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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

76%

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tessl review fix ./plugins/codex/arize-phoenix/skills/phoenix-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.

An exemplary progressive-disclosure index: every reference link is real, one level deep, and reachable via multiple navigation aids (category table, prefixes, globs, reading order). The weaknesses are in the body itself — workflows rely on placeholder paths with no executable quick-start snippet and no validation checkpoints, and the category table duplicates the file listing.

Suggestions

Add a minimal copy-paste quick start to the body (e.g., the pip/npm install command and 3-5 lines registering the tracer for one language) so the most common path is executable without opening a reference file.

Replace placeholder workflow notation like "setup-{lang} → instrumentation-auto-{lang} → Check Phoenix" with concrete per-language steps, and make the verification step explicit (e.g., how to confirm spans appear in the Phoenix UI).

Add a validation/check step to the Production workflow (marked CRITICAL), such as verifying batching is active or masked attributes before rollout, since batch/masking operations currently lack any feedback loop in the body.

Trim duplication between the "Reference Categories" table and the "Quick Reference" section — keep one as the canonical index and reduce the other to bare links.

DimensionReasoningScore

Conciseness

The body is lean and assumes competence (no explanations of tracing or OTel basics), but the "Reference Categories" table substantially duplicates the "Quick Reference" listing, and the intro paragraph ("Contains reference files covering setup, instrumentation, span types, and production deployment") restates the table. This minor trimmable redundancy keeps it below anchor 5, though it is well above anchor 3's "some unnecessary explanation".

4 / 5

Actionability

Navigation is concrete (real file paths, executable `references/setup-*` globs, all 32 links verified to exist), but the workflows use placeholder notation ("setup-{lang} → instrumentation-auto-{lang} → Check Phoenix") and the body contains no copy-paste commands or code for the actual task — "Install arize-phoenix-otel, configure endpoint" describes rather than instructs. Anchor 3's "concrete guidance but incomplete / missing key details" fits; anchor 4 would require mostly executable guidance with only minor gaps.

3 / 5

Workflow Clarity

"Reading Order: 1. Start with setup-{lang} ... 2. Choose instrumentation-auto OR manual ... 3. Reference span-{type} ... 4. See fundamentals-*" gives a clear sequence, but validation checkpoints are absent — "Check Phoenix" is implicit and unexplained, and the production/batch workflows (marked CRITICAL) have no verify step in the body. This matches anchor 3 (sequence present, checkpoints missing) and the rubric's cap for batch workflows lacking validation.

3 / 5

Progressive Disclosure

The body is a pure overview indexing 32 one-level-deep reference files, all of which exist in the bundle, organized by category table, filename prefixes, language globs, and a reading order. This is the anchor-5 pattern: clear overview, well-signaled single-level references, easy navigation.

5 / 5

Total

15

/

20

Passed

Description

78%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 with an explicit and specific 'Use when' clause and a clearly branded niche (Phoenix/OpenInference). Its main weakness is that the 'what' portion is a domain description rather than an enumeration of concrete capabilities, which slightly limits specificity and leaves a few natural trigger synonyms unmentioned.

DimensionReasoningScore

Specificity

"OpenInference semantic conventions and instrumentation for Phoenix AI observability" names the domain, and "implementing LLM tracing, creating custom spans" give 1-2 concrete actions, but the 'what' is a noun-phrase domain descriptor rather than a list of several specific capabilities (querying traces, feedback/annotations, session tracking are uncovered). It does not reach anchor 4's "several specific actions" and is clearly above anchor 2's domain-only naming.

3 / 5

Completeness

It 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. The 'when' clause is explicit and specific, so anchor 4's caveat ("when could be more explicit") does not apply.

5 / 5

Trigger Term Quality

"LLM tracing", "custom spans", "deploying to production", and "Phoenix AI observability" are phrases users would naturally say, giving good keyword coverage. Missing common variations like "telemetry", "OpenTelemetry/OTel", or "monitoring", so it does not reach anchor 5's comprehensive synonym coverage, but it is well above anchor 3's partial coverage.

4 / 5

Distinctiveness Conflict Risk

"Phoenix AI observability" and "OpenInference" establish a clear niche with distinct branded triggers. Minor overlap remains: a user asking only about "LLM tracing" or "custom spans" could also match a generic OpenTelemetry tracing skill, which keeps it just below anchor 5's minimal-conflict bar.

4 / 5

Total

16

/

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

plugins/codex/arize-phoenix/skills/phoenix-tracing/SKILL.md

Reviewed

Table of Contents

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