mathfin-data-analysis
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.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill mathfin-data-analysis --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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 1. **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"). 2. **State the method precisely.** Discretization scheme (Euler–Maruyama, Milstei
- Note on framing
- When to trigger
- How to keep numerics journal-appropriate
- Reproducibility (light but real)
- Matching scheme to result type
- Worked micro-example: convergence exhibit for a rough-volatility paper
- Pre-submission numerics audit
- Anti-patterns
- Output format
What does the mathfin-data-analysis skill do?
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.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill mathfin-data-analysis --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From brycewang-stanford/Awesome-Journal-Skills, a repository with 909 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.