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 topprismdata/cultivating-ml-agent --skill autogluon-preset-strategygit clone --depth 1 https://github.com/topprismdata/cultivating-ml-agentWrote 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/topprismdata/cultivating-ml-agent/autogluon-preset-strategy)<a href="https://agentmods.dev/skills/topprismdata/cultivating-ml-agent/autogluon-preset-strategy"><img src="https://agentmods.dev/badge/skills/topprismdata/cultivating-ml-agent/autogluon-preset-strategy/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/topprismdata/cultivating-ml-agent/autogluon-preset-strategy"><img src="https://agentmods.dev/badge/skills/topprismdata/cultivating-ml-agent/autogluon-preset-strategy.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 | $0.00180 | $0.02212 |
| Opus 5 | $0.00090 | $0.01106 |
| Sonnet 5 | $0.00036 | $0.00442 |
| Haiku 4.5 | $0.00018 | $0.00221 |
Grade A, and why
autogluon-preset-strategy 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 11d 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 — 192 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AutoGluon Preset Strategy
Problem
autogluon-first says "use best_quality preset always". But:
best_qualitytakes 1-4 hours — wasted if you only need a quick baselinemedium_qualityis the default but doesn't enable bagging/stacking → no OOF predictions- The choice between presets has a 5x time × accuracy tradeoff that the existing skill doesn't quantify
Decision Tree
N = training rows
N < 10K → presets='best_quality' (single GBDT plateau is real concern)
N 10K-100K → presets='good_quality' first (10-30 min), upgrade to high if time allows
N > 100K → presets='good_quality' is enough (data variance dominates model choice)
GPU available?
No → presets='high_quality' max (TabPFNv2 needs GPU)
Yes → consider 'extreme_quality' (uses TabPFNv2, TabICL, Mitra)
Metric?
balanced_accuracy / F1 → add calibrate_decision_threshold=True
accuracy → default ('auto')
log_loss → use predict_proba, no calibration
Preset Reference (from AG 1.4.0 docstring)
| Preset | Train Time | OOF Available? | Models | When |
|---|---|---|---|---|
medium_quality (default) |
~5min | ❌ No (auto_stack=False) | 3 | NEVER for serious use |
good_quality |
10-30min | ✅ Yes | 12 | Default starting point |
high_quality |
30-60min | ✅ Yes | 14 | When good isn't enough |
best_quality |
1-4h | ✅ Yes | ~100 | Kaggle competition |
extreme_quality |
2-8h | ✅ Yes | 22+TabFM | GPU only, 30K rows ideal |
Why medium_quality has no OOF: AG's predict_oof() requires bag mode (auto_stack=False means no bag). The "val score" reported is in-sample on the holdout — not real OOF. Don't trust it.
Empirical Validation (s6e7, N=690K, 3-class)
| Preset | Time | Models | Best Val | OOF | OOF/Val gap |
|---|---|---|---|---|---|
| medium_quality | 182s | 3 | 0.8825 | N/A | N/A |
| good_quality | 376s | 12 | 0.8730 | 0.8730 | 0 |
| high_quality | 947s | 14 | 0.8739 | 0.8739 | 0 |
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.
- 11d ago First seen · 192 lines · 180 tokens per session scan A ed2a84f07a36
autogluon-preset-strategy is a skill published in the GitHub repository topprismdata/cultivating-ml-agent (5 stars, last pushed 14d ago), licensed MIT. It adds 180 tokens to every session and 2,212 once invoked, about $0.0009 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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