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repo-health-groom

Groom the dotnet/machinelearning repo-health dashboard locally by linking investigation results, marking resolved findings, archiving stale entries, and drafting conservative comment minimization. Use when asked to clean, update, or maintain the ML.NET health dashboard.

72

Quality

89%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

SKILL.md
Quality
Evals
Security

Quality

Content

86%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 tight, well-structured instruction skill that uses progressive disclosure effectively and includes validation checkpoints for a destructive batch operation. The main gap is that executable command details are delegated to the reference rather than shown inline.

Suggestions

Inline one representative gh command (e.g. the issue-list lookup) so the core flow is copy-paste runnable without opening the playbook.

Add an explicit fix-and-revalidate feedback loop for the 80-percent length floor check instead of only aborting on failure.

Briefly note what to do when no 'repo-health' issue exists so the workflow's terminal case is visible in the body.

DimensionReasoningScore

Conciseness

The ~26-line body is lean and assumes Claude's competence, explaining no background concepts and keeping every line load-bearing.

5 / 5

Actionability

Steps carry concrete specifics (80 percent length floor, 50-comment budget, the 'repo-health' label), but the executable gh commands live in the referenced playbook rather than inline, leaving minor gaps.

4 / 5

Workflow Clarity

A clear seven-step sequence with explicit validation (enforce length floor and sections), a pre-mutation diff review, and an approval gate; it aborts on validation failure rather than offering a fix-and-retry feedback loop, which keeps it just below a 5.

4 / 5

Progressive Disclosure

SKILL.md is a concise overview with a single well-signaled one-level-deep reference to references/playbook.md (verified to exist), splitting detailed methodology appropriately.

5 / 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, well-scoped description that states concrete capabilities and an explicit trigger clause for a narrow domain. Only trigger-term synonym coverage leaves minor room for improvement.

DimensionReasoningScore

Specificity

Lists four concrete actions — 'linking investigation results, marking resolved findings, archiving stale entries, and drafting conservative comment minimization' — giving comprehensive coverage of the skill's capabilities rather than generic verbs.

5 / 5

Completeness

Explicitly answers both what (the four grooming actions) and when ('Use when asked to clean, update, or maintain the ML.NET health dashboard') with concrete trigger phrases.

5 / 5

Trigger Term Quality

Natural trigger phrases 'clean, update, or maintain the ML.NET health dashboard' map well to what a user would say, though a few synonyms (e.g. tidy, review, groom) are not exhaustively covered.

4 / 5

Distinctiveness Conflict Risk

Scoped to the 'dotnet/machinelearning repo-health dashboard' niche with repo-specific triggers, making conflict with unrelated skills minimal.

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
dotnet/machinelearning
Reviewed

Table of Contents

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