image-enhancer

Improves the quality of images, especially screenshots, by enhancing resolution, sharpness, and clarity. Perfect for preparing images for presentations, documentation, or social media posts.

1.22x

Quality

28%

Does it follow best practices?

Impact

88%

1.22x

Average score across 3 eval scenarios

Securityby

Passed

No known issues

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npx tessl skill review --optimize ./.trae/skills/image-enhancer/SKILL.md

Quality

Discovery

50%

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 communicates the general purpose of image quality enhancement but lacks explicit trigger guidance ('Use when...'), which limits its effectiveness for skill selection. It has moderate specificity and trigger term coverage but could be more distinctive and include more natural user phrases to reduce ambiguity with other image-related skills.

Suggestions

Add an explicit 'Use when...' clause, e.g., 'Use when the user asks to enhance, upscale, sharpen, or improve the quality of an image or screenshot.'

Include more natural trigger terms users would say, such as 'upscale', 'blurry', 'low-res', 'improve photo', 'make image clearer', and common file extensions like '.png', '.jpg'.

Differentiate from other image skills by specifying what this skill does NOT do (e.g., 'Does not generate new images or perform creative editing') or by narrowing the scope more precisely.

Dimension	Reasoning	Score
Specificity	Names the domain (image quality improvement) and some actions (enhancing resolution, sharpness, clarity), but doesn't list multiple distinct concrete operations—it's essentially one action (enhance) with three attributes.	2 / 3
Completeness	The 'what' is reasonably covered (enhancing image resolution/sharpness/clarity), but there is no explicit 'Use when...' clause. The 'Perfect for...' phrase hints at use cases but doesn't provide explicit trigger guidance for when Claude should select this skill.	2 / 3
Trigger Term Quality	Includes some relevant keywords like 'screenshots', 'images', 'resolution', 'sharpness', 'presentations', 'documentation', but misses common user phrases like 'upscale', 'blurry', 'low-res', 'sharpen image', 'improve photo quality', or file extensions like '.png', '.jpg'.	2 / 3
Distinctiveness Conflict Risk	It's somewhat specific to image enhancement/upscaling, but 'images' and 'screenshots' are broad enough to potentially overlap with other image-related skills (e.g., image editing, image generation, screenshot annotation). The lack of a clear niche boundary increases conflict risk.	2 / 3
	Total	8 / 12 Passed

Implementation

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

This skill is essentially a marketing description rather than actionable technical guidance. It lacks any concrete implementation details—no libraries (Pillow, OpenCV, waifu2x, etc.), no executable code, no specific commands, and no real workflow. The content describes what image enhancement is rather than teaching Claude how to do it, violating the core principle that skills should provide actionable, executable guidance.

Suggestions

Add concrete, executable code examples using specific libraries (e.g., Pillow for sharpening with `ImageFilter.SHARPEN`, OpenCV for upscaling with `cv2.resize` using INTER_CUBIC/INTER_LANCZOS4)

Replace the fake output example with an actual implementation workflow: load image → analyze → apply specific enhancement operations → validate output → save

Consolidate the redundant sections ('When to Use', 'What This Skill Does', 'Common Use Cases') into a single brief overview, and use the saved space for actual implementation code

Add validation steps for batch processing (e.g., check output file size, verify dimensions, compare before/after quality metrics) to ensure operations succeed

Dimension	Reasoning	Score
Conciseness	The skill is verbose and padded with unnecessary content. It explains what the skill does in multiple redundant sections ('When to Use', 'What This Skill Does', 'Common Use Cases') that largely overlap. The 'What This Skill Does' section describes obvious image processing concepts Claude already knows. The fake output example adds bulk without teaching anything actionable.	1 / 3
Actionability	There is no executable code, no specific library or tool referenced, no concrete commands, and no actual implementation guidance. The 'How to Use' section contains vague natural language prompts rather than actionable instructions. The example output is fabricated and doesn't show how to actually perform any image enhancement operation.	1 / 3
Workflow Clarity	There is no real workflow defined. The numbered list in 'What This Skill Does' describes abstract concepts, not executable steps. There are no validation checkpoints, no error handling, no feedback loops for batch operations, and no concrete sequence of operations Claude could follow to actually enhance an image.	1 / 3
Progressive Disclosure	The content is organized into sections with headers, which provides some structure. However, it's a monolithic file with redundant sections that could be consolidated. There are no references to external files, but the content doesn't warrant them—the problem is that the sections themselves are poorly organized with significant overlap.	2 / 3
	Total	5 / 12 Passed

Validation

90%

Warnings & errors only

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 10 / 11 Passed

Validation for skill structure

Criteria	Description	Result
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	10 / 11 Passed

Repository: Lingjie-chen/MT5
Commit: 3069d33

Reviewed: 26 days ago

Table of Contents

Discovery Implementation Validation

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