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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.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
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.
SOC 직업 분류 기준
| name | revision-notebook |
| description | 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. |
| argument-hint | Path to the notebook to review (e.g. notebooks/01_eda_example.ipynb) |
| user-invocable | true |
Audit a Jupyter Notebook for structural quality and pedagogical clarity.
Work through the checklist in references/quality-checklist.md.
Key rules:
a, b, tmp).data, result, output) that could be more descriptive.outputs array is non-empty).jupyter nbconvert --clear-output --inplace <file> before committing."A numbered list of findings with severity (Warning / Error) and the cell index, plus a summary score (0–10).