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phoenix-cli

Debug LLM applications using the Phoenix CLI (`px`, from @arizeai/phoenix-cli). Fetch traces, spans, and sessions, annotate them, analyze errors, measure how often a failure or pattern occurs across traces, inspect datasets, review experiments, query annotation configs, and use the GraphQL API. Use whenever the user works with a Phoenix instance from the terminal, or wants to measure or investigate something in Phoenix with the CLI.

75

Quality

94%

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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.

An exceptionally actionable CLI reference: executable commands with flags, exit-code semantics, output shapes, and safety gating for destructive operations, plus strong verification guidance in the setup workflow. The weaknesses are minor — some structural duplication between the Invocation catalog and per-resource sections, and more bulk material (JSON shapes, annotation-config details) could follow filter-expressions.md into reference files.

Suggestions

Trim the Invocation command catalog to the resource/action pattern plus a few examples, since the per-resource sections already enumerate each command with its flags — this removes the largest duplication.

State the --identifier tagging/upsert pattern once (e.g., in the first annotate example) instead of repeating the same inline comment across the trace, span, and session sections.

Move the Trace/Span/Session JSON shape specifications into a references/ file (like filter-expressions.md) and keep only the most-used fields inline, shortening the main SKILL.md.

DimensionReasoningScore

Conciseness

The body is dense and product-specific throughout — commands, flags, exit codes, output shapes — with no padding explaining concepts Claude already knows. It falls at 4 rather than 5 due to trimmable duplication: the 50-command catalog in Invocation repeats commands detailed in per-resource sections, the '--identifier' annotate/add-note comment pattern is restated six times across trace/span/session, and the delete env-var gating is explained globally then re-stated inline twice (lines 356, 410).

4 / 5

Actionability

Fully executable, copy-paste-ready commands with concrete flags, jq pipelines, a curl example, GraphQL queries, and documented output shapes and exit codes. Specific examples cover the common cases (filtering, annotating, piping to jq, extracting project ids), matching the score-5 anchor.

5 / 5

Workflow Clarity

The setup flow is explicitly sequenced with verification checkpoints and feedback loops: check 'tracesVerified' rather than trusting the agent's summary, exit 6 means 'treat that as a failure to report', missing inputs 'exit 3 with exact remediation', and re-runnable slices for recovery. Destructive deletes are gated (env var + confirmation prompt + documented no-op without the gate), so the destructive-operation cap at 3 does not apply; filter conditions also get validate-before-run GraphQL fields.

5 / 5

Progressive Disclosure

The most detail-heavy domain (filter expressions) is appropriately split into a real, well-signaled, one-level-deep references/filter-expressions.md, and sections are clearly navigable. It is 4 rather than 5 because the ~530-line body remains a large single-file reference — the Trace/Span/Session JSON shape specs and annotation-config CRUD details are also bulk material that could be offloaded the same way.

4 / 5

Total

18

/

20

Passed

Description

100%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 exemplary: third-person, concise, and comprehensive, listing concrete capabilities and pairing them with an explicit 'Use whenever...' trigger clause covering both terminal work and investigation/measurement intents. No fluff, no over-claims, minimal conflict risk.

DimensionReasoningScore

Specificity

Lists many specific concrete actions — 'Fetch traces, spans, and sessions, annotate them, analyze errors, measure how often a failure or pattern occurs across traces, inspect datasets, review experiments, query annotation configs, and use the GraphQL API' — giving comprehensive coverage of the CLI's resources, matching the score-5 anchor. It is not 4 because there are no meaningful gaps in action coverage.

5 / 5

Completeness

Explicitly answers both 'what' (the concrete action list) and 'when' ('Use whenever the user works with a Phoenix instance from the terminal, or wants to measure or investigate something in Phoenix with the CLI'). This is a direct match to the score-5 anchor with concrete trigger phrases; the missing-'Use when' cap at 3 does not apply.

5 / 5

Trigger Term Quality

Natural terms a user would say — 'Debug LLM applications', 'traces', 'spans', 'sessions', 'Phoenix instance', 'from the terminal' — with synonym coverage ('measure', 'investigate', 'analyze errors'). This matches the score-5 anchor's comprehensive natural-term coverage including synonyms, not 4, since no common variations are missing.

5 / 5

Distinctiveness Conflict Risk

A clear niche is staked out via 'the Phoenix CLI (`px`, from @arizeai/phoenix-cli)' plus domain-specific triggers (traces, spans, sessions, annotation configs), making conflicts with other skills minimal. Matches the score-5 anchor.

5 / 5

Total

20

/

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

skill_md_line_count

SKILL.md is long (545 lines); consider splitting into references/ and linking

Warning

Total

15

/

16

Passed

Repository

Arize-ai/phoenix

Path

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

Reviewed

Table of Contents

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