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o11y-dev/opentelemetry-skill

Expert OpenTelemetry guidance for collector configuration, pipeline design, and production telemetry instrumentation across Kubernetes, ECS, serverless, and standalone deployments. Use when configuring collectors, designing pipelines, instrumenting applications, implementing sampling, managing cardinality, securing telemetry, writing OTTL transformations, or setting up AI coding agent observability (Claude Code, Codex, Gemini CLI, GitHub Copilot).

97

1.10x
Quality

95%

Does it follow best practices?

Impact

98%

1.10x

Average score across 21 eval scenarios

SecuritybySnyk

Passed

No findings from the security scan

Overview
Quality
Evals
Security
Files

Quality

Content

92%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.

Excellent body structure: a sequenced workflow with explicit validation and fix-retry feedback, highly concrete settings and commands, and a best-practice progressive-disclosure table with all 20 references verified to exist one level deep. The only notable issue is minor: inline version pins (Collector 0.160, SDK 1.44) and some duplicated AI-agent specifics that could be delegated to compatibility.md/ai-agents.md to tighten token efficiency.

Suggestions

Move the inline version pin "tail_sampling is Beta in Collector 0.160" into references/compatibility.md, keeping only a version-neutral stability warning plus a pointer in SKILL.md.

Trim the AI Agent Instrumentation section to the constraints that gate design decisions and delegate the exact env-var and attribute specifics (e.g. gen_ai.tool.name forms) to references/ai-agents.md, which already covers them.

DimensionReasoningScore

Conciseness

The body is dense and assumes competence — no space is spent explaining what OpenTelemetry or collectors are, and the review-mode checklist is terse and operational. However, version-sensitive pins appear inline ("tail_sampling is Beta in Collector 0.160") and the AI-agent section inlines exact env-var/attribute specifics duplicated in ai-agents.md; the rubric penalizes inline version numbers not placed in a compatibility/deprecated section. Not 5: these minor instances could be trimmed or delegated; not 3: there is no padded or redundant explanation, only a couple of tight spots.

4 / 5

Actionability

Guidance is copy-paste ready: an exact command ("otelcol validate --config <path>"), exact env vars ("CLAUDE_CODE_ENABLE_TELEMETRY=1", "OTEL_EXPORTER_OTLP_METRICS_TEMPORALITY_PREFERENCE=cumulative"), exact ports ("OTLP gRPC (4317)"/"4318"), exact config keys ("routing_key: traceID", "clusterIP: None"), and named OTTL/GenAI attribute keys ("gen_ai.operation.name: execute_tool"). Per the instruction-skill scoring note, the absence of full code blocks is not penalized since the guidance is this specific; common cases are covered inline with deep examples delegated. Not 4: there are no gaps — every directive names exact keys, values, or commands.

5 / 5

Workflow Clarity

The 5-step Workflow has an explicit validation checkpoint (step 4: run otelcol validate / exercise a representative request) with a feedback loop ("Fix reported failures and repeat the affected check; if blocked, report the exact failure and remaining verification") and honest delivery criteria (step 5: "Separate configuration validity from observed end-to-end delivery"). Not 4: both the validation step and the fix-retry loop are explicit, matching the anchor-5 pattern including error-recovery and reporting when blocked.

5 / 5

Progressive Disclosure

The body is a lean overview (~68 lines) with a trigger-keyword table mapping every request type to one of 20 one-level-deep reference files; all referenced files verified present in references/ with no second-level nesting. Version-sensitive detail is properly split out to compatibility.md and full configs to production-baseline.md. Not 4: navigation is a single, well-signaled table covering the entire bundle, with no inlined content that belongs in a separate file.

5 / 5

Total

19

/

20

Passed

Description

96%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: concrete multi-action capability statement, an explicit 'Use for' trigger clause, rich natural-language keywords with synonyms and filenames, and a clearly scoped observability niche. The only weakness is minor trigger overlap with generic Kubernetes/Helm and coding-agent requests introduced by broad keywords.

DimensionReasoningScore

Specificity

Four concrete actions are named — "Build OpenTelemetry collector configs", "instrument services", "transform telemetry with OTTL", and "debug missing traces, metrics, or logs" — comprehensively covering config authoring, SDK instrumentation, telemetry transformation, and troubleshooting. Not 4: coverage of the domain's action space is comprehensive rather than having minor gaps; every action is concrete, not generic.

5 / 5

Completeness

The 'what' is explicit ("Build... instrument... transform... debug") and the 'when' is explicit via "Use for OTel/otelcol collector config, OTLP export, SDK instrumentation, sampling...", directly matching the anchor-5 pattern of a capability list followed by concrete trigger phrases. Not 4: the 'when' clause is explicit and specific rather than merely present.

5 / 5

Trigger Term Quality

Natural terms include synonyms ("OTel/otelcol"), file patterns ("Kubernetes/Helm values.yaml"), tool names users would actually say ("Claude Code, Codex, Gemini CLI, GitHub Copilot"), and problem-shaped phrases ("missing traces, metrics, or logs", "collector health and alerts", "cardinality", "TLS/PII controls"). Not 4: few if any natural terms are missing — jargon, synonyms, concrete filenames, and symptom phrasing are all present.

5 / 5

Distinctiveness Conflict Risk

The niche (OpenTelemetry/observability) is clear and most triggers (otelcol, OTLP, OTTL, cardinality, span-to-metric) are unambiguous, but "Kubernetes/Helm values.yaml deployments" and the agent names ("Claude Code, Codex") create minor overlap risk with generic Kubernetes/Helm or coding-agent skills that a user might invoke for non-telemetry work. Not 5: the overlap risk is more than minimal because values.yaml/Helm and agent-name triggers are broad outside the telemetry scoping; not 3: the domain scoping keeps conflicts to closely related skills only.

4 / 5

Total

19

/

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.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Reviewed

Table of Contents