What does this skill do, and when should you use it?
golden-jupyter-topics is a test-oriented Jupyter Notebook skill for the golden_jupyter_topics build. It summarizes the notebook structure, documented topics, dependencies, code examples, and reference-file navigation. The notebook uses a Python 3 kernel and identifies Python 3.11.4 as its language version, with numpy, pandas, and sklearn listed as imports. It is useful for build verification and example lookup, but it is not a complete data-analysis tutorial or production workflow.
The skill explains when to consult golden_jupyter_topics concepts, analysis workflow, code examples, outputs, methodology, visualizations, and results. It reports five sections, including Data Loading and Evaluation, identifies Getting Started as the major topic and Modeling Results as a subtopic, lists numpy, pandas, and sklearn as dependencies, and provides navigation to references/section_s2-s2.md, references/section_s5-s5.md, references/section_s1-s1.md, references/section_s3-s4.md, and references/index.md. Its examples include pip install pandas, loading data.csv with pandas, and %timeit broken().
- A test engineer validating whether the golden_jupyter_topics build produced the expected notebook structure.
- A developer looking up the notebook's Getting Started, Data Loading, or Modeling Results sections.
- A data-analysis learner reviewing the included pandas CSV-loading example.
- A maintainer checking generated dependencies, example quality, language metadata, and reference-file navigation.
How do you install this skill?
- Do not treat this as a complete Jupyter analysis or modeling assistant; it is primarily a generated topic index and sample excerpt.
- Before running examples, confirm that data.csv exists, pandas is installed, and the undefined broken() call is intentional or corrected.
- Add explicit inputs and outputs, version and maintenance information, and correct the cell-count and truncated-example inconsistencies before increasing the score.
- Shell / CLI
- Local filesystem
Python 3.11.4numpypandassklearn
This repository is a monorepo containing 27 skills; this skill is located at tests/golden/phase2/jupyter_topics/SKILL.md. The README documents installation for the overall Skill Seekers collection as pip install skill-seekers, but it does not provide a dedicated installation command or package for this test skill. In an Agent Skills-compatible client, place the directory containing this SKILL.md in the client's skill directory; the source does not specify a dedicated destination for this test skill.
tmp="$(mktemp -d)"
git clone --depth 1 https://github.com/yusufkaraaslan/Skill_Seekers.git "$tmp"
mkdir -p ~/.claude/skills
cp -R "$tmp/tests/golden/phase2/jupyter_topics" ~/.claude/skills/
rm -rf "$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?
Once installed, send your agent any of these to trigger it:
- Use the golden-jupyter-topics skill to inspect the golden_jupyter_topics build and list its notebook sections and dependencies.
Enable the skill in an Agent Skills-compatible client and use a request such as: “Use the golden-jupyter-topics skill to inspect the golden_jupyter_topics build and list its notebook sections and dependencies.” To reproduce the examples, use Python 3.11.4 with numpy, pandas, and sklearn. The source explicitly shows pip install pandas, but it does not provide a complete dependency-installation command. The reference files are under references/, with references/index.md as the stated entry point.
What are this skill's strengths and limitations?
- Its purpose and activation context are explicit: testing the golden_jupyter_topics build.
- It records section counts, language information, dependencies, examples, and reference paths for build inspection.
- It includes both Bash and Python examples for checking captured notebook content.
- Its frontmatter uses only the standard `name` and `description` fields, with no visible platform-specific hooks or subagent configuration.
- The scope is narrow and test-focused, so it is not a substitute for a complete Jupyter or data-analysis skill.
- The first Python example is truncated at `x3 ...` and cannot be run as shown.
- The `%timeit broken()` example is rated 2.0/10 and refers to an undefined function.
- The reported statistics appear inconsistent: the skill says there are 2 code cells, while the language breakdown lists 3 Python code cells and 1 Bash code cell.
- The source does not document a standalone installation workflow, provide the full notebook content, or establish independent test results for this skill.
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 |
|---|---|---|---|---|
| Jupyter Topics Build Test Skill this page | 31 · Not recommended | ★ 15k | 25d ago | MIT |
| Golden Jupyter Notebook Skill | 37 · 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 selected SKILL.md only provides notebook topics, code examples, and reference-file links. It discloses no credentials, network access, writes, external effects, user confirmation, rollback, or sensitive-data handling. No red-line is evident, but data-flow and security boundaries are entirely unspecified, so it receives 10/25.
The reference files broadly match the listed sections, dependencies, and examples, and one NameError is explicitly shown. However, the statistics are inconsistent (SKILL.md says two code cells while listing three Python examples plus one Bash example), the long example is truncated, and there are no run instructions, environment lock, robust error handling, or diagnostic guidance. Static calibration limits this to 5/20.
The trigger is limited to testing the golden_jupyter_topics golden build. Audience, inputs, outputs, non-fit boundaries, and semantic trigger conditions are unclear. Chinese-language use and mainland-China network compatibility are not addressed. It receives 4/15.
It has basic front matter, section navigation, dependency listing, a reference index, and repository-level MIT license metadata. The selected skill lacks installation guidance, stable versioning, changelog, maintenance owner, update path, FAQ, and limitation disclosure; its name and description describe a test build rather than a user-facing capability. It receives 6/15.
It can help a reader browse a small set of notebook topics, dependencies, and examples, but the examples include an artificial long variable sequence, an undefined-function error, and raw-cell content, without a complete executable workflow. Evidence for reproducible analysis or directly usable results is weak; it receives 3/15 under static calibration.
Committed Markdown references, code, output, and error text provide limited traceability. There is no skill-specific test coverage or third-party execution evidence, and the statistics conflict with the listed content. It receives 3/10 under static calibration.
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 →