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phoenix-harbor

Configure and interpret the Phoenix plugin for Harbor agent evaluations. Use when adding `arize-phoenix` to Harbor jobs, choosing ATIF tracing, mapping Harbor tasks and rewards to Phoenix experiments, comparing agents or models, resuming jobs, or troubleshooting Harbor records in Phoenix.

73

Quality

92%

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SKILL.md
Quality
Evals
Security

Quality

Content

88%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 strong operational manual: every command is copy-paste ready, the workflow is sequenced with explicit failure, resume, and conflict-handling checkpoints, and the prose is lean with no filler. The two weaker spots are inline time-sensitive version details (conciseness) and the absence of any reference-file split for the dense interpretation material (progressive disclosure).

Suggestions

Concentrate version-sensitive facts (Python 3.12+, Harbor 0.21.0+, Phoenix server 15.0+, ATIF v1.0–v1.7 support and v1.8 exclusions, legacy-version output shape) into a single clearly labeled Requirements/Compatibility section or a short reference file, so the operational sections stay stable as versions change.

Consider moving the detailed ATIF trace material (span hierarchy, converter version support, reconstruction and matching rules) into a one-level-deep references/ file (e.g., references/atif-traces.md) linked from a brief "Interpret ATIF traces" summary, keeping SKILL.md as a lean overview.

Tighten dense edge-case passages such as the output-extraction rules in "Predict the Phoenix records" by consolidating repeated "empty output" conditions into a compact list, reducing token cost without losing information.

DimensionReasoningScore

Conciseness

The body is terse and imperative with no padding or explanation of concepts Claude already knows, but it inlines time-sensitive version information ("Python 3.12 or newer", "Harbor 0.21.0 or newer", "Phoenix server 15.0 or newer", "ATIF v1.0 through v1.7... v1.8 audio fields are unsupported", legacy-version output notes) outside any requirements/deprecation-focused section, and some dense edge-case passages could be trimmed. This fits anchor 4 (efficient, minor trimmable instances) rather than 3 or 5.

4 / 5

Actionability

Fully executable, copy-paste-ready commands throughout: `uv pip install "arize-phoenix-client[harbor]"`, `export PHOENIX_COLLECTOR_ENDPOINT=...`, a complete `harbor run ... --plugin arize-phoenix` invocation, `--plugin-kwarg trace_mode=null`, `dataset=<name>`, template fields for experiment names, and a concrete `task.toml` snippet; interpretation guidance is equally concrete (exact evaluation names like `<step_name>.<reward_key>`, `infra_ok` semantics, a numbered comparison procedure, the span tree). Matches anchor 5.

5 / 5

Workflow Clarity

Sections follow the operational lifecycle (requirements, install, trace mode, destination, add plugin, record mapping, naming, interpretation, failures/resume, boundaries) with explicit checkpoints and error-recovery loops: setup failures stop the job before trials, "Stop and explain the constraint if the job has any unsupported source shape" includes an enumerated checklist, per-stage failure semantics are given, and Phoenix conflicts require resolution rather than dismissal. This is not a destructive/batch operation skill, so the cap-3 rule does not apply; the recovery guidance matches anchor 5.

5 / 5

Progressive Disclosure

No bundle files exist, so this is a single-file skill judged on structure alone: headers are clear, purposeful, and ordered, with no dead-weight bulk. However, it is a ~240-line monolith with no one-level-deep references at all — detailed material such as the ATIF span hierarchy, converter version support, and reference-output wrapping rules is inlined. Anchor 4 (good structure, minor organization gaps) fits better than 3 (content that should be separate is inline with poor signaling) and better than 5 (well-signaled references, which are absent by design).

4 / 5

Total

18

/

20

Passed

Description

92%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: it states the domain and concrete capabilities in third person, then gives an explicit multi-trigger "Use when" clause enumerating the natural use cases. The only gap is modest keyword/synonym coverage (traces, runs, scores), which keeps trigger term quality at 4.

DimensionReasoningScore

Specificity

"Configure and interpret the Phoenix plugin for Harbor agent evaluations" plus enumerated concrete actions ("adding `arize-phoenix` to Harbor jobs", "choosing ATIF tracing", "mapping Harbor tasks and rewards to Phoenix experiments", "comparing agents or models", "resuming jobs", "troubleshooting Harbor records") covers the domain comprehensively, matching the anchor-5 pattern rather than the minor-gaps level of 4.

5 / 5

Completeness

It explicitly answers both: "what" ("Configure and interpret the Phoenix plugin for Harbor agent evaluations") and "when" (an enumerated "Use when adding..., choosing..., mapping..., comparing..., resuming..., or troubleshooting..." clause with concrete trigger phrases), exactly matching the anchor-5 example.

5 / 5

Trigger Term Quality

Natural phrases a user would say are present ("adding `arize-phoenix` to Harbor jobs", "comparing agents or models", "resuming jobs", "troubleshooting"), but a few natural terms and synonyms are missing (e.g., traces, runs, scores, viewing/analyzing results). This matches anchor 4 ("good keyword coverage; a few natural terms missing") rather than 5's demand for synonyms and variations.

4 / 5

Distinctiveness Conflict Risk

The description occupies a clear niche (Phoenix plugin for Harbor agent evaluations with ATIF tracing) and triggers on distinctive terms (`arize-phoenix`, Harbor jobs, ATIF, Phoenix experiments), so overlap with generic evaluation, observability, or CI skills is minimal — matching anchor 5.

5 / 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.

Repository

Arize-ai/phoenix

Path

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

Reviewed

Table of Contents

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