micro-environment-simulatorlisted

# Micro Environment Simulator Use this skill when a workflow needs code-based simulation of learner environments before or during workshop replay analysis. ## Goal Create and run a **JavaScript abstract state machine** that models student execution environments and validates workshop assumptions step-by-step. ## Included Source Use the checked-in source directly (do not re-implement from scratch): - `.github/skills/micro-environment-simulator/simulator.js` - `.github/skills/micro-environment-simulator/workshop-student-journey.js` (workshop-specific example journey) - `.github/skills/micro-environment-simulator/workshop-student-population.json` (explicit synthetic-population assumptions) It exports: - `defaultEnvironmentForStudent(student, dayOfYear)` - `replayJourney({ student, date, initialState, steps, transitions })` - `replayWorkshop({ student, date, initialState, steps, transitions })` - `simulateStudents(students, date, config)` CLI usage: ```bash node .github/skills/micro-environment-simulator/simulator.js \ --students /tmp/gh-aw/agent/sim/data/profiles.json \ --journey .github/skills/micro-environment-simulator/workshop-student-journey.js \ --date "$TODAY" \ --out /tmp/gh-aw/agent/sim/data/environment-replay.json ``` Use `/tmp/gh-aw/agent/sim/data/environment-replay.json` as the source of environment mismatch diagnostics during simulation. Generate a reproducible synthetic cohort from the maintained population model: ```bash node .github/skills/mi