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 AmariahAK/atlarix-skills --skill acr-automl-hyperparameter-optimizationgit clone --depth 1 https://github.com/AmariahAK/atlarix-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/amariahak/atlarix-skills/acr-automl-hyperparameter-optimization)<a href="https://agentmods.dev/skills/amariahak/atlarix-skills/acr-automl-hyperparameter-optimization"><img src="https://agentmods.dev/badge/skills/amariahak/atlarix-skills/acr-automl-hyperparameter-optimization/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/amariahak/atlarix-skills/acr-automl-hyperparameter-optimization"><img src="https://agentmods.dev/badge/skills/amariahak/atlarix-skills/acr-automl-hyperparameter-optimization.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 | $0.00014 | $0.00658 |
| Opus 5 | $0.00007 | $0.00329 |
| Sonnet 5 | $0.00003 | $0.00132 |
| Haiku 4.5 | $0.00001 | $0.00066 |
Grade A, and why
AutoML and hyperparameter optimization rules for Python ML projects using Ray Tu 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 — 67 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AutoML and hyperparameter optimization rules for Python ML projects using Ray Tu
When to use this skill
AutoML and hyperparameter optimization rules for Python ML projects using Ray Tune, Optuna, PyCaret, and time-series AutoML libraries
Source
Synced from https://github.com/PatrickJS/awesome-cursorrules/tree/main/rules/automl-hyperparameter-optimization.mdc.
AutoML and Hyperparameter Optimization Rules
Scope
- Use AutoML to accelerate model exploration, not to bypass problem framing, validation design, or explainability.
- Start with a simple baseline model and fixed metric before launching a search.
- Keep training, evaluation, feature generation, and search configuration separate.
- Record datasets, splits, metric definitions, random seeds, library versions, and search spaces for every run.
Experiment Design
- Define the target metric before selecting tooling.
- Use nested validation or a final untouched test split for model selection claims.
- Use time-aware splits for time-series problems; never shuffle across time boundaries.
- Prevent leakage by fitting preprocessing only on training folds.
- Include simple baselines such as linear models, random forests, or naive time-series forecasts.
- Use early stopping and resource limits for expensive searches.
- Prefer structured search spaces with domain-informed ranges over arbitrary broad grids.
Tooling
- Use Ray Tune or Optuna for custom training loops, distributed trials, pruning, and scheduler control.
- Use PyCaret for quick low-code comparisons when the dataset and metric are straightforward.
- Use AutoTS, Merlion, PyAF, or project-approved time-series tooling when forecast-specific validation, seasonality, and horizon handling matter.
- Store run metadata in MLflow, Weights & Biases, TensorBoard, or a project-approved tracker.
- Use
uvor the existing project package manager for reproducible environments.
Search Spaces
- Keep search spaces explicit and reviewed.
- Use log-scale sampling for learning rates, regularization, tree counts, and other scale-sensitive values.
- Constrain model complexity to avoid unrealistic training time or memory use.
- Include preprocessing choices only when they can be applied without leakage.
- Do not tune on the test set.
What ships with it
1 file 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.
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 · 67 lines · 14 tokens per session scan A af06b2d17f81
AutoML and hyperparameter optimization rules for Python ML projects using Ray Tu is a skill published in the GitHub repository AmariahAK/atlarix-skills (2 stars, last pushed 3d ago), licensed Apache-2.0. It adds 14 tokens to every session and 658 once invoked, about $0.0001 per session on Opus 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 2026-08-31.
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