Content
70%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-structured diagnostic workflow skill with clear sequencing, good conditional logic, and appropriate progressive disclosure to supporting files. Its main weaknesses are moderate redundancy across sections (Required Behavior, Workflow, Guardrails, and Output Contract overlap significantly) and a lack of concrete executable examples such as sample MCP call payloads or example trace IDs. Tightening the repeated instructions and adding one concrete example call would meaningfully improve it.
Suggestions
Deduplicate overlapping instructions: the rules about checking all Observability Platforms and not stopping after the first hit appear in Required Behavior (#7-10), Workflow step 5 (#7), and Guardrails. Consolidate into one authoritative location.
Add at least one concrete example MCP tool call with parameters and expected response shape to improve actionability (e.g., a sample `get_network_app_synthetics_metrics` call with filter parameters).
Include a concrete trace ID format example and validation rule (e.g., '32-character hex string matching `/^[0-9a-f]{32}$/`') rather than the vague 'Validate the trace ID format before using it.'
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is mostly efficient and avoids explaining basic concepts, but it is quite verbose for what it conveys. Several rules in 'Required Behavior' are restated in the workflow steps (e.g., checking distributed tracing, enumerating all Observability Platforms), and phrases like 'Do this even when direct trace lookup is available, because the extra telemetry helps explain the problem more completely' are unnecessarily explanatory for Claude. The repeated emphasis on checking every Observability Platform appears in Required Behavior, Workflow step 5, Guardrails, and Output Contract. | 2 / 3 |
Actionability | The skill names specific MCP tool calls (e.g., `list_network_app_synthetics_tests`, `get_service_map`, `get_network_app_synthetics_metrics`) and provides concrete parameters like `filter_dimension=TEST`, which is good. However, there are no executable code examples, no example MCP call payloads, and no concrete example of what a trace ID looks like or how to validate its format. The guidance is specific but not copy-paste ready. | 2 / 3 |
Workflow Clarity | The workflow is clearly sequenced across 6 numbered phases with sub-steps, includes explicit validation checkpoints (e.g., 'If no recent failure, stop unless user wants historical analysis'), feedback loops (e.g., 'If evidence is weak, ask for one precise next data point'), and conditional branching (service map available vs. fallback). The approval gate before code changes is explicit. The sequence is logical and handles edge cases well. | 3 / 3 |
Progressive Disclosure | The skill provides a clear overview with well-signaled one-level-deep references to reference.md (for metric names and correlation guidance) and examples.md (for output formatting). The main SKILL.md stays at the right level of abstraction, deferring detailed metric names and output templates to supporting files. Navigation is clear with specific instructions on when to load each file. | 3 / 3 |
Total | 10 / 12 Passed |