banner-creator
Create banners using AI image generation. Discuss format/style, generate variations, iterate with user feedback, crop to target ratio. Use when user wants to create a banner, header, hero image, cover image, GitHub banner, Twitter header, or readme banner.
93
92%
Does it follow best practices?
Impact
92%
1.80xAverage score across 3 eval scenarios
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 strong skill description that clearly communicates what the skill does (AI-powered banner creation with iterative feedback and cropping), when to use it (explicit trigger clause with diverse banner types), and uses natural language terms users would actually say. It follows third-person voice, is concise without being vague, and occupies a clear niche that minimizes conflict with other skills.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: discuss format/style, generate variations, iterate with user feedback, crop to target ratio. These are clear, actionable steps in the banner creation workflow. | 3 / 3 |
Completeness | Clearly answers both 'what' (create banners using AI image generation with discussion, variations, iteration, and cropping) and 'when' (explicit 'Use when' clause listing multiple trigger scenarios). | 3 / 3 |
Trigger Term Quality | Excellent coverage of natural terms users would say: 'banner', 'header', 'hero image', 'cover image', 'GitHub banner', 'Twitter header', 'readme banner'. These are specific, varied, and match real user language. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive niche focused specifically on banner/header image creation. The specific mention of banner types (GitHub banner, Twitter header, readme banner) and the workflow (crop to target ratio) clearly distinguishes it from general image generation or design skills. | 3 / 3 |
Total | 12 / 12 Passed |
Implementation
85%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 skill with strong actionability and workflow clarity. The 6-step process is clearly sequenced with user checkpoints and iteration loops. Minor verbosity in the discovery section and some explanatory padding prevent a perfect conciseness score, but overall the skill is effective and well-organized.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is reasonably efficient but includes some unnecessary verbosity, such as the detailed list of style preferences and color preferences that Claude could infer. The discovery checklist is somewhat padded, though the workflow steps and command examples earn their place. | 2 / 3 |
Actionability | Provides fully executable bash commands for each step, concrete prompt patterns, specific crop dimensions, and copy-paste ready examples. The commands reference actual scripts with real flags and parameters. | 3 / 3 |
Workflow Clarity | Clear 6-step sequential workflow with explicit checkpoints: wait for user confirmation after discovery, iterate with user feedback loop (generate → preview → feedback → regenerate), and a defined crop-then-deliver sequence. The iteration loop in Step 4 is well-structured with clear feedback recovery. | 3 / 3 |
Progressive Disclosure | Well-structured with a clear overview workflow, quick reference section for common patterns, and appropriate references to external files (formats.md, example conversation). Content is logically split between the main skill and referenced materials. | 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
- resciencelab/opc-skills
- Commit
74fa178
- Reviewed
Table of Contents
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