to the theorems.
Numerical Experiments (mathfin-data-analysis)
Note on framing
This is a theory-first journal. Mathematical Finance explicitly states that numerical experiments are welcome only when accompanied by a rigorous analysis supporting the theoretical developments, and that routine application of computational methods to financial data will not be considered. So "data analysis" here is not empirical estimation — it is numerical work that illustrates or stress-tests a theorem. This skill is deliberately lighter than its empirical-journal counterpart.
When to trigger
- You want to add simulations or a numerical scheme to a proof-based paper
- A referee may ask whether your theorem "does anything" beyond existence
- You need to show convergence, accuracy, or qualitative behavior predicted by the theory
How to keep numerics journal-appropriate
- Tie every experiment to a result. Each figure/table should illustrate a specific theorem, proposition, or rate (e.g., "Monte Carlo error decays at the proven $O(n^{-1/2})$ rate", "the free boundary matches the smooth-fit characterization").
- State the method precisely. Discretization scheme (Euler–Maruyama, Milstein, PDE finite-difference/finite-element), step sizes, number of paths, variance reduction, truncation of the domain — enough that the experiment is reproducible.
- Report error, not just output. Where the theory gives a rate or bound, show the empirical rate against it; show convergence as the grid refines.
- Choose parameters with financial meaning (volatilities, maturities, strikes) so the illustration speaks to the modelling problem.
- Keep numerics subordinate. They support the theory; they are never the contribution. Do not let a numerical section grow into a stand-alone empirical study.
Reproducibility (light but real)
- Pin software/library versions; set and report random seeds for any Monte Carlo.
- Make illustrative code reproducible; consider archiving it (Zenodo/GitHub) and citing it.
- Include a Data Availability Statement even if no external data are used (see mathfin-replication-and-data-policy).
Matching scheme to result type
| Result being illustrated | Natural scheme | What the exhibit must report |
|---|---|---|
| Strong/weak SDE convergence rate | Euler–Maruyama or Milstein with halving steps | log–log error slope against the proven order |
| BSDE well-posedness or rate | Backward Euler / least-squares Monte Carlo / deep BSDE solver | terminal error and driver residual across grids |
| Optimal stopping / free boundary | Binomial tree or PDE variational-inequality solver | boundary location against the smooth-fit characterization |
| Rough-volatility approximation | Hybrid scheme for fractional kernels; Markovian lift | implied-vol skew slope against the proven power law |
| Duality gap = 0 | Primal candidate and dual bound computed independently | gap shrinking as the discretization refines |
| Mean-field limit | N-player simulation vs. McKean–Vlasov solver | distance to the limit decaying in N at the stated rate |
Show full SKILL.md (214 more words)Show less
Worked micro-example: convergence exhibit for a rough-volatility paper
Suppose Theorem 3.2 proves that a Markovian multi-factor approximation of a rough volatility model converges at a rate governed by the Hurst parameter H. The journal-appropriate exhibit: simulate both models with the same Brownian increments, plot the implied-volatility error against the number of factors on log axes, draw the theoretical slope as a reference line, and caption with the scheme, step size, path count, seed, and the theorem number. What would NOT fit: calibrating the approximation to index-option data and reporting fit quality — that turns an illustration into the empirical study the journal screens out.
Pre-submission numerics audit
- Every exhibit names the theorem, proposition, or rate it illustrates — no orphan plots.
- The observed rate is computed (regression slope), not eyeballed, and stated next to the proven one.
- Degenerate sanity cases (zero volatility, Black–Scholes limit, H → 1/2) reproduce known closed forms before the general runs are trusted.
- The numerical section would survive deletion: the theorems stand alone without it.
Anti-patterns
- A numerical study with no theorem behind it (out of scope for this journal).
- Plots with no error/convergence analysis where the theory promises a rate.
- Unstated scheme, step size, or path count — irreproducible.
- Calibrating to real market data and presenting it as the paper's result.
Output format
【Experiment】what it illustrates (which theorem/rate)
【Method】scheme + step/paths + variance reduction
【Error reported】empirical vs. theoretical rate/bound
【Parameters】financial values used
【Reproducibility】seeds + versions + code location
【Next step】mathfin-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 Mathematical-Finance-Skills/skills/mathfin-data-analysis of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Mathfin 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 |
|---|---|---|---|---|---|---|
| Mathfin Data Analysis this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.3k | 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 Mathfin Data Analysis
What does Mathfin Data Analysis do?
A skill your agent uses when designing or auditing the numerical-experiments part of a Mathematical Finance (Wiley) manuscript — at this theory-first venue that means illustrative computation that…. Mathfin Data Analysis is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when designing or auditing the numerical-experiments part of a Mathematical Finance (Wiley) manuscript — at this theory-first venue that means illustrative computation that SUPPORTS a proof (convergence, error bounds, qualitative behavior), never empirical data analysis.
When should I use Mathfin Data Analysis?
Mathfin Data Analysis fits situations like: qualitative behavior); never empirical data analysis.
How do I install Mathfin Data Analysis in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill mathfin-data-analysis -a claude-code`. Or copy the skill folder (Mathematical-Finance-Skills/skills/mathfin-data-analysis in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/mathfin-data-analysis in your project. Claude Code loads it when a task matches its description.
How do I install Mathfin Data Analysis in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill mathfin-data-analysis -a codex`. Or copy the skill folder (Mathematical-Finance-Skills/skills/mathfin-data-analysis in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/mathfin-data-analysis in your project. Codex loads it when a task matches its description.
Can I use Mathfin 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 mathfin-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/mathfin-data-analysis, .gemini/skills/mathfin-data-analysis, .github/skills/mathfin-data-analysis and .opencode/skills/mathfin-data-analysis in your project.
What does Mathfin Data Analysis need to run?
SKILL.md names no scripts, command-line tools or credentials: Mathfin Data Analysis is instructions for the agent only.
Does Mathfin 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 Mathfin 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 Mathfin Data Analysis use?
Mathfin 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 Mathfin 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 Mathfin Data Analysis?
Skills that share tags, products or a category with Mathfin 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 Mathfin 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.