Jupyter Data Analysis Reference
A compact reference for understanding and reproducing a Jupyter notebook workflow.
The skill contains only static notebook-derived reference material and declares no credentials, network access, external effects, or sensitive-data handling, so no explicit red-line risk is shown. However, it lacks permission boundaries, source-integrity attribution, user confirmation, rollback, and data-flow disclosure; one example reads local data.csv, so substantial points are deducted.
The index, statistics, dependency list, and reference files are broadly consistent, and both normal output and a NameError example are preserved. However, the installation command covers only pandas, versions are absent, the long code example is visibly truncated, and there are no input constraints, controlled failure paths, or reproducibility checks, warranting only a low-to-mid score.
The stated use is narrowly and vaguely limited to testing the golden_jupyter_kw build. It does not define input/output contracts, semantic triggers, non-fit boundaries, or Chinese-language usage, and provides little evidence of fit for real analysis tasks, so the score is heavily reduced.
The material has front matter, section navigation, a reference index, statistics, and repository-level MIT license context. The skill itself lacks installation guidance, versions, changelog, maintenance owner, update path, FAQs, and known limitations; empty categories and incomplete code examples also reduce maintainability.
It can help inspect a small notebook's structure, code, outputs, and errors, with references that are easy to locate. But it is not the described documentation/GitHub/PDF-to-Claude skill, its examples are truncated, and there is no evidence that it completes a full analysis workflow or produces directly usable skill output, so only limited credit is justified.
The files provide limited auditable evidence through section listings, statistics, code, outputs, and an error record. There is no committed execution result specific to this skill, third-party evidence, or independent corroboration, and some displayed material is incomplete; therefore the static ceiling is applied.
- The assessed path does not match the repository-level product description; treat it as golden notebook-test reference material, not evidence of documentation, GitHub, or PDF conversion capability.
- Dependency guidance is incomplete: numpy, pandas, and sklearn are listed, but only pandas is installed in the example, with no versions or compatibility requirements.
- The long Python example is visibly truncated, and the %timeit example is intentionally failing; do not treat this as a complete runnable tutorial.
- The material does not explain the provenance or privacy handling of local data.csv, network behavior, external effects, or recovery from failures.
What it does & when to use it
This is a reference skill for testing the golden_jupyter_kw golden build. It covers notebook sections such as setup, loading, and modeling, with Python and Bash examples. It records dependencies including numpy, pandas, and scikit-learn, plus navigation links to generated reference files. Its scope is a specific test notebook, so it is better suited to validation and targeted reproduction than to production data-science work.
Summarizes the notebook structure, topics, dependencies, documentation patterns, reference paths, language breakdown, and code examples for golden_jupyter_kw. The examples include installing pandas, loading data.csv with pandas, and a modeling-related Python function.
- A test engineer validating Skill Seeker output can use it to inspect whether notebook sections, dependencies, and examples were preserved.
- A data analyst reproducing the loading workflow can follow the pandas CSV example as a starting point.
- A reader locating modeling material can use the quick-reference section and references/index.md navigation.
- A maintainer evaluating notebook-scraper quality can compare the reported statistics with the generated content.
Pros & cons
- Clearly scoped to one Jupyter notebook golden build.
- Includes section, dependency, language, statistics, and reference-file navigation.
- Provides both Python and Bash examples.
- The repository uses the MIT license and documents ongoing maintenance and extensive testing for the collection.
- Primarily a golden-build testing artifact rather than a production analysis skill.
- The longest Python example is truncated in the supplied source, so it cannot be reproduced completely.
- The reported counts are internally inconsistent: the document says 2 code cells, while the language breakdown lists 3 Python and 1 Bash code cell.
- No individual installation path, test command, or failure-handling procedure is documented for this skill.
How to install
This skill is located at tests/golden/phase2/jupyter_kw/SKILL.md in yusufkaraaslan/Skill_Seekers. The README documents the collection command: pip install skill-seekers[jupyter]. The source does not document a client-specific folder location for installing this individual skill.
How to use
After loading it as an Agent Skill, ask requests such as “Explain the golden_jupyter_kw analysis workflow,” “List its dependencies and notebook sections,” or “Show the pandas CSV-loading example.” No dedicated trigger command is documented.