Translate visa application documents (images) to English and create a bilingual PDF with original and translation
57
57%
Does it follow best practices?
Impact
Pending
No eval scenarios have been run
Passed
No known issues
Quality
Discovery
67%Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.
The description is strong in specificity and distinctiveness, clearly carving out a narrow niche around visa document translation from images to bilingual PDFs. However, it lacks an explicit 'Use when...' clause and could benefit from additional trigger term variations to improve discoverability. Adding explicit trigger guidance and broader keyword coverage would elevate this from a good to excellent description.
Suggestions
Add a 'Use when...' clause, e.g., 'Use when the user needs to translate visa, immigration, or passport application documents from images into English.'
Include additional trigger terms and variations such as 'immigration documents', 'passport forms', 'consulate paperwork', specific image formats like '.jpg', '.png', and language-related terms like 'foreign language documents'.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: translate visa application documents, work with images, create bilingual PDF, include original and translation. These are clear, actionable capabilities. | 3 / 3 |
Completeness | Clearly answers 'what does this do' (translate visa documents from images, create bilingual PDF), but lacks an explicit 'Use when...' clause or equivalent trigger guidance, which caps this at 2 per the rubric. | 2 / 3 |
Trigger Term Quality | Includes good natural terms like 'visa application', 'translate', 'bilingual PDF', and 'images', but misses common variations users might say such as 'immigration documents', 'passport application', specific languages, or file format extensions like '.jpg', '.png'. | 2 / 3 |
Distinctiveness Conflict Risk | Very specific niche combining visa application documents, image-to-text translation, and bilingual PDF creation. This is unlikely to conflict with generic translation or PDF skills due to the narrow domain focus. | 3 / 3 |
Total | 10 / 12 Passed |
Implementation
27%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This skill is overly verbose, mixing high-level workflow instructions with unnecessary explanations, installation commands, and marketing copy. While the workflow sequence is reasonable, it lacks executable code for the most critical steps (OCR extraction, PDF generation) and has no validation checkpoints. The content would benefit significantly from trimming unnecessary sections and replacing vague instructions with complete, executable code.
Suggestions
Remove the Supported Documents list, Example Usage, marketing closing line, and installation commands - these waste tokens on information Claude already knows or can infer
Provide a complete, executable Python script for the PDF generation step instead of describing what the script should do
Add validation checkpoints: verify OCR output is non-empty before translating, preview/check PDF page count after generation
Move technical implementation details (OCR method fallback code) into a separate reference file, keeping SKILL.md as a lean workflow overview
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Significant verbosity throughout. Explains things Claude already knows (what document types are, how to install libraries, what OCR is). The supported documents list with Chinese translations, example usage section, and marketing-style closing line ('Perfect for visa applications to Australia...') all waste tokens. The technical implementation section lists installation commands Claude already knows how to find. | 1 / 3 |
Actionability | Provides a clear multi-step process with some concrete commands (sips conversion, pip installs), but the core OCR and PDF generation steps lack executable code. The Vision framework snippet is just two import lines, not a working implementation. The PDF generation step says 'Create a Python script' rather than providing one. Key implementation details are missing. | 2 / 3 |
Workflow Clarity | Steps are clearly numbered and sequenced, and there's a fallback strategy for OCR methods. However, there are no validation checkpoints - no step to verify OCR quality, confirm translation accuracy, or validate the generated PDF. For a multi-step process involving image manipulation and document generation, the lack of verification steps is a notable gap. | 2 / 3 |
Progressive Disclosure | Everything is in a single monolithic file with no references to external files. The supported documents list, technical implementation details, installation instructions, and example usage could all be separated or removed. The content mixes high-level workflow with low-level installation details in a flat structure. | 1 / 3 |
Total | 6 / 12 Passed |
Validation
100%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 11 / 11 Passed
Validation for skill structure
No warnings or errors.
Reviewed
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