distributed-tracing
Implement distributed tracing with Jaeger and Tempo to track requests across microservices and identify performance bottlenecks. Use when debugging microservices, analyzing request flows, or implementing observability for distributed systems.
75
61%
Does it follow best practices?
Impact
100%
1.29xAverage score across 3 eval scenarios
Advisory
Suggest reviewing before use
Optimize this skill with Tessl
npx tessl skill review --optimize ./tests/ext_conformance/artifacts/agents-wshobson/observability-monitoring/skills/distributed-tracing/SKILL.mdQuality
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 a strong skill description that clearly communicates specific capabilities (distributed tracing with named tools), includes natural trigger terms users would employ, and explicitly states both what the skill does and when to use it. The mention of specific technologies (Jaeger, Tempo) and domain-specific terms (distributed tracing, observability, request flows) makes it highly distinctive and easy for Claude to select appropriately.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: 'implement distributed tracing with Jaeger and Tempo', 'track requests across microservices', and 'identify performance bottlenecks'. These are concrete, actionable capabilities. | 3 / 3 |
Completeness | Clearly answers both 'what' (implement distributed tracing with Jaeger and Tempo to track requests and identify bottlenecks) and 'when' (explicit 'Use when debugging microservices, analyzing request flows, or implementing observability for distributed systems'). | 3 / 3 |
Trigger Term Quality | Includes strong natural keywords users would say: 'distributed tracing', 'Jaeger', 'Tempo', 'microservices', 'request flows', 'observability', 'performance bottlenecks', 'debugging microservices'. Good coverage of terms a user working in this domain would naturally use. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive with specific tool names (Jaeger, Tempo) and a clear niche (distributed tracing for microservices). Unlikely to conflict with general monitoring, logging, or other observability skills due to the specific focus on tracing and named tools. | 3 / 3 |
Total | 12 / 12 Passed |
Implementation
22%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 comprehensive documentation than an actionable skill file. It is excessively verbose with redundant multi-language examples inline, explains concepts Claude already understands (trace structure, what spans are), and lacks a clear sequential workflow with validation steps. The referenced bundle files don't exist, and content that should be delegated to references is instead inlined, bloating the main file.
Suggestions
Add a clear numbered workflow (e.g., 1. Deploy Jaeger, 2. Instrument one service, 3. Verify traces appear in UI, 4. Add context propagation) with explicit validation checkpoints at each step.
Move multi-language instrumentation examples to a referenced file (e.g., references/instrumentation.md) and keep only one language example inline as a quick-start.
Remove the 'Distributed Tracing Concepts' section and best practices list — Claude already knows these concepts; focus on the specific configuration and code needed.
Create the referenced bundle files (references/jaeger-setup.md, references/instrumentation.md, assets/jaeger-config.yaml.template) and move detailed deployment configs there to keep SKILL.md as a concise overview.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is extremely verbose at ~300+ lines, explaining basic concepts Claude already knows (trace structure, what spans/tags/logs are), providing full instrumentation examples in three languages (Python, Node.js, Go) when one would suffice with a reference to others, and including generic best practices lists and troubleshooting tips that add little unique value. | 1 / 3 |
Actionability | The code examples are mostly executable and concrete (Jaeger deployment YAML, OpenTelemetry instrumentation code), but some are incomplete (e.g., `query_database()` and `fetch_users_from_db()` are undefined stubs, Go example missing imports like `attribute`). The sheer volume makes it hard to identify the critical path for getting started. | 2 / 3 |
Workflow Clarity | There is no clear sequenced workflow for implementing distributed tracing end-to-end. The content presents disconnected sections (setup, instrumentation, context propagation, sampling) without a numbered step-by-step process, and there are no validation checkpoints to verify traces are actually flowing through the system. | 1 / 3 |
Progressive Disclosure | References to external files are mentioned (references/jaeger-setup.md, references/instrumentation.md, assets/jaeger-config.yaml.template) but no bundle files exist to support them. The main file contains massive inline content (full instrumentation in 3 languages, full Tempo K8s deployment) that should be in referenced files, while the overview itself lacks a concise quick-start section. | 2 / 3 |
Total | 6 / 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
- Dicklesworthstone/pi_agent_rust
- Commit
b09ec7f
- Reviewed
Table of Contents
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