liteverge-opentelemetry-python
Add OpenTelemetry observability (traces, logs, metrics) to a Python app with Liteverge. Use when the user asks to add observability, tracing, logging, or metrics to a FastAPI, Django, Flask, or similar Python backend — or to add custom spans, logs, and instrumentation to an already-instrumented app.
94
92%
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 hits all the marks. It provides specific capabilities (traces, logs, metrics, custom spans), uses natural trigger terms developers would actually say, includes an explicit 'Use when' clause with multiple scenarios, and is distinctive enough to avoid conflicts with other skills. The description is concise yet comprehensive.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: 'Add OpenTelemetry observability (traces, logs, metrics)', 'add custom spans, logs, and instrumentation'. Also specifies the technology stack (Python, Liteverge) and frameworks (FastAPI, Django, Flask). | 3 / 3 |
Completeness | Clearly answers both what ('Add OpenTelemetry observability to a Python app with Liteverge') and when ('Use when the user asks to add observability, tracing, logging, or metrics...'). Includes explicit 'Use when' clause with multiple trigger scenarios. | 3 / 3 |
Trigger Term Quality | Excellent coverage of natural terms users would say: 'observability', 'tracing', 'logging', 'metrics', 'FastAPI', 'Django', 'Flask', 'Python backend', 'custom spans', 'instrumentation'. These are terms developers naturally use when seeking this functionality. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive with clear niche: specifically OpenTelemetry + Liteverge + Python backends. The combination of technology (OpenTelemetry), library (Liteverge), and target frameworks (FastAPI, Django, Flask) creates a unique, non-conflicting scope. | 3 / 3 |
Total | 12 / 12 Passed |
Implementation
85%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 routing skill that efficiently directs Claude to the appropriate detailed documentation based on user intent. The decision criteria are clear and the progressive disclosure is excellent. The main limitation is that the skill itself contains no executable guidance—it relies entirely on referenced files for actionability.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is lean and efficient, providing only decision logic and clear pointers to detailed files. No unnecessary explanations of what OpenTelemetry is or how tracing works. | 3 / 3 |
Actionability | The skill provides clear decision criteria but no executable code or commands itself—it's purely a routing document. The actionability depends entirely on the referenced files (init.md, instrument.md). | 2 / 3 |
Workflow Clarity | For a routing/decision skill, the workflow is crystal clear: three distinct decision paths with unambiguous criteria and a fallback check (look for opentelemetry-api in dependencies). | 3 / 3 |
Progressive Disclosure | Excellent structure with a concise overview and one-level-deep references to detailed files. Clear signaling of when to use each referenced document. | 3 / 3 |
Total | 11 / 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.
- Repository
- liteverge/liteverge-opentelemetry
- Commit
3e0a0be
- Reviewed
Table of Contents
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