What does this skill do, and when should you use it?
This is a test-oriented Jupyter Notebook reference skill from the Skill Seekers repository. It covers a Python 3 notebook using Python 3.11.4 and organizes material around data loading, evaluation, setup, and modeling results. It records NumPy, pandas, and scikit-learn dependencies and includes Bash and Python examples. It is best suited to validating the golden_jupyter_dir build and consulting notebook-derived references, rather than running a complete analytics project.
Indexes a Python 3 notebook and reports five sections, cell counts, languages, and imported packages; shows examples for installing pandas, reading a CSV with pandas, previewing a DataFrame, and timing code; and links to section files under references/ plus references/index.md.
- A developer testing the golden_jupyter_dir golden build can use it to verify the generated skill structure.
- A data analyst reproducing a basic CSV-loading workflow can consult the pandas example.
- A reviewer examining notebook methodology, evaluation, visualizations, or modeling results can follow the section navigation.
- A maintainer of Skill Seekers' Jupyter Notebook Scraper can use the reported statistics and reference-file map to inspect generated output.
How do you install this skill?
- Do not treat this as a complete data-analysis executor; it is primarily static reference material for a specific golden notebook.
- Before running examples, verify dependencies and the presence of data.csv; pip install changes the environment and should require user confirmation in an isolated environment.
- The SKILL.md code-cell counts do not match the displayed examples, and some examples intentionally fail, so reproducibility should not be assumed.
- There is no clear version pinning, maintenance ownership, or update path, so the referenced content may become stale.
- Shell / CLI
- Local filesystem
Python 3.11.4numpypandasscikit-learn
The README documents installation for the overall Skill Seekers collection with pip install skill-seekers and lists optional Jupyter support via pip install skill-seekers[jupyter]. The supplied source does not document how to install this individual SKILL.md into a particular agent directory, so its standalone installation location is unspecified.
tmp="$(mktemp -d)"
git clone --depth 1 https://github.com/yusufkaraaslan/Skill_Seekers.git "$tmp"
mkdir -p ~/.claude/skills
cp -R "$tmp/tests/golden/phase2/jupyter_dir" ~/.claude/skills/
rm -rf "$tmp"Generated from the source repository and skill path; it copies only this skill's folder. If the author's install steps above differ, follow those first. To scope it to one project, replace ~/.claude/skills with that project's .claude/skills.
How do you use this skill?
Use it in a golden_jupyter_dir build-testing or notebook-analysis context. Example trigger: Use when testing the golden_jupyter_dir golden build. For reference lookup, start with references/index.md and then open section_s2-s2.md, section_s5-s5.md, section_s1-s1.md, and section_s3-s4.md.
What are this skill's strengths and limitations?
- Its scope is explicit: testing the golden_jupyter_dir golden build.
- It provides section navigation, language and notebook statistics, dependencies, and code examples.
- It covers data loading, evaluation, setup, and modeling results.
- It uses standard name and description YAML frontmatter.
- It is a test reference skill, not a complete data-analysis tutorial or production workflow.
- One long Python example is truncated in the supplied source and cannot be fully reproduced.
- Example quality varies, including a %timeit example rated 2.0/10.
- The source does not specify a standalone installation directory or agent configuration method.
How does this skill compare with similar options?
Side by side with related skills; every score comes from the same FSRS standard.
| Skill | FS score | Stars | Last updated | License |
|---|---|---|---|---|
| Golden Jupyter Directory Notebook Skill this page | 34 · Not recommended | ★ 15k | 25d ago | MIT |
| Golden Jupyter Notebook Skill | 37 · Not recommended | ★ 15k | 25d ago | MIT |
| Jupyter Topics Build Test Skill | 31 · Not recommended | ★ 15k | 25d ago | MIT |
| Jupyter Data Analysis Reference | 26 · Not recommended | ★ 15k | 25d ago | MIT |
| Jupyter Notebook Workflow Assistant ✓ OpenAI · Official | 49 · Use with care | ★ 28k | 4mo ago | — |
How did FollowSkills review this skill?
The skill contains notebook content, code examples, and reference links, with no evident malware, credential theft, or exfiltration instructions. However, pip install pandas causes an external dependency change without source verification, user confirmation, isolation, or rollback guidance, and attribution and data-flow disclosure are limited, so points are deducted.
The structure is broadly coherent and records a NameError, but the metadata is inconsistent: Code Cells is listed as 2 while three Python examples and one Bash example are shown. The dependency list includes numpy and sklearn while the setup example installs only pandas, and some code is not directly runnable. Static review also caps this dimension at 10.
The scenario is identifiable and fits browsing this golden notebook's analysis workflow. However, the name and description are narrowly test-specific, with no declared non-fit cases, input/output boundaries, or precise trigger conditions, and no Chinese-language or environment-reachability guidance, so points are deducted.
The skill provides section organization, navigation, dependency information, and reference files, giving it basic progressive disclosure. It lacks installation guidance, version compatibility, changelog, maintenance ownership, update path, FAQs, and limitation disclosure; the generated-artifact marker also suggests test output rather than a maintained user skill, so points are deducted.
It can help a user browse notebook sections, code, and selected outputs, but the long example has limited practical value, one example is explicitly broken, and the metadata is inconsistent. There is no evidence that it independently completes an analysis task or produces a directly reusable complete result, so points are deducted. Static review caps this dimension at 7.
The skill includes section references, code, textual output, error output, and metric tables, providing limited traceability. It has no external source attribution, independent reproduction record, or committed tests covering the skill's key path, so the conclusion relies mainly on the submitted files. Static review caps this dimension at 5.
Open a dimension to read why it scored that way
Evidence confidence：Low — Mostly static review, author material or a limited demo; useful for discovery, not high-risk decisions.
See the full review method →