gemini-logo-remover
Remove Gemini logos, watermarks, or AI-generated image markers using OpenCV inpainting. Use this skill when the user asks to remove Gemini logo, AI watermark, or any logo/watermark from images.
95
93%
Does it follow best practices?
Impact
Pending
No eval scenarios have been run
Passed
No known issues
Quality
Discovery
100%Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.
This is a well-crafted skill description that clearly specifies the capability (removing logos/watermarks using OpenCV inpainting), includes natural trigger terms users would use, and explicitly states when to use the skill. The description is concise, uses third person voice correctly, and carves out a distinct niche that minimizes conflict with other image-related skills.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists specific concrete actions: 'Remove Gemini logos, watermarks, or AI-generated image markers using OpenCV inpainting.' Names the specific technique (OpenCV inpainting) and multiple specific targets (logos, watermarks, AI-generated markers). | 3 / 3 |
Completeness | Clearly answers both what ('Remove Gemini logos, watermarks, or AI-generated image markers using OpenCV inpainting') and when ('Use this skill when the user asks to remove Gemini logo, AI watermark, or any logo/watermark from images'). | 3 / 3 |
Trigger Term Quality | Includes natural keywords users would say: 'Gemini logo', 'AI watermark', 'logo/watermark from images'. These are terms users would naturally use when requesting this functionality. | 3 / 3 |
Distinctiveness Conflict Risk | Very specific niche targeting Gemini logos and AI watermarks specifically, with OpenCV inpainting as the method. Unlikely to conflict with general image editing skills due to the specific focus on logo/watermark removal. | 3 / 3 |
Total | 12 / 12 Passed |
Implementation
87%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a well-crafted, focused skill that provides immediately actionable code for logo removal. The conciseness and actionability are excellent with complete, executable examples. The main weakness is the lack of validation steps to verify successful removal or handle cases where inpainting produces poor results.
Suggestions
Add a validation step after inpainting, such as displaying or checking the result before final save (e.g., 'Preview the result and re-run with adjusted coordinates if artifacts appear')
Include guidance on what to do if inpainting produces visible artifacts (adjust radius, try INPAINT_NS algorithm, or expand/shrink the mask region)
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is lean and efficient, providing only necessary code examples without explaining what OpenCV or inpainting is. Every section serves a clear purpose with no padding. | 3 / 3 |
Actionability | Provides fully executable, copy-paste ready Python code with complete functions, specific examples, and clear parameter documentation. Both coordinate-based and corner-based approaches are immediately usable. | 3 / 3 |
Workflow Clarity | Steps are clear but lacks explicit validation checkpoints. No verification step to confirm the logo was successfully removed or guidance on what to do if inpainting produces artifacts. | 2 / 3 |
Progressive Disclosure | For a skill under 50 lines with a single focused task, the content is well-organized with clear sections (Setup, Usage variants, Output, Notes). No external references needed for this scope. | 3 / 3 |
Total | 11 / 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.
- Repository
- bear2u/my-skills
- Commit
1be5394
- Reviewed
Table of Contents
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