coil-compose
Expert guidance on using Coil for image loading in Jetpack Compose. Use this when asked about loading images from URLs, handling image states, or optimizing image performance in Compose.
96
95%
Does it follow best practices?
Impact
Pending
No eval scenarios have been run
Passed
No known issues
Quality
Discovery
89%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-structured skill description with explicit 'Use when' guidance and good trigger term coverage for the Coil/Compose image loading domain. The main weakness is that the capabilities could be more specific - listing concrete actions like caching, placeholders, transformations, or error handling would strengthen the description.
Suggestions
Add more specific concrete actions such as 'cache images', 'display loading placeholders', 'handle error states', or 'apply image transformations' to improve specificity.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Names the domain (Coil, Jetpack Compose, image loading) and mentions some actions (loading images from URLs, handling image states, optimizing performance), but lacks specific concrete actions like 'cache images', 'display placeholders', or 'transform images'. | 2 / 3 |
Completeness | Clearly answers both what ('Expert guidance on using Coil for image loading in Jetpack Compose') and when ('Use this when asked about loading images from URLs, handling image states, or optimizing image performance in Compose') with explicit trigger guidance. | 3 / 3 |
Trigger Term Quality | Includes natural keywords users would say: 'Coil', 'image loading', 'Jetpack Compose', 'loading images from URLs', 'image states', 'image performance', 'Compose'. These are terms developers would naturally use when seeking this help. | 3 / 3 |
Distinctiveness Conflict Risk | Very specific niche combining Coil + Jetpack Compose + image loading. The combination of these three specific technologies creates a clear, distinct trigger that is unlikely to conflict with generic Android or image processing skills. | 3 / 3 |
Total | 11 / 12 Passed |
Implementation
100%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is an excellent skill file that demonstrates best practices: concise, actionable code examples for each API variant, clear warnings about performance pitfalls, and a practical implementation checklist. The content respects Claude's intelligence by focusing on Coil-specific patterns rather than explaining general image loading concepts.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is lean and efficient, providing only necessary information without explaining what Coil is or how image loading works conceptually. Every section delivers actionable value without padding. | 3 / 3 |
Actionability | All code examples are complete, executable Kotlin snippets that can be directly copied into a Compose project. Each API variant includes ready-to-use code with proper imports implied by context. | 3 / 3 |
Workflow Clarity | For this single-purpose skill, the workflow is clear: choose the right composable based on use case, with explicit warnings about when NOT to use certain variants. The checklist provides a clear validation mechanism for implementation. | 3 / 3 |
Progressive Disclosure | Content is well-organized with clear sections progressing from primary use case to advanced variants. For a skill under 50 lines with no need for external references, the structure is appropriate and easy to navigate. | 3 / 3 |
Total | 12 / 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.
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