菜单
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.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
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 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.
| name | csv-eda-basica |
| description | 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. |
| argument-hint | Path to the CSV file to analyse (e.g. data/sample.csv) |
| user-invocable | true |
Perform a structured Exploratory Data Analysis (EDA) on a CSV file.
references/eda-checklist.md.assets/notebook-template.md as the structure..ipynb file at notebooks/eda_<dataset_name>.ipynb with cells matching each step above..github/instructions/notebooks.instructions.md.A populated EDA notebook ready to run, plus a brief summary in the chat of key findings.