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 linear-solversgit 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/linear-solvers)<a href="https://agentmods.dev/skills/beita6969/scienceclaw/linear-solvers"><img src="https://agentmods.dev/badge/skills/beita6969/scienceclaw/linear-solvers/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/linear-solvers"><img src="https://agentmods.dev/badge/skills/beita6969/scienceclaw/linear-solvers.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 | $0.00055 | $0.01537 |
| Opus 5.5 | $0.00022 | $0.00615 |
| Sonnet 5.5 | $0.00011 | $0.00307 |
| Haiku 4.5 | $0.00006 | $0.00154 |
Grade A, and why
linear-solvers 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 — 166 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Linear Solvers
Goal
Provide a universal workflow to select a solver, assess conditioning, and diagnose convergence for linear systems arising in numerical simulations.
Requirements
- Python 3.8+
- NumPy, SciPy (for matrix operations)
- See individual scripts for dependencies
Inputs to Gather
| Input | Description | Example |
|---|---|---|
| Matrix size | Dimension of system | n = 1000000 |
| Sparsity | Fraction of nonzeros | 0.01% |
| Symmetry | Is A = Aᵀ? | yes |
| Definiteness | Is A positive definite? | yes (SPD) |
| Conditioning | Estimated condition number | 10⁶ |
Decision Guidance
Solver Selection Flowchart
Is matrix small (n < 5000) and dense?
├── YES → Use direct solver (LU, Cholesky)
└── NO → Is matrix symmetric?
├── YES → Is it positive definite?
│ ├── YES → Use CG with AMG/IC preconditioner
│ └── NO → Use MINRES
└── NO → Is it nearly symmetric?
├── YES → Use BiCGSTAB
└── NO → Use GMRES with ILU/AMG
Quick Reference
| Matrix Type | Solver | Preconditioner |
|---|---|---|
| SPD, sparse | CG | AMG, IC |
| Symmetric indefinite | MINRES | ILU |
| Nonsymmetric | GMRES, BiCGSTAB | ILU, AMG |
| Dense | LU, Cholesky | None |
| Saddle point | Schur complement, Uzawa | Block preconditioner |
Script Outputs (JSON Fields)
| Script | Key Outputs |
|---|---|
scripts/solver_selector.py |
recommended, alternatives, notes |
scripts/convergence_diagnostics.py |
rate, stagnation, recommended_action |
scripts/sparsity_stats.py |
nnz, density, bandwidth, symmetry |
scripts/preconditioner_advisor.py |
suggested, notes |
scripts/scaling_equilibration.py |
row_scale, col_scale, notes |
scripts/residual_norms.py |
residual_norms, relative_norms, converged |
Workflow
- Characterize matrix - symmetry, definiteness, sparsity
- Analyze sparsity - Run
scripts/sparsity_stats.py - Select solver - Run
scripts/solver_selector.py - Choose preconditioner - Run
scripts/preconditioner_advisor.py - Apply scaling - If ill-conditioned, use
scripts/scaling_equilibration.py - Monitor convergence - Use
scripts/convergence_diagnostics.py - Diagnose issues - Check residual history with
scripts/residual_norms.py
What ships with it
10 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/convergence_patterns.md 10 KB
- references/preconditioner_catalog.md 8.0 KB
- references/scaling_guidelines.md 8.3 KB
- references/solver_decision_tree.md 7.9 KB
- scripts/convergence_diagnostics.py 2.2 KB runs code
- scripts/preconditioner_advisor.py 3.3 KB runs code
- scripts/residual_norms.py 5.5 KB runs code
- scripts/scaling_equilibration.py 4.2 KB runs code
- scripts/solver_selector.py 4.4 KB runs code
- scripts/sparsity_stats.py 2.7 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 · 166 lines · 55 tokens per session scan A 924c9c02c5f3
linear-solvers is a skill published in the GitHub repository beita6969/ScienceClaw (1,019 stars, last pushed 10d ago), licensed MIT. It adds 55 tokens to every session and 1,537 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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