Name: distributed-tracing
Rating: 51.2 (1 reviews)
Author: administrakt0r

Blog Docs Log in Get started

distributed-tracing

Implement distributed tracing with Jaeger and Tempo for request flow visibility across microservices.

Quality

57%

Does it follow best practices?

Run evals on this skill

Adds up to 20 points to the overall score

View guide

Securityby

Passed

No findings from the security scan

Fix and improve this skill with Tessl

tessl review fix ./plugins/AI-Agents-Safe-Coding-Skills/skills/distributed-tracing/SKILL.md

The canonical home for this skill is distributed-tracing in administrakt0r/AI-Agents-Safe-Coding-Skills

Quality

Content

53%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.

The content is highly actionable with broad executable examples, but it is verbose, lacks a sequenced workflow with validation checkpoints, and inlines material that should live in reference files that are themselves missing. Tightening structure and splitting into real bundle files would markedly improve it.

Suggestions

Split the deployment manifests and per-language instrumentation into the referenced files and create those files so the in-body references resolve.

Add an explicit numbered workflow with validation checkpoints (e.g., deploy Jaeger -> instrument -> verify traces appear in the UI before proceeding).

Remove the "Distributed Tracing Concepts" definitions and generic Best Practices/Troubleshooting padding that restates what Claude already knows.

Dimension	Reasoning	Score
Conciseness	The body is mostly valuable executable code, but padded sections re-explain concepts Claude already knows (the "Distributed Tracing Concepts" block defining Trace/Span/Context/Tags/Logs, a generic 10-item Best Practices list, and a generic Troubleshooting section).	3 / 5
Actionability	Provides concrete, mostly copy-paste-ready code across Python/Node/Go, k8s and Docker Compose manifests, sampling configs, and trace queries; minor gaps exist (e.g., the Node.js snippet calls trace.getTracer without importing trace, the Go snippet uses an unimported attribute package).	4 / 5
Workflow Clarity	Sections imply a rough sequence (deploy -> instrument -> propagate -> sample -> analyze) but there are no explicit numbered steps or validation checkpoints, and the rubric caps workflow clarity at 3 for batch/deployment operations lacking verification.	3 / 5
Progressive Disclosure	The body inlines ~450 lines of full deployment manifests and multi-language instrumentation that clearly belong in separate reference files, and the referenced files (references/jaeger-setup.md, references/instrumentation.md, assets/jaeger-config.yaml.template, resources/implementation-playbook.md) do not exist in the bundle.	2 / 5
	Total	12 / 20 Passed

Description

61%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.

The description is specific and tool-named but lacks an explicit 'when to use' trigger clause, capping its completeness. It is distinguishable yet would benefit from trigger phrases to reduce overlap with sibling observability skills.

Suggestions

Add an explicit 'Use when...' clause listing natural trigger phrases (e.g., 'Use when debugging latency across services, tracing request paths, or instrumenting microservices with OpenTelemetry').

Expand the action list beyond 'Implement' to concrete capabilities (e.g., 'instrument services, propagate trace context, configure sampling, query traces') to raise specificity.

Include common synonyms and the OpenTelemetry keyword so the description matches the terms users actually say.

Dimension	Reasoning	Score
Specificity	"Implement distributed tracing with Jaeger and Tempo for request flow visibility across microservices" names the domain plus concrete tools and a purpose, but offers only one action verb ("Implement") rather than a comprehensive set of capabilities.	3 / 5
Completeness	It clearly states the "what" but has no "Use when..." clause or equivalent explicit trigger guidance, which per the rubric caps completeness at 3.	3 / 5
Trigger Term Quality	Includes natural terms users say ("distributed tracing", "Jaeger", "Tempo", "microservices", "request flow visibility"), but omits common synonyms like "OpenTelemetry", "trace latency", or "spans".	4 / 5
Distinctiveness Conflict Risk	Naming specific tools (Jaeger, Tempo) carves a clear niche, though it could still overlap with adjacent observability skills (Prometheus, Grafana, SLO) since no trigger phrases bound the scope.	4 / 5
	Total	14 / 20 Passed

Validation

87%

Warnings & errors only

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 14 / 16 Passed

Validation for skill structure

Criteria	Description	Result
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning
referenced_paths_exist	Referenced path issues: 6 missing	Warning

	Total	14 / 16 Passed

Repository: administrakt0r/AI-Agents-Safe-Coding-Skills
Path: plugins/AI-Agents-Safe-Coding-Skills/skills/distributed-tracing/SKILL.md
Commit: 57c135b

Reviewed: 3 days ago

Table of Contents

Discovery Implementation Validation

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.