Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add HeshamFS/materials-simulation-skills --skill time-steppinggit clone --depth 1 https://github.com/HeshamFS/materials-simulation-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/heshamfs/materials-simulation-skills/time-stepping)<a href="https://agentmods.dev/skills/heshamfs/materials-simulation-skills/time-stepping"><img src="https://agentmods.dev/badge/skills/heshamfs/materials-simulation-skills/time-stepping/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/heshamfs/materials-simulation-skills/time-stepping"><img src="https://agentmods.dev/badge/skills/heshamfs/materials-simulation-skills/time-stepping.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00108 | $0.02730 |
| Opus 5.5 | $0.00043 | $0.01092 |
| Sonnet 5.5 | $0.00022 | $0.00546 |
| Haiku 4.5 | $0.00011 | $0.00273 |
Grade A, and why
time-stepping scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 216 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Time Stepping
Goal
Provide a reliable workflow for choosing, ramping, and monitoring time steps plus output/checkpoint cadence.
Requirements
- Python 3.10+
- 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, notes |
scripts/output_schedule.py |
output_times, interval, count |
scripts/checkpoint_planner.py |
checkpoint_interval, checkpoints, overhead_fraction, warnings |
output_schedule.py count is endpoint-inclusive: it includes both t_start and t_end, so count = number_of_intervals + 1 (e.g. t=0..5 at 0.05 spacing yields 101 frames for 100 intervals).
Workflow
- Get stability limits - Use numerical-stability skill
- Plan time stepping - Run
scripts/timestep_planner.py - Schedule outputs - Run
scripts/output_schedule.py - Plan checkpoints - Run
scripts/checkpoint_planner.py - Monitor during run - Adjust dt if limits change
Conversational Workflow Example
What ships with it
8 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- CHANGELOG.md 2.6 KB
- evals/evals.json 6.6 KB
- references/cfl_coupling.md 7.3 KB
- references/output_checkpoint_guidelines.md 9.1 KB
- references/ramping_strategies.md 7.6 KB
- scripts/checkpoint_planner.py 3.7 KB runs code
- scripts/output_schedule.py 2.8 KB runs code
- scripts/timestep_planner.py 6.2 KB runs code
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- yesterday Changed · +4 lines 79af6000da70
- 1mo ago First seen · 212 lines · 108 tokens per session scan A ab3d98b5f402
time-stepping is a skill published in the GitHub repository HeshamFS/materials-simulation-skills (82 stars, last pushed 2d ago), licensed Apache-2.0. It adds 108 tokens to every session and 2,730 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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