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 agentmods add rules/patrickjs/awesome-cursorrules/automl-hyperparameter-optimizationgit clone --depth 1 https://github.com/PatrickJS/awesome-cursorrulesWhat 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 | $0.00000 | $0.00577 |
| Opus 5 | $0.00000 | $0.00289 |
| Sonnet 5 | $0.00000 | $0.00115 |
| Haiku 4.5 | $0.00000 | $0.00058 |
Grade A, and why
automl-hyperparameter-optimization 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 2d 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 — 56 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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.
Reporting
- Report the selected model, metric, confidence interval or variance, validation scheme, and final test result.
- Include the best parameters and the search budget.
- Compare the chosen model against the baseline and at least one non-AutoML alternative.
- Document operational constraints such as inference latency, memory use, retraining cost, and explainability.
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.
- 2d ago First seen · 56 lines · 0 tokens per session scan A 3d683db3136a
automl-hyperparameter-optimization is a cursor rule published in the GitHub repository PatrickJS/awesome-cursorrules (40,694 stars, last pushed 3mo ago), licensed CC0-1.0. It costs nothing until one of its globs matches a file; then it loads 577 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.
Other cursor rules, from other repositories
snyk_rules
Snyk Security At Inception.
cursorrules
// Good: Use anyhow for application errors use anyhow::{Context, Result}.
creating-cursor-rules
Meta-rule for creating effective Cursor IDE rules with best practices, patterns, and examples.
prpm-json-best-practices
Best practices for structuring prpm.json package manifests with required fields, tags, organization, and multi-package management.
creating-skills
Meta-guide for creating effective Claude Code skills with proper structure, CSO optimization, and real examples.
beanstalk-deploy
Robust deployment patterns for Elastic Beanstalk with GitHub Actions, Pulumi, and edge case handling.