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 seb1n/awesome-ai-agent-skills --skill hyperparameter-tuninggit clone --depth 1 https://github.com/seb1n/awesome-ai-agent-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/seb1n/awesome-ai-agent-skills/hyperparameter-tuning)<a href="https://agentmods.dev/skills/seb1n/awesome-ai-agent-skills/hyperparameter-tuning"><img src="https://agentmods.dev/badge/skills/seb1n/awesome-ai-agent-skills/hyperparameter-tuning/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/seb1n/awesome-ai-agent-skills/hyperparameter-tuning"><img src="https://agentmods.dev/badge/skills/seb1n/awesome-ai-agent-skills/hyperparameter-tuning.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 | $0.00049 | $0.02051 |
| Opus 5 | $0.00024 | $0.01026 |
| Sonnet 5 | $0.00010 | $0.00410 |
| Haiku 4.5 | $0.00005 | $0.00205 |
Grade A, and why
hyperparameter-tuning 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- hyperparameter-tuning — 95% identical, 2 lines differ
How it starts
The opening of the file, as written. The whole thing — 161 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Hyperparameter Tuning
This skill enables an AI agent to systematically search for optimal hyperparameter configurations for machine learning models. It covers defining search spaces, selecting search strategies (grid, random, Bayesian, Hyperband), running trials with cross-validation, applying early stopping to prune poor configurations, and analyzing results to identify the best-performing parameters. The agent balances exploration and exploitation to find strong configurations within a given computational budget.
Workflow
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Define the search space: Specify each hyperparameter with its type (categorical, integer, float) and range. Use log-uniform distributions for parameters that span orders of magnitude (e.g., learning rate from 1e-5 to 1e-1). Group related parameters and define conditional search spaces where certain parameters only apply when others take specific values.
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Select the search strategy: Choose the tuning algorithm based on compute budget and search space size. Grid search is exhaustive but only feasible for small spaces. Random search is a strong baseline that scales better. Bayesian optimization (Tree-structured Parzen Estimators or Gaussian Processes) is most sample-efficient for expensive evaluations. Hyperband and ASHA combine early stopping with random search for deep learning workloads.
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Configure evaluation: Set up k-fold cross-validation (typically 5-fold) for reliable performance estimates on small to medium datasets. For large datasets or expensive models, use a single holdout validation set. Define the objective metric to optimize (e.g., validation F1, AUC-ROC, RMSE) and whether to minimize or maximize it.
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Run trials with pruning: Execute the search, launching trials in parallel when possible. Enable pruning to terminate underperforming trials early based on intermediate results (e.g., after a few epochs of training), freeing compute for more promising configurations.
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Analyze and select results: Inspect the optimization history to understand which hyperparameters matter most (importance analysis). Visualize parameter interactions with contour plots or parallel coordinate plots. Select the best configuration and retrain the final model on the full training set with those parameters.
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 · 161 lines · 49 tokens per session scan A a946c82a3544
hyperparameter-tuning is a skill published in the GitHub repository seb1n/awesome-ai-agent-skills (179 stars, last pushed 1mo ago), licensed MIT. It adds 49 tokens to every session and 2,051 once invoked, about $0.0002 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 2026-08-30.
Other skills, from other repositories
shipping-reproducible-results
Package completed data analysis and ML work so an independent recipient can reproduce the claimed results, verify artifact lineage, and operate the handoff within its stated scope. Use when finalizing a project, study, model package, or review bundle; not for deploying to a live system.
validating-models-and-claims
Validate trained models and analytical claims against their intended decision, independent evidence, and human-reviewed ground truth. Use when reviewing model performance, analysis conclusions, launch claims, or evaluation reports; use failure diagnosis instead when the main task is locating a known defect.
auditing-data-and-ground-truth
Audit datasets, joins, labels, and ground truth before analysis or modeling. Use when data meaning, row grain, time semantics, source-of-truth reliability, or label construction may invalidate conclusions; not for general model evaluation after the evidence base is already trusted.
designing-leakage-safe-experiments
Design leakage-safe machine learning experiments that mirror real deployment and support fair model comparisons. Use when defining prediction timing, feature eligibility, train-validation-test splits, baselines, metrics, or controlled model iterations; not for auditing whether raw labels are trustworthy.
diagnosing-ml-failures
Isolate the root cause of ML performance drops, inconsistent evaluations, prediction errors, and training-serving mismatches across data, labels, splits, pipelines, models, metrics, and runtime behavior. Use when investigating a reproducible failure or regression, not routine model selection or general performance…
using-data-analysis
Route data analysis and machine learning work to the right skill in this suite. Use when starting any analysis, modeling, validation, or reproducibility task and the matching specialized skill is not yet clear; not needed when one specific skill already clearly applies.