What does this skill do, and when should you use it?
This is a test-focused PDF documentation skill bundled in the Skill Seekers repository. It is intended for testing the golden PDF build and covers a three-page manual with getting-started and troubleshooting topics. It summarizes installation and API usage material, extracted code examples, documentation statistics, and reference-file navigation. It is not documented as a general-purpose PDF parser, OCR tool, or PDF conversion workflow.
Explains golden_pdf concepts, API references, code examples, tutorials, and documentation structure; indexes the Getting Started Guide, Installation Steps, API Usage, and Troubleshooting sections; reports three pages, four code blocks, two images or diagrams, three programming languages, and an average code-quality score of 7.4/10; points to references/manual.md and references/index.md.
- A maintainer testing whether the golden PDF build produces the expected skill documentation.
- A developer locating getting-started, installation, API, or troubleshooting material for golden_pdf.
- A reviewer checking extracted code examples and documentation statistics from the PDF build.
- A user inspecting the reference structure generated for the manual documentation.
How do you install this skill?
- This is a static review; no build or test was executed. Repository-level badges, CI, or version metadata do not prove that this selected skill passed validation.
- The Python example in SKILL.md is visibly truncated, and the referenced image assets are not confirmed by the supplied files.
- This file is a PDF golden-test artifact and should not be treated as the repository's full documentation/PDF/GitHub conversion capability.
- The source PDF, content freshness, attribution details, and sensitive-data handling are not documented.
- Local filesystem
The README documents installation for the full Skill Seekers collection with pip install skill-seekers. The repository contains 27 skills, but the supplied material does not document a separate installation procedure for this skill.
tmp="$(mktemp -d)"
git clone --depth 1 https://github.com/yusufkaraaslan/Skill_Seekers.git "$tmp"
mkdir -p ~/.claude/skills
cp -R "$tmp/tests/golden/phase2/pdf" ~/.claude/skills/
rm -rf "$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:
- Show the golden_pdf installation steps
- Locate the API Usage and Troubleshooting sections.
Invoke the skill in an Agent Skills-compatible client and ask questions about the golden PDF build, such as “Show the golden_pdf installation steps” or “Locate the API Usage and Troubleshooting sections.” Use references/index.md for the broader structure and references/manual.md for the manual reference.
What are this skill's strengths and limitations?
- Clearly scoped to golden PDF build testing.
- Provides page, topic, example, and reference-file navigation.
- Reports Bash, Python, and text code-block statistics.
- Uses standard SKILL.md frontmatter containing only name and description.
- Covers only a three-page manual, so its scope is narrow.
- Does not document actual PDF parsing, OCR, table extraction, or conflict-detection operations.
- Provides no separate installation procedure, test command, or maintenance workflow for this skill.
- One Python example is truncated and cannot be reused as a complete implementation.
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 PDF Build Tester this page | 25 · Not recommended | ★ 15k | 25d ago | MIT |
| Golden Word Documentation Skill | 30 · Not recommended | ★ 15k | 25d ago | MIT |
| EPUB Golden Build Guide | 36 · Not recommended | ★ 15k | 25d ago | MIT |
| PDF Chapter Classification Skill | 31 · Not recommended | ★ 15k | 25d ago | MIT |
| Web Handbook Skill — Multi-Page Fixture | 41 · Not recommended | ★ 15k | 25d ago | MIT |
How did FollowSkills review this skill?
The skill contains only a static PDF-document extract and does not instruct execution, network access, credential handling, or external side effects, so direct permission risk is limited. However, it omits data-flow disclosure, source attribution, sensitive-data handling, confirmation, and rollback guidance, so substantial points are deducted.
The SKILL.md, index, and manual are broadly consistent about pages and sections, and they expose code-block statistics. However, there are no test steps, input/output contract, abnormal-input handling, or diagnostic failure messages; the Python example is visibly truncated after x3, so the score remains low.
The stated use case is narrowly labeled as testing a PDF golden build, but trigger conditions, supported environments, non-fit boundaries, and an actionable workflow are absent. The supplied skill also provides no evidence of Chinese-language usability, so most adaptability points are deducted.
The skill uses a basic overview-to-reference structure, while repository context provides MIT licensing, version metadata, and CI/test maintenance signals. The selected skill itself lacks installation notes, changelog/versioning guidance, ownership/update path, FAQs, and limitation disclosure, and its name does not match the repository-level capability description, so points are deducted.
This is primarily a generated snapshot of a three-page PDF. It offers limited browsing value but no clear executable task, complete code, or directly usable output; its marginal benefit over reading the PDF directly is small, so the score is near the floor.
Page numbers, statistics, and reference paths provide limited traceability, and repository context includes test configuration and CI. Nevertheless, no executable evidence for this skill's key path is supplied; the example is truncated and the referenced image assets are not verifiable from the provided material, so the score is very low.
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 →