machinelearning

Investigate one dotnet/machinelearning repository-health finding locally, gather evidence for an issue, pull request, or pipeline problem, determine root cause confidence, and draft a dashboard report. Use when following up a repo-health finding or investigating a specific ML.NET maintenance risk.

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.

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.

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.

Generates comprehensive, workable unit tests for any programming language using a multi-agent pipeline. Use when asked to generate tests, write unit tests, improve test coverage, add test coverage, create test files, or test a codebase. Supports C#, TypeScript, JavaScript, Python, Go, Rust, Java, and more. Orchestrates research, planning, and implementation phases to produce tests that compile, pass, and follow project conventions.

Analyze recent dotnet/machinelearning MachineLearning-CI failures locally, identify stable failure signatures, deduplicate them against Known Build Error issues, and draft up to three actionable KBE issues. Use when asked to scan ML.NET CI, investigate recurring main-branch failures, or run the former ci-scan agent locally.

Audit the local dotnet/machinelearning ci-scan skill using recent ci-scan issues, maintainer feedback, and an optional local scan report, then draft targeted prompt improvements. Use when reviewing scanner quality, investigating false-positive KBEs, or improving the local ci-scan methodology.