ScienceClaw is an agent system for scientific work that completes tasks as typed, executable workflows and turns replay-verified improvements into reusable skills and operators. It is intended for AI-assisted research across natural and social science disciplines. The catalogue entries are its skills and instruction for use with the system.
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 beita6969/ScienceClaw --skill numerical-stabilitygit clone --depth 1 https://github.com/beita6969/ScienceClawWrote 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/beita6969/scienceclaw/numerical-stability)<a href="https://agentmods.dev/skills/beita6969/scienceclaw/numerical-stability"><img src="https://agentmods.dev/badge/skills/beita6969/scienceclaw/numerical-stability/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/beita6969/scienceclaw/numerical-stability"><img src="https://agentmods.dev/badge/skills/beita6969/scienceclaw/numerical-stability.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00061 | $0.01568 |
| Opus 5.5 | $0.00024 | $0.00627 |
| Sonnet 5.5 | $0.00012 | $0.00314 |
| Haiku 4.5 | $0.00006 | $0.00157 |
Grade A, and why
numerical-stability 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 1mo ago.
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 — 150 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Numerical Stability
Goal
Provide a repeatable checklist and script-driven checks to keep time-dependent simulations stable and defensible.
Requirements
- Python 3.8+
- NumPy (for matrix_condition.py and von_neumann_analyzer.py)
- See
scripts/requirements.txtfor dependencies
Inputs to Gather
| Input | Description | Example |
|---|---|---|
Grid spacing dx |
Spatial discretization | 0.01 m |
Time step dt |
Temporal discretization | 1e-4 s |
Velocity v |
Advection speed | 1.0 m/s |
Diffusivity D |
Thermal/mass diffusivity | 1e-5 m²/s |
Reaction rate k |
First-order rate constant | 100 s⁻¹ |
| Dimensions | 1D, 2D, or 3D | 2 |
| Scheme type | Explicit or implicit | explicit |
Decision Guidance
Choosing Explicit vs Implicit
Is the problem stiff (fast + slow dynamics)?
├── YES → Use implicit or IMEX scheme
│ └── Check conditioning with matrix_condition.py
└── NO → Is CFL/Fourier satisfied with reasonable dt?
├── YES → Use explicit scheme (cheaper per step)
└── NO → Consider implicit or reduce dx
Stability Limit Quick Reference
| Physics | Number | Explicit Limit (1D) | Formula |
|---|---|---|---|
| Advection | CFL | C ≤ 1 | C = v·dt/dx |
| Diffusion | Fourier | Fo ≤ 0.5 | Fo = D·dt/dx² |
| Reaction | Reaction | R ≤ 1 | R = k·dt |
Multi-dimensional correction: For d dimensions, diffusion limit is Fo ≤ 1/(2d).
Script Outputs (JSON Fields)
| Script | Key Outputs |
|---|---|
scripts/cfl_checker.py |
metrics.cfl, metrics.fourier, recommended_dt, stable |
scripts/von_neumann_analyzer.py |
results.max_amplification, results.stable |
scripts/matrix_condition.py |
results.condition_number, results.is_symmetric |
scripts/stiffness_detector.py |
results.stiffness_ratio, results.stiff, results.recommendation |
Workflow
- Identify dominant physics (advection vs diffusion vs reaction)
- Run CFL checker with
scripts/cfl_checker.py - Compare to limits and adjust
dtif needed - Check stiffness with
scripts/stiffness_detector.pyif multiple scales - Analyze custom schemes with
scripts/von_neumann_analyzer.py - Check conditioning with
scripts/matrix_condition.pyfor implicit solves - Document the stability verdict and recommended time step
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.
- references/common_pitfalls.md 6.3 KB
- references/scheme_catalog.md 5.2 KB
- references/stability_criteria.md 4.9 KB
- scripts/cfl_checker.py 7.1 KB runs code
- scripts/matrix_condition.py 4.3 KB runs code
- scripts/requirements.txt 12 B
- scripts/stiffness_detector.py 3.4 KB runs code
- scripts/von_neumann_analyzer.py 4.1 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.
- 1mo ago First seen · 150 lines · 61 tokens per session scan A 8350046ff9c3
numerical-stability is a skill published in the GitHub repository beita6969/ScienceClaw (1,018 stars, last pushed 8d ago), licensed MIT. It adds 61 tokens to every session and 1,568 once invoked, about $0.0002 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 2026-09-03.
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