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label-core-bugs

Review open mastra-ai/mastra GitHub issues, identify direct @mastra/core bugs, and apply the @mastra/core label. Use when auditing issues for core ownership, labeling direct core bugs, or periodically reconciling the @mastra/core issue label.

72

Quality

87%

Does it follow best practices?

Run evals on this skill

Adds up to 20 points to the overall score

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SecuritybySnyk

Low

Low-risk findings worth noting

The canonical home for this skill is label-core-bugs in mastra-ai/mastra

SKILL.md
Quality
Evals
Security

Quality

Content

82%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 lean, highly actionable skill body with concrete gh commands, a clearly sequenced workflow, and built-in validation/dry-run safeguards for a batch GitHub-labeling operation. Minor room to tighten prose and make error-recovery loops explicit.

Suggestions

Tighten the explanatory prose around stale-claim detection to reduce token load while keeping the rule.

Add an explicit error-recovery feedback loop for step 5 (e.g., what to do if `gh issue edit` fails or the verification shows the label was not applied).

Consider moving the long include/exclude ownership lists into a reference file to keep the core workflow scannable.

DimensionReasoningScore

Conciseness

The body is dense and largely purposeful with copy-paste gh commands and no concept padding, but a few explanatory sentences (e.g. the stale-claim reasoning) could be trimmed, fitting the efficient-but-minor-over-explanation score-4 anchor rather than the fully-lean score-5.

4 / 5

Actionability

It provides fully executable, copy-paste-ready `gh` commands for every operation (auth, label list/create, issue view, issue edit, verification) covering the common cases, matching the score-5 anchor.

5 / 5

Workflow Clarity

A clear numbered 1-5 sequence with explicit validation checkpoints (verify label exists, dry-run must not mutate, verify the label was applied), but the error-recovery feedback loop is only lightly sketched, so it fits score 4 rather than 5; the destructive/batch cap does not apply because verification steps are present.

4 / 5

Progressive Disclosure

No bundle files exist and the skill is a single self-contained document with well-organized sections (Inputs, Classification, Workflow, Output) and no nested references, giving good structure that fits score 4; at ~60 lines it slightly exceeds the under-50-line simple-skill exception that would justify a 5.

4 / 5

Total

17

/

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 concretely states what the skill does and gives explicit 'Use when' triggers tied to a narrow, distinctive niche. Trigger-term coverage is good but could add a few more natural synonyms.

DimensionReasoningScore

Specificity

Lists multiple concrete actions — 'Review open mastra-ai/mastra GitHub issues, identify direct @mastra/core bugs, and apply the @mastra/core label' — with comprehensive coverage of the task and no gaps, matching the score-5 anchor.

5 / 5

Completeness

It explicitly answers both 'what' (review issues, identify direct core bugs, apply the label) and 'when' with concrete trigger phrases in the 'Use when...' clause, matching the score-5 anchor exactly.

5 / 5

Trigger Term Quality

The 'Use when auditing issues for core ownership, labeling direct core bugs, or periodically reconciling the @mastra/core issue label' clause gives good natural-phrase coverage, but a few common synonyms (e.g. 'triage', 'label issues') are missing, fitting score 4 rather than 5.

4 / 5

Distinctiveness Conflict Risk

The niche is extremely specific — labeling @mastra/core bugs on mastra-ai/mastra issues — with distinct triggers and minimal realistic conflict with other skills, matching the score-5 anchor.

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.

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
mastra-ai/mastra
Reviewed

Table of Contents

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