time-stepping
Plan and control time-step policies for simulations. Use when coupling CFL/physics limits with adaptive stepping, ramping initial transients, scheduling outputs/checkpoints, or planning restart strategies for long runs.
Best use case
time-stepping is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Plan and control time-step policies for simulations. Use when coupling CFL/physics limits with adaptive stepping, ramping initial transients, scheduling outputs/checkpoints, or planning restart strategies for long runs.
Teams using time-stepping should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/time-stepping/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How time-stepping Compares
| Feature / Agent | time-stepping | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Plan and control time-step policies for simulations. Use when coupling CFL/physics limits with adaptive stepping, ramping initial transients, scheduling outputs/checkpoints, or planning restart strategies for long runs.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Time Stepping ## Goal Provide a reliable workflow for choosing, ramping, and monitoring time steps plus output/checkpoint cadence. ## Requirements - Python 3.8+ - No external dependencies (uses stdlib) ## Inputs to Gather | Input | Description | Example | |-------|-------------|---------| | Stability limits | CFL/Fourier/reaction limits | `dt_max = 1e-4` | | Target dt | Desired time step | `1e-5` | | Total run time | Simulation duration | `10 s` | | Output interval | Time between outputs | `0.1 s` | | Checkpoint cost | Time to write checkpoint | `120 s` | ## Decision Guidance ### Time Step Selection ``` Is stability limit known? ├── YES → Use min(dt_target, dt_limit × safety) └── NO → Start conservative, increase adaptively Need ramping for startup? ├── YES → Start at dt_init, ramp to dt_target over N steps └── NO → Use dt_target from start ``` ### Ramping Strategy | Problem Type | Ramp Steps | Initial dt | |--------------|------------|------------| | Smooth IC | None needed | Full dt | | Sharp gradients | 5-10 | 0.1 × dt | | Phase change | 10-20 | 0.01 × dt | | Cold start | 10-50 | 0.001 × dt | ## Script Outputs (JSON Fields) | Script | Key Outputs | |--------|-------------| | `scripts/timestep_planner.py` | `dt_limit`, `dt_recommended`, `ramp_schedule` | | `scripts/output_schedule.py` | `output_times`, `interval`, `count` | | `scripts/checkpoint_planner.py` | `checkpoint_interval`, `checkpoints`, `overhead_fraction` | ## Workflow 1. **Get stability limits** - Use numerical-stability skill 2. **Plan time stepping** - Run `scripts/timestep_planner.py` 3. **Schedule outputs** - Run `scripts/output_schedule.py` 4. **Plan checkpoints** - Run `scripts/checkpoint_planner.py` 5. **Monitor during run** - Adjust dt if limits change ## Conversational Workflow Example **User**: I'm running a 10-hour phase-field simulation. How often should I checkpoint? **Agent workflow**: 1. Plan checkpoints based on acceptable lost work: ```bash python3 scripts/checkpoint_planner.py --run-time 36000 --checkpoint-cost 120 --max-lost-time 1800 --json ``` 2. Interpret: Checkpoint every 30 minutes, overhead ~0.7%, max 30 min lost work on crash. ## Pre-Run Checklist - [ ] Confirm dt limits from stability analysis - [ ] Define ramping strategy for transient startup - [ ] Choose output interval consistent with physics time scales - [ ] Plan checkpoints based on restart risk - [ ] Re-evaluate dt after parameter changes ## CLI Examples ```bash # Plan time stepping with ramping python3 scripts/timestep_planner.py --dt-target 1e-4 --dt-limit 2e-4 --safety 0.8 --ramp-steps 10 --json # Schedule output times python3 scripts/output_schedule.py --t-start 0 --t-end 10 --interval 0.1 --json # Plan checkpoints for long run python3 scripts/checkpoint_planner.py --run-time 36000 --checkpoint-cost 120 --max-lost-time 1800 --json ``` ## Error Handling | Error | Cause | Resolution | |-------|-------|------------| | `dt-target must be positive` | Invalid time step | Use positive value | | `t-end must be > t-start` | Invalid time range | Check time bounds | | `checkpoint-cost must be < run-time` | Checkpoint too expensive | Reduce checkpoint size | ## Interpretation Guidance ### dt Behavior | Observation | Meaning | Action | |-------------|---------|--------| | dt stable at target | Good | Continue | | dt shrinking | Stability issue | Check CFL, reduce target | | dt oscillating | Borderline stability | Add safety factor | ### Checkpoint Overhead | Overhead | Acceptability | |----------|---------------| | < 1% | Excellent | | 1-5% | Good | | 5-10% | Acceptable | | > 10% | Too frequent, increase interval | ## Limitations - **Not adaptive control**: Plans static schedules, not runtime adaptation - **Assumes constant physics**: If parameters change, re-plan ## References - `references/cfl_coupling.md` - Combining multiple stability limits - `references/ramping_strategies.md` - Startup policies - `references/output_checkpoint_guidelines.md` - Cadence rules ## Version History - **v1.1.0** (2024-12-24): Enhanced documentation, decision guidance, examples - **v1.0.0**: Initial release with 3 planning scripts
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