Name: repo-health-check
Rating: 72 (1 reviews)
Author: dotnet

repo-health-check

Run the dotnet/machinelearning repository health check locally, covering issue backlog, pull requests, GitHub Actions, and Azure DevOps pipelines, then draft a dashboard update and prioritized investigations. Use when asked for ML.NET repository health, maintenance status, stale work, or CI trends.

Quality

89%

Does it follow best practices?

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Securityby

Passed

No findings from the security scan

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.

Well-structured, concise content with good progressive disclosure and a clear validated workflow. The main improvement area is actionability, since executable commands live in the referenced playbook rather than the body itself.

Dimension	Reasoning	Score
Conciseness	Lean and efficient with no padding or explanation of concepts Claude already knows; every line earns its place.	5 / 5
Actionability	Concrete guidance with specific state paths, API fallbacks, budgets, and approval gating, though the body delegates the executable commands to the referenced playbook rather than inlining them.	4 / 5
Workflow Clarity	A clear 8-step sequence with an explicit approval checkpoint and a Validation section, satisfying the validation requirement for a batch/mutating skill, though error-recovery feedback loops are not spelled out.	4 / 5
Progressive Disclosure	A clear overview SKILL.md that points one level deep to the real references/playbook.md and the investigate skill via well-signaled links, with content appropriately split.	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, specific description with concrete actions, explicit trigger guidance, and a well-scoped niche. The only minor gap is slightly less-than-comprehensive synonym coverage in the trigger terms.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions — issue backlog, pull requests, GitHub Actions, Azure DevOps pipelines, dashboard update, prioritized investigations — giving comprehensive coverage of what the skill does.	5 / 5
Completeness	Explicitly answers both what (run health check, draft dashboard and investigations) and when ('Use when asked for ML.NET repository health, maintenance status, stale work, or CI trends') with concrete trigger phrases.	5 / 5
Trigger Term Quality	Natural triggers like 'ML.NET repository health, maintenance status, stale work, or CI trends' are phrases users would say, but synonym and extension coverage is not fully comprehensive.	4 / 5
Distinctiveness Conflict Risk	A clear niche scoped to the dotnet/machinelearning repository with distinct ML.NET-specific triggers, giving minimal conflict risk with other skills.	5 / 5
	Total	19 / 20 Passed

Validation

93%

Warnings & errors only

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

Criteria	Description	Result
relative_links	Relative link issues: 1 suspicious	Warning

	Total	15 / 16 Passed

Repository: dotnet/machinelearning
Path: .github/skills/repo-health-check/SKILL.md
Commit: cd2fbd6

Reviewed: 10 days ago

Table of Contents

Discovery Implementation Validation

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