name: time-stepping description: > Plan and control time-step policies for transient simulations — couple CFL and physics-based stability limits with adaptive stepping, ramp initial transients through sharp gradients or phase changes, schedule output intervals and checkpoint cadence, and plan restart strategies for long-running jobs. Use when choosing dt for a new simulation, diagnosing adaptive time-step oscillations, deciding checkpoint frequency to minimize lost work, or setting up output schedules aligned with physical time scales, even if the user only says "my run is too slow" or "how often should I save." allowed-tools: Read, Bash, Write, Grep, Glob metadata: author: HeshamFS version: "1.1.0" security_tier: high security_reviewed: true tested_with: - claude-code - gemini-cli - vs-code-copilot eval_cases: 2 last_reviewed: "2026-03-26"

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

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

Script Outputs (JSON Fields)

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:

   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

# 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

Interpretation Guidance

dt Behavior

Checkpoint Overhead

Security

Input Validation

• All numeric parameters (dt-target, dt-limit, safety, t-start, t-end, interval, run-time, checkpoint-cost, max-lost-time) are validated as finite positive numbers
• ramp-steps is validated as a non-negative integer with an upper bound
• Time range consistency is enforced (t-end must exceed t-start; checkpoint-cost must be less than run-time)

File Access

• Scripts read no external files; all inputs are provided via CLI arguments
• Scripts write only to stdout (JSON output); no files are created unless the agent explicitly uses the Write tool

Tool Restrictions

• Read: Used to inspect script source, references, and user configuration files
• Bash: Used to execute the three Python planning scripts (timestep_planner.py, output_schedule.py, checkpoint_planner.py) with explicit argument lists
• Write: Used to save generated time-step plans or checkpoint schedules; writes are scoped to the user's working directory
• Grep/Glob: Used to locate relevant files and search references

Safety Measures

• No eval(), exec(), or dynamic code generation
• All subprocess calls use explicit argument lists (no shell=True)
• Scripts use only Python standard library; no pickle loading or deserialization of untrusted data
• All output is deterministic JSON with no shell-interpretable content

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