vscode-customizations-AI-intro

Use when writing a git commit message. Proposes a clear, Conventional Commits-compliant message from the staged changes. Invoke with: /conventional-commit, commit message, write commit, git commit, stage and commit.

Use when deciding which Copilot customization primitive to use for a new requirement. Compares instructions, prompts, skills, agents, and hooks and recommends the best fit. Invoke with: /comparar-primitivas, choose primitive, which primitive, instruction vs prompt, skill vs prompt, agent vs skill.

Use when starting exploratory data analysis on a CSV file. Detects columns, types, missing values, and distributions. Proposes initial questions and produces a structured EDA notebook. Invoke with: /csv-eda-basica, eda, explore dataset, analyze csv, check data quality.

Use when documenting a dataset for a project. Creates a structured dataset card covering origin, variables, known biases, licence, and limitations. Invoke with: /dataset-card, document dataset, create dataset card, dataset documentation, data card.

Use when a Python script raises an exception or produces unexpected output. Follows a structured protocol: read the traceback, locate the line, isolate a hypothesis, propose the minimal fix. Invoke with: /debug-python-basico, debug, fix error, traceback, exception, python error.

Use when preparing a complex task for an AI agent. Evaluates what files, examples, or templates should be attached as context to get the best result. Invoke with: /evaluar-contexto-necesario, what context do I need, prepare context, context for agent, attach files.

Use when creating a new Copilot customization file (skill, prompt, agent, or instruction). Proposes the correct frontmatter fields and values. Invoke with: /frontmatter-designer, design frontmatter, write frontmatter, new skill frontmatter, new agent frontmatter.

Use when designing or preparing a practical exercise with clear steps, checkpoints, and deliverables. Structures a practice session for students or for personal learning. Invoke with: /preparar-practica, prepare exercise, design practice, create lab, practice structure.

Use when reviewing the quality of a Jupyter Notebook. Checks whether explanation and code are properly mixed, variable names are clear, and visualisations are justified. Invoke with: /revision-notebook, review notebook, check notebook quality, notebook audit.

Use when converting a specification document into an ordered implementation and validation task list. Applies a simplified Spec-Driven Development methodology. Invoke with: /spec-a-tareas, spec to tasks, implement spec, sdd, specification driven, break down spec.

Use when converting scattered TODO comments into a structured, prioritised backlog. Reads all TODOs in the project, estimates effort, identifies dependencies, and outputs a planning document. Invoke with: /todo-a-plan, convert todos, make plan from todos, backlog, prioritise tasks.