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phoenix-tracing

OpenInference semantic conventions and instrumentation for Phoenix AI observability. Use when implementing LLM tracing, creating custom spans, or deploying to production.

Quality

85%

Does it follow best practices?

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SecuritybySnyk

Passed

No findings from the security scan

The canonical home for this skill is phoenix-tracing in Arize-ai/phoenix

SKILL.md
Quality
Evals
Security

Quality

Content

78%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 body is an effective, well-organized index that delegates details to 33 real, one-level-deep reference files with strong navigation aids. Its main weaknesses are mild redundancy across the framing sections and a navigation-only workflow whose final steps ('Check Phoenix') are not concrete, with no guidance for verifying traces actually appear.

Suggestions

Make the workflow terminal step concrete, e.g., 'Verify: open the Phoenix UI (or GET /v1/spans) and confirm spans for your app appear' — this adds a validation checkpoint without adding bulk.

Merge or trim overlapping framing sections: the intro sentence duplicates the Reference Categories table, and 'Reading Order' repeats 'Common Workflows'; keep one of each.

Fix the minor count inconsistency: the table says '9 span kinds' while 10 span-* files are listed (span-decision marked experimental) — state '9 stable + 1 experimental' to avoid confusion.

DimensionReasoningScore

Conciseness

The body is a lean index with no explanations of concepts Claude already knows, but contains minor duplication that could be trimmed: the intro sentence ("Contains reference files covering setup, instrumentation, span types, and production deployment") repeats the Reference Categories table, and "When to Apply", "Common Workflows", and "Reading Order" overlap in content. Not 5 because of this redundancy; not 3 because padding is minor and most tokens serve navigation.

4 / 5

Actionability

Concrete guidance via real file links with one-line descriptions, specific bash glob commands (e.g., "references/setup-*"), and named workflow sequences ("setup-{lang} → instrumentation-auto-{lang} → Check Phoenix"). Not 5 because the body contains no executable code itself and steps like "Check Phoenix" are vague; not 3 because navigation is fully concrete — every referenced path was verified to exist with specific per-file descriptions.

4 / 5

Workflow Clarity

"Common Workflows" and "Reading Order" give clear, correctly sequenced navigation paths (setup → instrumentation choice → span types → fundamentals). Not 5 because the terminal step "Check Phoenix" lacks a concrete verification action and no checkpoints tell the reader how to confirm tracing works; not 3 because sequences are explicit, ordered, and unambiguous, and no destructive/batch operations are involved so no cap applies.

4 / 5

Progressive Disclosure

Exemplary one-level-deep structure: all 33 referenced files verified to exist, organized by priority table with prefixes, per-file descriptions, navigation by category prefix and by language, a recommended reading order, and clearly separated external links. Matches the anchor-5 clear-overview pattern with well-signaled references and easy navigation.

5 / 5

Total

17

/

20

Passed

Description

87%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 a concrete statement of what the skill provides with an explicit 'Use when' clause containing realistic trigger phrases. Minor room to improve by adding synonyms like telemetry/OTel and mentioning analysis/feedback capabilities to round out coverage.

Suggestions

Add one or two natural synonyms (e.g., 'telemetry', 'OpenTelemetry', 'monitoring') to the trigger clause so users phrasing the need differently still match.

Consider mentioning trace analysis/feedback (e.g., 'adding annotations to spans') to close the coverage gap with the skill's actual scope.

DimensionReasoningScore

Specificity

Lists several concrete actions — "instrumentation", "implementing LLM tracing", "creating custom spans", "deploying to production" — comparable to the anchor-4 example of several specific actions with minor gaps. Not 5 because coverage has gaps the body itself admits (querying/analyzing trace data, feedback/annotations are not mentioned), and not 3 because more than 1-2 concrete actions are explicitly named.

4 / 5

Completeness

Explicitly answers both: what = "OpenInference semantic conventions and instrumentation for Phoenix AI observability", and when = "Use when implementing LLM tracing, creating custom spans, or deploying to production" with concrete trigger phrases. Matches the anchor-5 example structure (what + explicit 'Use when' with concrete triggers); not 4 because the 'when' clause is already explicit and specific rather than vague or merely implied.

5 / 5

Trigger Term Quality

Contains natural terms users would actually say: "LLM tracing", "custom spans", "AI observability", plus the product name "Phoenix". Not 5 because common synonyms and variations like "telemetry", "OpenTelemetry/OTel", "monitoring", or "spans" alone are missing; not 3 because the key natural phrases (tracing, spans, observability) are present rather than only technical jargon.

4 / 5

Distinctiveness Conflict Risk

"Phoenix AI observability" and "OpenInference" name a clear niche with distinct triggers — only another Phoenix/OpenInference skill could conflict, matching the anchor-5 clear-niche example. Third-person voice is used throughout, so no voice penalty applies.

5 / 5

Total

18

/

20

Passed

Validation

93%

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

Validation — 15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

referenced_paths_exist

Referenced path issues: 6 missing

Warning

Total

15

/

16

Passed

Repository

Arize-ai/phoenix

Path

.agents/skills/phoenix-tracing/SKILL.md

Reviewed

Table of Contents

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