183-java-observability-tracing-opentelemetry

Use when you need to implement or improve distributed tracing with OpenTelemetry in Java — including trace/span modeling, context propagation, semantic conventions, span attributes/events/status, sampling strategy, baggage usage, privacy safeguards, and backend integration with OTLP collectors. This should trigger for requests such as Improve tracing; Apply OpenTelemetry tracing; Add distributed tracing; Refactor tracing instrumentation. Part of cursor-rules-java project

Quality

67%

Does it follow best practices?

Impact

—

No eval scenarios have been run

Securityby

Passed

No known issues

Optimize this skill with Tessl

npx tessl skill review --optimize ./skills/183-java-observability-tracing-opentelemetry/SKILL.md

Quality

Content

35%

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

This skill reads more like a table of contents or project charter than actionable guidance. It lacks any concrete code examples, specific OpenTelemetry API usage, or executable patterns, delegating all substantive content to a reference file that wasn't provided. The workflow is logically sequenced but entirely abstract, making it difficult for Claude to act on without the referenced material.

Suggestions

Add at least 2-3 concrete, executable Java code examples showing core patterns: creating a span, propagating context across an HTTP call, and recording errors/status — these are the minimum for actionability.

Replace the abstract 'What is covered' bullet list with a concise quick-start section that demonstrates the most common tracing instrumentation pattern in under 10 lines of code.

Add explicit validation checkpoints in the workflow with concrete commands or code snippets, e.g., how to verify parent-child span relationships in test output or how to check OTLP export locally.

Trim the introductory paragraph and 'When to use this skill' section (which duplicates frontmatter trigger information) to improve token efficiency.

Dimension	Reasoning	Score
Conciseness	The skill includes some unnecessary framing ('What is covered in this Skill?' section) and verbose descriptions that Claude doesn't need. The workflow steps are somewhat padded with abstract descriptions rather than being lean and direct. However, it's not egregiously verbose.	2 / 3
Actionability	The skill provides no concrete code examples, no executable commands (beyond the generic mvn verify), no specific API calls, and no copy-paste ready instrumentation patterns. Everything is described abstractly — 'Add OpenTelemetry spans to key boundaries' — without showing how.	1 / 3
Workflow Clarity	The four workflow steps provide a reasonable sequence and include a validation step (run mvn verify, verify parent-child relationships), but the steps are abstract and lack explicit validation checkpoints with concrete commands or feedback loops for error recovery.	2 / 3
Progressive Disclosure	The skill references a detailed guide in references/183-java-observability-tracing-opentelemetry.md, which is good one-level-deep disclosure. However, no bundle files were provided to verify the reference exists, and the SKILL.md itself contains too much abstract overview content that could either be trimmed or replaced with actionable content, while the reference file presumably holds all the real value.	2 / 3
	Total	7 / 12 Passed

Description

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 defines its scope (OpenTelemetry distributed tracing in Java), lists specific capabilities comprehensively, and provides explicit trigger guidance with example user requests. The description is well-structured with both a 'Use when' clause and example trigger phrases, making it easy for Claude to select appropriately.

Dimension	Reasoning	Score
Specificity	Lists multiple specific concrete actions and concepts: trace/span modeling, context propagation, semantic conventions, span attributes/events/status, sampling strategy, baggage usage, privacy safeguards, and backend integration with OTLP collectors.	3 / 3
Completeness	Clearly answers both 'what' (implement/improve distributed tracing with OpenTelemetry in Java across many sub-topics) and 'when' (explicit 'Use when' clause at the start plus 'This should trigger for requests such as...' with concrete examples).	3 / 3
Trigger Term Quality	Includes strong natural trigger terms users would say: 'distributed tracing', 'OpenTelemetry', 'tracing', 'tracing instrumentation', 'OTLP collectors', plus explicit example phrases like 'Improve tracing', 'Add distributed tracing', 'Apply OpenTelemetry tracing'.	3 / 3
Distinctiveness Conflict Risk	Highly distinctive — scoped to OpenTelemetry distributed tracing in Java specifically, with domain-specific terms like OTLP collectors, span modeling, baggage usage, and semantic conventions that are unlikely to conflict with other skills.	3 / 3
	Total	12 / 12 Passed

Validation

100%

Warnings & errors only

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.

Repository: jabrena/cursor-rules-java
Commit: b73c9d3

Reviewed: 4 days ago

Table of Contents

Discovery Implementation Validation

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