arcgis-ai-components
Add AI-powered natural language assistants to maps using the ArcGIS AI Components package. Use for chat-based map interaction, data exploration, navigation, and custom agents.
76
70%
Does it follow best practices?
Impact
Pending
No eval scenarios have been run
Advisory
Suggest reviewing before use
Optimize this skill with Tessl
npx tessl skill review --optimize ./contexts/5.0/skills/arcgis-ai-components/SKILL.mdQuality
Discovery
75%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 reasonably well-crafted description that clearly identifies its niche (ArcGIS AI Components for map-based AI assistants) and includes both what and when guidance. Its main weaknesses are moderate specificity in the listed capabilities and missing some natural trigger term variations that users might employ when seeking this skill.
Suggestions
Add more natural trigger term variations such as 'GIS', 'geospatial', 'mapping chatbot', 'Esri', or specific component names to improve discoverability.
List more concrete actions like 'configure map chat widgets, set up place search, build location-aware conversational agents' to increase specificity.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Names the domain (ArcGIS AI Components, maps) and some actions (chat-based map interaction, data exploration, navigation, custom agents), but these are somewhat high-level rather than deeply concrete actions like specific API calls or step-by-step operations. | 2 / 3 |
Completeness | Clearly answers both 'what' (add AI-powered natural language assistants to maps using ArcGIS AI Components) and 'when' ('Use for chat-based map interaction, data exploration, navigation, and custom agents'), with explicit trigger guidance via the 'Use for...' clause. | 3 / 3 |
Trigger Term Quality | Includes relevant keywords like 'ArcGIS', 'AI Components', 'maps', 'chat-based', 'natural language assistants', 'navigation', and 'custom agents', but misses common user variations like 'GIS', 'geospatial', 'mapping chatbot', 'map assistant', or specific component names users might reference. | 2 / 3 |
Distinctiveness Conflict Risk | The combination of 'ArcGIS AI Components' and 'natural language assistants to maps' creates a very specific niche that is unlikely to conflict with other skills. The domain is narrow and well-defined. | 3 / 3 |
Total | 10 / 12 Passed |
Implementation
64%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a comprehensive and highly actionable skill with excellent executable examples covering CDN setup, ESM imports, built-in agents, custom agents, and layout patterns. Its main weakness is that it tries to be both a quick-start guide and a complete API reference in one file, making it longer than necessary. Adding a clear sequential workflow with validation checkpoints and splitting API tables into a reference file would significantly improve it.
Suggestions
Move the detailed property/method/event tables for arcgis-assistant and each agent into a separate REFERENCE.md file, keeping only the most essential properties inline.
Add an explicit numbered workflow for getting an AI assistant working end-to-end, including authentication verification and agent registration validation steps.
Condense the common pitfalls section by removing redundant anti-pattern examples — a single correct example with a brief note about what to avoid would be more token-efficient.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is quite long (~300 lines) and includes extensive API reference tables (properties, methods, events) that could be split into a separate reference file. The common pitfalls section, while useful, is verbose with anti-pattern/correct pairs that add significant length. However, it doesn't over-explain basic concepts Claude already knows. | 2 / 3 |
Actionability | The skill provides fully executable, copy-paste ready HTML and JavaScript examples throughout — from the minimal CDN setup to custom agent definitions with real tool implementations. Import patterns, event listeners, and programmatic usage are all concrete and complete. | 3 / 3 |
Workflow Clarity | The skill covers setup well but lacks explicit sequential workflow steps with validation checkpoints. There's no clear 'Step 1, Step 2, Step 3' process for getting an AI assistant working end-to-end, and no verification steps (e.g., checking authentication status before rendering, validating agent registration). The requirements section mentions prerequisites but doesn't integrate them into a workflow. | 2 / 3 |
Progressive Disclosure | The skill has good section organization and references related skills at the bottom, but the extensive API reference tables (properties, methods, events for each component) are inlined rather than split into a separate REFERENCE.md file. The content would benefit from moving detailed API tables to a reference document and keeping the main skill focused on setup and usage patterns. | 2 / 3 |
Total | 9 / 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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