How do I install the Repo Health Check skill?

Run npx skillmds@latest add dotnet/repo-health-check in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Repo Health Check safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Repo Health Check?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Repo Health Check free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Repo Health Check?

.NET (Microsoft) (@dotnet) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Repo Health Check by dotnet — Agent Skill

Name: Repo Health Check
Author: .NET (Microsoft)

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.

.NET (Microsoft) Updated Sep 17, 2026 4k repo stars

File contents

ML.NET repository health check

Collect and analyze repository-health data from a local Copilot CLI session. This skill replaces the scheduled orchestrator for now.

Workflow

Confirm local GitHub access and the dotnet/machinelearning repository.
Create state under /tmp/mlnet-repo-health/.
Read references/playbook.md and follow its data collection, fingerprinting, severity, and dashboard-format rules.
Prefer available authenticated GitHub and Azure DevOps tools. If those are unavailable, use the public dnceng-public/public REST API; skip AzDO checks only when both access paths fail, and state the coverage gap.
Treat dashboard creation, body updates, comments, and investigation requests as drafts during analysis.
For up to five critical or high-confidence warning findings, either:
- investigate inline by following ../repo-health-investigate/SKILL.md, or
- produce a prioritized investigation queue when the user requested check-only mode.
Present the proposed dashboard body, daily delta, and any investigation reports.
Create or update the dashboard and post comments only after explicit approval for the exact writes.

Local substitutions

Store local state under /tmp/mlnet-repo-health/.
Perform investigations locally rather than dispatching a workflow.
Use the authenticated local user instead of workflow tokens.
Preserve the one-dashboard-update, one-delta-comment, and five-investigation budgets.

Validation

Every finding has a deterministic fingerprint and status.
Baselined findings are not marked new.
Required dashboard sections remain present.
No source issue or PR is modified.

---name: repo-health-checkdescription: 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.---# ML.NET repository health checkCollect and analyze repository-health data from a local Copilot CLI session. This skill replaces the scheduled orchestrator for now.## Workflow1. Confirm local GitHub access and the `dotnet/machinelearning` repository.2. Create state under `/tmp/mlnet-repo-health/`.3. Read [`references/playbook.md`](references/playbook.md) and follow its data collection, fingerprinting, severity, and dashboard-format rules.4. Prefer available authenticated GitHub and Azure DevOps tools. If those are unavailable, use the public `dnceng-public/public` REST API; skip AzDO checks only when both access paths fail, and state the coverage gap.5. Treat dashboard creation, body updates, comments, and investigation requests as drafts during analysis.6. For up to five critical or high-confidence warning findings, either: - investigate inline by following [`../repo-health-investigate/SKILL.md`](../repo-health-investigate/SKILL.md), or - produce a prioritized investigation queue when the user requested check-only mode.7. Present the proposed dashboard body, daily delta, and any investigation reports.8. Create or update the dashboard and post comments only after explicit approval for the exact writes.## Local substitutions- Store local state under `/tmp/mlnet-repo-health/`.- Perform investigations locally rather than dispatching a workflow.- Use the authenticated local user instead of workflow tokens.- Preserve the one-dashboard-update, one-delta-comment, and five-investigation budgets.## Validation- Every finding has a deterministic fingerprint and status.- Baselined findings are not marked new.- Required dashboard sections remain present.- No source issue or PR is modified.

Frequently asked questions