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.mdSecurity
2 findings — 2 medium severity. This skill can be installed but you should review these findings before use.
The skill exposes the agent to untrusted, user-generated content from public third-party sources, creating a risk of indirect prompt injection. This includes browsing arbitrary URLs, reading social media posts or forum comments, and analyzing content from unknown websites.
Third-party content exposure detected (high risk: 0.70). The SKILL.md explicitly instructs fetching and applying a public GitHub manifest (kubectl create -f https://github.com/jaegertracing/jaeger-operator/releases/download/v1.51.0/jaeger-operator.yaml) as part of the Jaeger setup, which pulls untrusted third‑party content from the open web that can change runtime behavior and thus influence subsequent actions.
The skill fetches instructions or code from an external URL at runtime, and the fetched content directly controls the agent’s prompts or executes code. This dynamic dependency allows the external source to modify the agent’s behavior without any changes to the skill itself.
Potentially malicious external URL detected (high risk: 0.90). The skill instructs fetching and applying a remote Kubernetes manifest at runtime using kubectl from https://github.com/jaegertracing/jaeger-operator/releases/download/v1.51.0/jaeger-operator.yaml, which will execute remote configuration/code on the cluster and is presented as a required setup step.
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