arcgis-imagery
Work with raster and imagery data using ImageryLayer, ImageryTileLayer, pixel filtering, raster functions, multidimensional data, and oriented imagery. Use for satellite imagery, elevation data, and scientific raster datasets.
89
86%
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 strong skill description that clearly identifies its domain (raster and imagery data), lists specific capabilities and technologies, and provides explicit trigger scenarios. It uses proper third-person voice and covers both the 'what' and 'when' effectively with natural trigger terms that domain users would employ.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete capabilities: ImageryLayer, ImageryTileLayer, pixel filtering, raster functions, multidimensional data, and oriented imagery. These are distinct, named actions/components rather than vague abstractions. | 3 / 3 |
Completeness | Clearly answers both what ('Work with raster and imagery data using ImageryLayer, ImageryTileLayer, pixel filtering, raster functions, multidimensional data, and oriented imagery') and when ('Use for satellite imagery, elevation data, and scientific raster datasets'). | 3 / 3 |
Trigger Term Quality | Includes strong natural keywords users would say: 'satellite imagery', 'elevation data', 'raster', 'imagery', 'scientific raster datasets', plus technical terms like 'ImageryLayer', 'ImageryTileLayer', 'pixel filtering', 'raster functions' that domain users would naturally use. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive niche focused on raster/imagery data with specific layer types and processing techniques. Unlikely to conflict with general mapping, vector data, or other GIS skills due to the specific terminology like ImageryLayer, pixel filtering, and raster functions. | 3 / 3 |
Total | 12 / 12 Passed |
Implementation
72%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a strong reference-style skill with excellent actionability through complete, executable code examples covering the full range of imagery operations. Its main weakness is the lack of explicit multi-step workflows with validation checkpoints — it reads more as an API reference than a guided process. Minor verbosity in descriptive sentences could be trimmed, but overall the content is well-structured and useful.
Suggestions
Add a workflow example showing a complete multi-step process (e.g., load imagery layer → check multidimensional info → set dimensional definition → apply raster function → validate output) with explicit validation checkpoints.
Remove brief descriptive sentences like 'ImageryLayer connects to ArcGIS Image Services for dynamic raster data' — Claude already knows this from the code context and section headers.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is mostly efficient with good code examples and tables, but includes some unnecessary descriptive sentences (e.g., 'ImageryLayer connects to ArcGIS Image Services for dynamic raster data', 'ImageryTileLayer provides fast tiled access to imagery'). These brief descriptions add minimal value for Claude. The overall length is substantial but most content earns its place. | 2 / 3 |
Actionability | Excellent actionability with fully executable, copy-paste ready code examples throughout. Import patterns, layer creation, raster functions, pixel filtering, multidimensional data, and identify operations all have concrete, complete code. The pixel filter example is particularly thorough with a full working implementation. | 3 / 3 |
Workflow Clarity | The skill presents individual features well but lacks explicit multi-step workflows with validation checkpoints. For example, there's no guidance on the sequence of setting up an imagery layer with raster functions and then validating the output. The pixel filter section mentions calling redraw() in pitfalls but doesn't integrate it into a clear workflow. The content is more reference-oriented than workflow-oriented. | 2 / 3 |
Progressive Disclosure | Well-organized with clear section headers, tables for properties, and a 'Related Skills' section pointing to other skill files. The 'Reference Samples' section provides clear one-level-deep pointers to example projects. Content is appropriately structured for scanning and discovery without deeply nested references. | 3 / 3 |
Total | 10 / 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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