Skip to content
Back to skills

Time Stepping

ASecurity

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.

  • 11 stars
  • 0 votes
  • 0 copies
  • 4 views
  • Added September 10, 2026
toolspythongobashreactdocumentation

Works with

  • cli

Security analysis

A100/100

Pro scans all 7 files and shows the line behind each finding

Scanned September 10, 2026

npx -y skills add luokai0/ai-agent-skills-by-luo-kai --skill time-stepping --agent claude-code

Installs into .claude/skills of the current project.

Are you the author of Time Stepping?

Add the live security badge to your README. It updates with every re-scan.

Security grade badge for Time Stepping
[![Security: A — Skills Directory](https://www.skillsdirectory.com/api/skills/luokai0-time-stepping/badge)](https://www.skillsdirectory.com/skills/luokai0-time-stepping)

More formats (shields.io, HTML) on the badges page. Keep it an A: scan every change in CI with Pro.

Download with Pro
SKILL.md
---
author: luo-kai
name: time-stepping
description: 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.
allowed-tools: Read, Bash, Write, Grep, Glob
---

# 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

Files in this skill

  • SKILL.md4.4 KB
  • references/cfl_coupling.md7.3 KB
  • references/output_checkpoint_guidelines.md9.1 KB
  • references/ramping_strategies.md7.6 KB
  • scripts/checkpoint_planner.py2.9 KB
  • scripts/output_schedule.py2.3 KB
  • scripts/timestep_planner.py4.8 KB

Attribution

Is this your skill, or is something wrong with this listing? Request removal or report an issue. Author removals are honored within 72 hours.

Comments

Loading comments…

Newsletter

Get the weekly list of safe new skills, and the ones we pulled.