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issue-triage

Triage a dotnet/machinelearning GitHub issue locally by selecting the best repository label, adding untriaged, identifying the affected ML.NET area, and drafting a useful maintainer comment. Use when asked to triage, classify, label, or respond to an ML.NET issue.

69

Quality

84%

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SecuritybySnyk

Low

Low-risk findings worth noting

SKILL.md
Quality
Evals
Security

Quality

Content

85%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 concise, well-structured instruction-only triage skill with concrete label taxonomy and an explicit pre-write approval checkpoint. It could be strengthened with a sample completed label+comment example and a revision loop.

Suggestions

Add one short worked example showing a final label set and drafted comment for a representative bug issue to make the common case copy-paste ready.

Include a small feedback/retry step for when the user requests changes to the drafted comment or labels before approval.

DimensionReasoningScore

Conciseness

Lean and efficient across ~40 lines; assumes Claude's competence with no padded concept explanations (e.g., no definition of what a GitHub issue or label is), and every line earns its place.

5 / 5

Actionability

Provides concrete, executable guidance (explicit label taxonomy with criteria, exact comment contents to draft, explicit approval gate), but lacks copy-paste-ready examples of an actual label set or comment draft for the common cases.

4 / 5

Workflow Clarity

A clear numbered sequence with a validation checkpoint ('Show the exact labels and comment before any write') and an explicit approval gate before writes; minor gap is the absence of a feedback/retry loop for when the maintainer requests changes to the draft.

4 / 5

Progressive Disclosure

A simple, well-organized skill under 50 lines with no need for external references; sections (Inputs, Labels, Workflow) are cleanly structured and all content is appropriately inline per the simple-skill exception.

5 / 5

Total

18

/

20

Passed

Description

83%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, domain-specific description with explicit trigger guidance and concrete actions. Minor room to broaden natural trigger synonyms, but it cleanly answers both what and when.

DimensionReasoningScore

Specificity

Names the domain and several concrete actions ('selecting the best repository label, adding untriaged, identifying the affected ML.NET area, and drafting a useful maintainer comment'), with only minor coverage gaps (no mention of reading/verifying the issue).

4 / 5

Completeness

Clearly answers 'what' (triage steps) and 'when' with an explicit 'Use when asked to triage, classify, label, or respond to an ML.NET issue' trigger clause.

5 / 5

Trigger Term Quality

Includes natural user-facing terms ('triage, classify, label, or respond to an ML.NET issue') and the repo context, but lacks synonym/extension coverage like 'issue report', 'GitHub issue number', or file-type triggers that a user might naturally say.

4 / 5

Distinctiveness Conflict Risk

Scoped tightly to 'dotnet/machinelearning' triage with distinct triggers, making conflict with other skills minimal.

5 / 5

Total

18

/

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.

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
dotnet/machinelearning
Reviewed

Table of Contents

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