AgentSkillsCN

ml

机器学习模型指导：架构评审、训练流程设计、特征工程与模型部署。适用于ML模型的开发、模型代码的审查，以及ML系统的整体设计。 适用场景如下： - “对这个ML模型进行评审” - “设计ML训练流程” - “我该如何部署这个模型？” - “特征工程方面的建议” - “ML架构设计指导”

SKILL.md
--- frontmatter
name: ml
description: |
  Machine learning model guidance: architecture review, training pipeline design, feature engineering, deployment.
  Use when developing ML models, reviewing model code, or designing ML systems.

  Use when:
  - "review this ML model"
  - "design ML training pipeline"
  - "how should I deploy this model"
  - "feature engineering advice"
  - "ML architecture guidance"

ML Engineering Skill

Guide machine learning model development, training, and deployment.

Quick Start

Review Model Architecture

bash
/wicked-data:ml review path/to/model/

Reviews: Model choice, training data quality, evaluation strategy, deployment readiness.

Design Training Pipeline

bash
/wicked-data:ml pipeline --type classification

Generates: Data loading, feature engineering, training config, evaluation framework.

ML Development Workflow

1. Problem Definition

  • • Problem type clear (classification/regression/ranking)
  • • Success metric defined
  • • Baseline established
  • • Data availability confirmed
  • • Inference requirements understood (latency/throughput)

2. Data Assessment

  • • Sufficient volume (>1000x features)
  • • Labels accurate and consistent
  • • Features available at inference time
  • • No data leakage from target
  • • Class balance acceptable

3. Feature Engineering

Good features are: Predictive, Available at inference, Clean (no leakage), Interpretable.

Common transformations:

  • •Numeric: Scaling, log transform
  • •Categorical: One-hot, target encoding
  • •Time: Extract components, cyclical encoding
  • •Aggregations: Rolling windows, user stats

4. Model Selection

Data SizeStructuredRecommendation
<10K rowsYesLinear/Simple tree
10K-1MYesGradientBoosting (XGBoost/LightGBM)
>1MYesDeep learning possible
AnyImages/TextDeep learning

5. Training & Evaluation

Split strategy: Random (if i.i.d.), Time-based (if time series), Cross-validation (robust).

Key metrics:

  • •Classification: Accuracy, Precision, Recall, F1, AUC
  • •Regression: RMSE, MAE, R²

6. Hyperparameter Tuning

  • •Grid search: Exhaustive, slow
  • •Random search: More efficient
  • •Bayesian optimization: Most efficient

7. Deployment

Patterns: Batch scoring, REST API, Streaming

Checklist:

  • • Inference latency acceptable
  • • Model size appropriate
  • • Feature pipeline reproducible
  • • Monitoring instrumented
  • • Rollback plan documented

ML Monitoring

Model Performance: Prediction accuracy, distribution shifts, error rate by segment.

Data Quality: Feature distributions, missing rates, cardinality changes.

System Health: Latency (p50, p95, p99), throughput, memory.

Integration

  • •wicked-search: Find model code with /wicked-search:code "model|classifier"
  • •wicked-kanban: Track experiments as tasks
  • •wicked-data:numbers: Analyze training data

Best Practices

  • •Always baseline: Start simple, measure improvement, justify complexity
  • •Avoid leakage: Use only past data, split before processing
  • •Monitor production: Track predictions, detect drift, plan retraining
  • •Document everything: Architecture, features, training data, results

Reference

For detailed techniques: