What does this skill do, and when should you use it?
Golden Jupyter is a test-oriented notebook skill for the golden_jupyter golden build. It organizes five notebook sections, including Getting Started and Modeling Results, with navigation to generated reference files. The notebook targets Python 3.11.4 and uses numpy, pandas, and scikit-learn. It is useful for inspecting analysis workflows and examples, but the source does not present it as a general-purpose Jupyter or production analytics skill.
Provides structured notebook metadata, section and topic summaries, dependency information, navigation pointers, and Bash and Python examples. It supports looking up Golden_Jupyter concepts and analysis workflow, reproducing analysis or computation steps, and reviewing methodology, visualizations, results, and library usage patterns. Reference locations include references/analysis.md and references/index.md.
- A developer testing the golden_jupyter golden build can use it to verify the generated notebook skill.
- A data analyst can consult it for the documented pandas CSV-reading example and modeling-related section.
- A user reproducing the notebook can use the stated Python 3.11.4 version and dependency list as environment guidance.
- A reviewer can use its section and reference-file navigation to inspect methods, visualizations, and results.
How do you install this skill?
- Do not treat the code snippets as a verified runnable workflow; repair the truncated long_example, undefined broken(), and inconsistent statistics first.
- The pip install and local-file-read examples do not document dependency locking, input provenance, or sensitive-data handling; isolate and confirm before using real data.
- The publisher is not verified by the FollowSkills enterprise registry and should be treated as unknown; the repository MIT license does not establish independent quality validation for this generated artifact.
- Local filesystem
Python 3.11.4numpypandasscikit-learn
The README documents collection-level installation with pip install skill-seekers[jupyter], which enables Jupyter Notebook support. It does not document a standalone installation directory for tests/golden/phase2/jupyter/SKILL.md, so the individual skill’s install path is unspecified.
tmp="$(mktemp -d)"
git clone --depth 1 https://github.com/yusufkaraaslan/Skill_Seekers.git "$tmp"
mkdir -p ~/.claude/skills
cp -R "$tmp/tests/golden/phase2/jupyter" ~/.claude/skills/
rm -rf "$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?
With the skill loaded, ask: Use golden-jupyter when testing the golden_jupyter golden build. You can also ask it to explain Golden_Jupyter concepts, locate code examples, reproduce analysis steps, or review methodology, visualizations, and results. Use references/analysis.md for analysis content and references/index.md for the complete notebook structure.
What are this skill's strengths and limitations?
- Clearly scoped to testing the golden_jupyter golden build.
- Includes section, topic, dependency, language, and reference-file navigation.
- Provides both Bash and Python examples and highlights Getting Started and Modeling Results.
- The scope is narrow: one notebook and five sections.
- A long Python example is truncated with an ellipsis in the supplied source, limiting direct reproducibility.
- No standalone install location or execution command is documented in SKILL.md.
- The source lists numpy, pandas, and scikit-learn but provides no complete environment file.
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 Notebook Skill this page | 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 |
| Golden Jupyter Directory Notebook Skill | 34 · 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 reference material only, with no observed malware, credential theft, or destructive defaults. However, it includes a pip install command and a local data.csv read without documenting data flow, dependency provenance, sensitive-data handling, user confirmation, or rollback, so points are deducted.
The structure and reference files are broadly aligned, but the SKILL.md statistics conflict with the displayed examples, the imported-package list is not fully reflected in the code, the long example is visibly truncated, and %timeit broken() explicitly raises NameError. No reproducible execution evidence for this skill's key path is present, so the static cap is applied conservatively.
The intended scenario—understanding and reproducing a golden Jupyter notebook—is stated, and kernel, language, and dependencies are partly described. Trigger conditions, inputs, outputs, non-fit boundaries, and failure boundaries remain unclear; the skill itself does not document Chinese support or mainland-China network reachability, so points are deducted.
The skill has basic front matter, section overview, dependency notes, quick reference, and references navigation. It lacks skill versioning, changelog, maintenance ownership, update path, FAQs, installation prerequisites, and known limitations, and it contains inconsistent statistics, so points are deducted.
It can serve as a short index of notebook content and code snippets, but it is not a complete directly runnable notebook reference: code is truncated, one example uses an undefined function, and statistics conflict. Users still need substantial verification and repair, and static evidence does not establish meaningful advantage over manual inspection.
The committed SKILL.md and reference files, together with repository-level tests and CI configuration, provide limited auditability. There is no skill-specific test coverage, third-party execution evidence, or independent corroboration for this target, and the artifact contains observable inconsistencies, so only limited credit is warranted.
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 →