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 ItamarZand88/awesome-agent-conventions --skill pycsegit clone --depth 1 https://github.com/ItamarZand88/awesome-agent-conventionsWrote 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/itamarzand88/awesome-agent-conventions/pycse)<a href="https://agentmods.dev/skills/itamarzand88/awesome-agent-conventions/pycse"><img src="https://agentmods.dev/badge/skills/itamarzand88/awesome-agent-conventions/pycse/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/itamarzand88/awesome-agent-conventions/pycse"><img src="https://agentmods.dev/badge/skills/itamarzand88/awesome-agent-conventions/pycse.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 | $0.00000 | $0.01187 |
| Opus 5 | $0.00000 | $0.00593 |
| Sonnet 5 | $0.00000 | $0.00237 |
| Haiku 4.5 | $0.00000 | $0.00119 |
Grade A, and why
pycse 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 10d 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 — 162 lines — stays where its author put it; the contents beside it link to each section on GitHub.
name: pycse description: Python computations in science and engineering (pycse) - helps with scientific computing tasks including nonlinear regression, uncertainty quantification, design of experiments (DOE), Latin hypercube sampling, surface response modeling, and neural network-based UQ with DPOSE. Use when working with numerical optimization, data fitting, experimental design, or uncertainty analysis.
pycse - Python Computations in Science and Engineering
pycse is a comprehensive library for scientific computing, data analysis, and uncertainty quantification in Python.
Core Capabilities
1. Nonlinear Regression and Curve Fitting
nlinfit: Nonlinear least squares fitting with uncertainty quantificationregress: Linear regression with statistics- Supports parameter uncertainty estimation and confidence intervals
2. Design of Experiments (DOE)
- Latin Hypercube Sampling (LHC): Space-filling designs for efficient parameter exploration
- Surface Response Modeling: Fit polynomial response surfaces to experimental data
- Useful for optimizing experimental conditions with minimal trials
3. Uncertainty Quantification with DPOSE
- DPOSE (Direct Propagation of Shallow Ensembles): Neural network ensemble for UQ
- Provides per-sample uncertainty estimates (heteroscedastic)
- Handles gaps, extrapolation, and nonlinear relationships
- Trained using CRPS or NLL loss for calibrated uncertainties
4. Numerical Methods
- Root finding and optimization
- Integration and differentiation
- ODE solvers
- Statistical analysis tools
When to Use pycse
Use this skill when the user asks about:
- Fitting experimental data to nonlinear models
- Estimating parameter uncertainties
- Designing experiments or sampling parameter spaces
- Latin squares or Latin hypercube designs
- Surface response methodology
- Uncertainty quantification in predictions
- Neural network-based surrogate models with uncertainty
- Scientific data analysis in Python
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.
- 10d ago First seen · 162 lines · 0 tokens per session scan A ae9ec0992f00
pycse is a skill published in the GitHub repository ItamarZand88/awesome-agent-conventions (31 stars, last pushed 1mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,187 tokens. 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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