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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.

63

Quality

74%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

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tessl review fix ./plugins/observability-monitoring/skills/distributed-tracing/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

65%Weight 40%Scale 1-5

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

A well-organized, concise overview that correctly delegates depth to a single reference file. It is held back by light in-body executable guidance and the absence of a sequenced workflow with validation checkpoints for instrumentation and deployment.

Suggestions

Add a short executable instrumentation example (e.g., creating/finishing a span or configuring an OTLP exporter) in the body so the common case does not require loading the reference.

Provide a numbered deploy-and-verify workflow (instrument -> export -> validate traces in Jaeger/Tempo) with an explicit validation checkpoint before considering tracing live.

Collapse the Purpose and When to Use sections into the intro to remove redundancy with the frontmatter description.

DimensionReasoningScore

Conciseness

The body is mostly efficient and assumes Claude's competence, though the Purpose and When to Use sections lightly restate the frontmatter description and could be trimmed.

4 / 5

Actionability

One executable Python snippet (correlated logs) is present, but the bulk of actionable setup/instrumentation code lives only in references/details.md and the best practices are directive rather than executable, leaving key cases uncovered in the body.

3 / 5

Workflow Clarity

No multi-step instrumentation or deployment process is sequenced with validation checkpoints; troubleshooting is a flat checklist without explicit validate-then-proceed feedback loops.

3 / 5

Progressive Disclosure

The body is a clean overview with a single well-signaled one-level reference to references/details.md (a real file), and content is appropriately split for easy navigation.

5 / 5

Total

15

/

20

Passed

Description

83%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A strong description that pairs concrete capabilities with named tooling and an explicit, natural 'Use when' trigger clause. It is mostly comprehensive and distinct, with only minor gaps in trigger synonyms and action coverage.

DimensionReasoningScore

Specificity

Names the domain plus specific tooling (Jaeger, Tempo) and two concrete actions (track requests across microservices, identify performance bottlenecks), with only minor gaps in coverage relative to the comprehensive anchor.

4 / 5

Completeness

Explicitly states what the skill does and follows with a concrete 'Use when debugging microservices, analyzing request flows, or implementing observability' trigger clause.

5 / 5

Trigger Term Quality

Natural user phrases like 'debugging microservices', 'analyzing request flows', and 'implementing observability' are present, though synonyms and explicit file/extension terms are missing.

4 / 5

Distinctiveness Conflict Risk

The Jaeger/Tempo + microservices request-flow niche is clearly distinct from most skills, with only minor overlap risk against general observability or logging skills.

4 / 5

Total

17

/

20

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.

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
wshobson/agents
Reviewed

Table of Contents

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