Simulation Study
Scaffold and run a reproducible Monte Carlo simulation study in R, a declared...
Install
npx skills add pedrohcgs/claude-code-my-workflow --skill simulation-study -g -a claude-code -y- Repository
- pedrohcgs/claude-code-my-workflow1.7k
- Category
- Engineering
- Updated
- 1mo ago
- License
- MIT
- Context cost
- Mediumabout 2,623 tokens
- Runs scripts
- No, instructions only
What it does
Scaffold and run a reproducible Monte Carlo simulation study in R, a declared assumption regime, a parameterized DGP, an estimator grid, a seeded replication loop, and a summary of bias, RMSE, empirical SE, coverage, size/power with Monte Carlo standard errors. Use when the user says "run a Monte Carlo simulation", "simulation study", "check the bias/coverage of an estimator", "compare estimators in simulation", "size and power simulation", "Monte Carlo experiment", or wants to demonstrate an estimator's finite-sample properties. Produces a numbered R script in `scripts/R/` and saves per-replication raw results + a summary table to `output/`.
/simulation-study, Monte Carlo Simulation Study
Design and run a Monte Carlo experiment that characterizes an estimator's finite-sample behavior, then review it for the bugs that quietly invalidate simulation evidence.
Input: $ARGUMENTS, a description of the estimator(s) and DGP to study (e.g., "compare 2SLS vs LIML under weak instruments with heteroskedasticity"), or a pointer to an existing script/paper whose simulation you want to reproduce or extend.
Constraints
- Follow
.claude/rules/simulation-conventions.md, the simulation contract (DGP, truth, estimand, MCSE, assumption regime) is non-negotiable. - Declare the assumption regime in the script header and respect the firewall, an out-of-assumption run never supports a within-assumption claim (
simulation-conventions.md§2). - Follow
.claude/rules/r-code-conventions.mdfor general R standards (header,library()at top, relative paths, numerical discipline). - Save the script to
scripts/R/with a numbered, descriptive name (e.g.,scripts/R/sim_2sls_vs_liml.R). - Save outputs (per-rep raw tibble, summary table, figures) to
output/. saveRDS()the per-replication raw results, not just the summary, re-aggregation and the review pass need them.- Run the
sim-revieweragent on the generated script before presenting results, then address Critical/High findings.
Workflow Phases
Phase 0: Pre-Flight Report
Before writing any code, produce a Pre-Flight Report showing you have pinned down the experiment. This prevents the most common failure mode, a beautiful results table built on a mismatched estimand or a coverage-against-the-estimate bug.
## Pre-Flight Report, Simulation Design
