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.
76
63%
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
89%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 solid description that clearly communicates both what the skill does and when to use it, with good trigger terms covering the distributed tracing domain. The main weakness is that the capability description could be more granular—listing specific concrete actions like instrumenting services, configuring collectors, or analyzing trace data would strengthen specificity. Overall it performs well for skill selection purposes.
Suggestions
Add more specific concrete actions such as 'instrument services with OpenTelemetry, configure trace collectors, analyze span waterfalls, set up sampling strategies' to improve specificity.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Names the domain (distributed tracing) and specific tools (Jaeger, Tempo), and mentions actions like 'track requests' and 'identify performance bottlenecks', but doesn't list multiple concrete discrete actions (e.g., configure spans, set up collectors, create dashboards, instrument code). | 2 / 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', 'performance bottlenecks', 'observability', 'debugging microservices'. These cover a good range of terms a user would naturally use. | 3 / 3 |
Distinctiveness Conflict Risk | The combination of specific tools (Jaeger, Tempo) and the focused domain (distributed tracing across microservices) creates a clear niche that is unlikely to conflict with other skills like general monitoring, logging, or other observability tools. | 3 / 3 |
Total | 11 / 12 Passed |
Implementation
37%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This skill is a comprehensive but bloated reference document rather than an efficient, actionable skill. It provides excellent executable code examples across multiple languages and deployment targets, but fails to organize them into a clear workflow with validation steps. The content would benefit enormously from moving language-specific examples and deployment configs to reference files and focusing the main skill on a concise, step-by-step implementation workflow.
Suggestions
Add a clear sequential workflow (e.g., 1. Deploy collector → 2. Verify collector is running → 3. Instrument one service → 4. Verify traces appear in UI → 5. Add context propagation → 6. Verify cross-service traces) with explicit validation at each step.
Remove the 'Distributed Tracing Concepts' section entirely—Claude knows what traces, spans, and tags are. Remove the generic best practices list.
Move the multi-language instrumentation examples (Python, Node.js, Go) to references/instrumentation.md and keep only one language example inline as a quick-start pattern.
Move the full Kubernetes and Docker Compose deployment configs to the referenced files (references/jaeger-setup.md, assets/) and keep only a minimal quick-start deployment inline.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is extremely verbose at ~300+ lines, explaining basic tracing concepts Claude already knows (what a trace is, what a span is), providing full instrumentation examples in three languages (Python, Node.js, Go) when one with a note would suffice, and including a 'Distributed Tracing Concepts' section that adds no value for Claude. The best practices list is generic advice Claude already knows. | 1 / 3 |
Actionability | The skill provides fully executable, copy-paste ready code examples across multiple languages and deployment methods (Kubernetes, Docker Compose). Configuration files are complete YAML/Python/JS with specific endpoints, ports, and parameters. | 3 / 3 |
Workflow Clarity | There is no clear sequential workflow for implementing distributed tracing. The content is organized as a reference dump of configurations and code snippets without a step-by-step process. There are no validation checkpoints—no steps to verify traces are actually appearing, no feedback loops for debugging failed instrumentation. The troubleshooting section is vague bullet points. | 1 / 3 |
Progressive Disclosure | References to external files exist (references/jaeger-setup.md, references/instrumentation.md, assets/jaeger-config.yaml.template) which is good, but the main file itself contains massive inline code blocks that should be in those reference files instead. The content that's inline vs. referenced seems arbitrary—full Kubernetes deployments and multi-language instrumentation examples are inline while 'Jaeger installation' is referenced. | 2 / 3 |
Total | 7 / 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
47823e3
- Reviewed
Table of Contents
Is this your skill?
If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.