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issue-triage

Run open-source triage on Arize-ai/phoenix issues — classify type, check sufficiency, apply component and complexity labels, flag work that already shipped, investigate complex bugs, tidy formatting, then gate against a contributor policy such as "good student issue". Runs incrementally or as a configurable backlog sweep. Use when triaging issues, auditing triage quality, or refining triage criteria.

74

Quality

93%

Does it follow best practices?

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Adds up to 20 points to the overall score

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SecuritybySnyk

Low

Low-risk findings worth noting

SKILL.md
Quality
Evals
Security

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.

A well-structured, highly actionable triage skill with explicit sequencing and validation checkpoints for destructive/batch operations, and appropriately split one-level references. Minor rationale prose is the only thing keeping conciseness from full marks.

Suggestions

Tighten the justifying prose in Stage 2 and Stage 5 (e.g. 'Insufficient issues are the main reason contributor time is wasted, so act on it') to instruction-only phrasing that earns fewer tokens.

Consider moving the 'Run scope' parameter table and slice-sizing detail into a reference file to keep SKILL.md a leaner overview of the seven stages.

DimensionReasoningScore

Conciseness

Largely efficient and assumes Claude's competence — it never explains concepts Claude already knows — but includes some rationale prose ('Insufficient issues are the main reason contributor time is wasted', 'costs tokens, adds public noise, and helps no one') that could be trimmed.

4 / 5

Actionability

Provides copy-paste-ready commands (`gh issue list` with full jq filters, `gh issue view`, `gh issue comment --body-file`) plus findings→action tables and exact label names, covering the common cases concretely.

5 / 5

Workflow Clarity

A 7-stage process is laid out in a sequencing table with explicit validation checkpoints — re-read before editing, one issue at a time, evidence-or-silence, the regression 'Cannot tell → compute then judge' row — and feedback loops for the destructive/batch label-removal operations.

5 / 5

Progressive Disclosure

SKILL.md holds the core workflow while the policy and formatting recipe are split into one-level-deep, clearly signaled references ([references/good-student-issue.md], [references/body-formatting.md]), both of which exist on disk, making navigation easy.

5 / 5

Total

19

/

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, third-person description that states concrete capabilities and an explicit 'Use when' trigger clause. It is highly specific to its niche with negligible conflict risk; only trigger-term breadth keeps it from a perfect score.

DimensionReasoningScore

Specificity

Lists multiple concrete actions — 'classify type, check sufficiency, apply component and complexity labels, flag work that already shipped, investigate complex bugs, tidy formatting, then gate against a contributor policy' — giving comprehensive coverage with no vague padding.

5 / 5

Completeness

Explicitly answers both 'what' (the enumerated triage actions) and 'when' ('Use when triaging issues, auditing triage quality, or refining triage criteria') with concrete trigger phrases.

5 / 5

Trigger Term Quality

'Use when triaging issues, auditing triage quality, or refining triage criteria' gives good natural keyword coverage, but a few common variations (e.g. 'label issues', 'review backlog') are absent.

4 / 5

Distinctiveness Conflict Risk

Scoped to 'Arize-ai/phoenix issues' and a named 'good student issue' gate policy, giving it a clear niche with distinct triggers and minimal overlap with other skills.

5 / 5

Total

19

/

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

metadata_field

'metadata' should map string keys to string values

Warning

Total

15

/

16

Passed

Repository
Arize-ai/phoenix
Reviewed

Table of Contents

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