/ml--model-training

/ml--model-training is a Claude Code skill in the AI & Agents section. Trains a model methodically: a trivial baseline first, then the simplest model that could work, with cross-validation and tuning.

Author's description

Train a model — baseline first, framework choice, cross-validation, tuning

How to ask for it

/ml--model-training train a model to predict customer churn
/ml--model-training start with a simple baseline before anything fancy
/ml--model-training tune hyperparameters with cross-validation

Install

curl -fsSL https://raw.githubusercontent.com/sgomez-dev/claude-skills/main/install.sh | bash

After installing with the script, type /ml--model-training. Using Cursor, Windsurf or Codex? Platform guides

Demo coming soon

/ml--recommender-systemRecommender system
Builds a recommender that beats a popularity baseline: picks the approach, handles cold start and evaluates it offline.
Built here
/ml--time-series-forecastTime series forecasting
Builds a forecast with naive baselines, seasonality, backtesting and prediction intervals, without leaking future data.
Built here
/agent-reachWeb and social media research
Searches and reads content from the web and 16 platforms (Reddit, YouTube, X, LinkedIn, GitHub, RSS…) to research a topic. Read-only: it never posts.
By Panniantong
/ai--agent-builderBuilding an AI agent
Designs and implements an LLM agent in your codebase: the loop, tools, memory, stop limits and a task-level eval suite to run before shipping.
Built here
/ai--ai-integrationIntegrating an AI API
Adds an AI API integration (Claude, OpenAI) to your app: client setup, retries, error handling, streaming, typed requests and tests with mocked responses.
Built here
/ai--chatbot-scaffoldProduction chatbot scaffold
Scaffolds a chatbot on your existing stack: streaming responses, persistent conversation history, optional RAG grounding and a feedback loop.
Built here

How to ask for it

Install

More from this section