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/holtwood/awesome-cursorrules-zh/scikit-learn-machine-learninggit clone --depth 1 https://github.com/holtwood/awesome-cursorrules-zhWrote 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/rules/holtwood/awesome-cursorrules-zh/scikit-learn-machine-learning)<a href="https://agentmods.dev/rules/holtwood/awesome-cursorrules-zh/scikit-learn-machine-learning"><img src="https://agentmods.dev/badge/rules/holtwood/awesome-cursorrules-zh/scikit-learn-machine-learning.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 | $0.00181 | $0.00181 |
| Opus 5 | $0.00090 | $0.00090 |
| Sonnet 5 | $0.00036 | $0.00036 |
| Haiku 4.5 | $0.00018 | $0.00018 |
Grade A, and why
scikit-learn-machine-learning 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 yesterday.
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
Scikit-learn 机器学习指南
数据预处理
管道操作(Pipeline)
from sklearn.pipeline import Pipeline
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import RandomForestClassifier
pipeline = Pipeline([
('imputer', SimpleImputer(strategy='median')),
('scaler', StandardScaler()),
('classifier', RandomForestClassifier(n_estimators=100))
])
模型训练
交叉验证最佳实践
from sklearn.model_selection import cross_val_score
scores = cross_val_score(
pipeline, X, y,
cv=5, # 5折交叉验证
scoring='accuracy'
)
print(f"平均准确率: {scores.mean():.2f} ± {scores.std():.2f}")
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.
- yesterday First seen · 32 lines · 181 tokens per session scan A 6e703c8edefd
scikit-learn-machine-learning is a cursor rule published in the GitHub repository holtwood/awesome-cursorrules-zh (232 stars, last pushed 29d ago), licensed MIT. It adds 181 tokens to every session, 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 2026-09-03.
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