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 skills/mxslr/mlcraft/domain-tabularnpx skills add mxslr/mlcraft --skill domain-tabulargit clone --depth 1 https://github.com/mxslr/mlcraftWrote 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/mxslr/mlcraft/domain-tabular)<a href="https://agentmods.dev/skills/mxslr/mlcraft/domain-tabular"><img src="https://agentmods.dev/badge/skills/mxslr/mlcraft/domain-tabular.svg" alt="Measured on agentmods" 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 | $0.00094 | $0.00565 |
| Opus 5 | $0.00047 | $0.00282 |
| Sonnet 5 | $0.00019 | $0.00113 |
| Haiku 4.5 | $0.00009 | $0.00056 |
Grade A, and why
domain-tabular 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 3d 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.
What it actually says
Tabular ML - Method Selection
For most tabular problems, gradient-boosted trees beat deep learning - start there.
Decision table
| Situation | Recommended | Notes |
|---|---|---|
| Default (any size, mixed types) | LightGBM / XGBoost / CatBoost | CatBoost handles categoricals natively; LightGBM is fast; XGBoost robust. Tune with Optuna. |
| Strong baseline / linear signal | Regularized Linear/Logistic, ElasticNet | Fast sanity baseline; interpretable. |
| Very large + rich, or embeddings needed | Deep tabular (FT-Transformer / TabNet) | Only when GBTs plateau and data is large. |
| Tiny data | Linear / simple trees + heavy regularization | Avoid overfitting; strong CV. |
| Ensembling | Blend/stack GBTs + linear | Usually the last real gain. |
Feature engineering & correctness
- Target leakage is the #1 killer: drop features known only after the outcome or that encode the label. Audit each feature's availability at prediction time.
- Fit encoders/scalers/imputers/target-encoding inside CV folds (on train only) - fitting on the full set leaks (
data-rigor-and-leakage). - Handle categoricals (native/CatBoost, or target/one-hot with fold-safe encoding), missing values, and skew deliberately.
- Time-aware data needs a time-based split (train earlier than test), never random.
Evaluation & imbalance
- CV: StratifiedKFold (classification), GroupKFold if entities repeat, TimeSeriesSplit if temporal.
- Imbalanced (fraud/churn): PR-AUC, recall at a fixed precision, class weights or focal/
scale_pos_weight- not raw accuracy. - Calibrate probabilities (Platt/isotonic) if scores drive decisions/thresholds.
- Explainability: SHAP for global + per-prediction attribution (great for stakeholder trust).
- Improve: use
accuracy-improvement-loop(feature engineering usually beats model swaps; then tuning, then stacking).
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.
- 3d ago First seen · 31 lines · 94 tokens per session scan A 63ce2ae2c9db
domain-tabular is a skill published in the GitHub repository mxslr/mlcraft (8 stars, last pushed 1mo ago), licensed MIT. It adds 94 tokens to every session and 565 once invoked, about $0.0005 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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