kopai/create-dashboard
Create observability dashboards from OTEL metrics, logs, and traces using Kopai. Use when building metric visualizations, monitoring views, KPI panels, or when the user wants to see their telemetry data in a dashboard — even if they don't say "dashboard" explicitly. Also use when other skills or workflows need to present telemetry data visually (e.g. after root cause analysis).
100
100%
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 an excellent skill description that clearly defines its purpose, tool, and data domain while providing comprehensive trigger guidance. It covers both direct user requests and cross-skill invocation scenarios, and uses appropriate third-person voice throughout. The explicit mention of edge cases (e.g., users not saying 'dashboard') demonstrates thoughtful design for real-world skill selection.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple concrete actions: 'Create observability dashboards from OTEL metrics, logs, and traces using Kopai' along with specific use cases like 'metric visualizations, monitoring views, KPI panels'. Names the tool (Kopai) and data types (OTEL metrics, logs, traces). | 3 / 3 |
Completeness | Clearly answers both 'what' (create observability dashboards from OTEL metrics/logs/traces using Kopai) and 'when' (explicit 'Use when...' clause covering multiple trigger scenarios including cross-skill invocation after root cause analysis). | 3 / 3 |
Trigger Term Quality | Excellent coverage of natural trigger terms: 'dashboard', 'metric visualizations', 'monitoring views', 'KPI panels', 'telemetry data', 'OTEL', 'observability'. Also explicitly handles the case where users don't say 'dashboard' but want to visualize telemetry data. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive with a clear niche: OTEL-based observability dashboards via Kopai. The combination of the specific tool (Kopai), data domain (OTEL telemetry), and output type (dashboards/visualizations) makes it very unlikely to conflict with other skills. | 3 / 3 |
Total | 12 / 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 a well-crafted skill that is concise, highly actionable, and well-structured. It provides executable commands, a complete working example, a clear workflow with validation/retry feedback loop, and appropriate progressive disclosure through rules files and dynamic CLI output. The component compatibility table is a particularly useful quick-reference addition.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is lean and efficient. No unnecessary explanations of what dashboards or OTEL are. Every section serves a clear purpose — schema discovery, workflow steps, a concrete example, and a component reference table. The dynamic shell commands for schema/metrics are a smart use of space. | 3 / 3 |
Actionability | Provides fully executable bash commands for every step, a complete copy-paste-ready JSON example for creating a dashboard, and specific CLI commands with exact flags. The component compatibility table gives concrete guidance on which component to use for which metric type. | 3 / 3 |
Workflow Clarity | The 4-step workflow is clearly sequenced with an explicit validation/feedback loop in step 4: check for error, re-run metrics discover, fix the tree, and retry. This covers the error recovery path well for a potentially fragile operation (JSON tree creation). | 3 / 3 |
Progressive Disclosure | The skill provides a concise overview with the essential information inline, then clearly signals deeper content via the `rules/<rule-name>.md` reference for detailed rules and tree structure. The dynamic schema/metrics commands also serve as progressive disclosure by pulling live data rather than inlining static documentation. | 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.
Reviewed
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