How do I install the CI Scan skill?

Run npx skillmds@latest add dotnet/ci-scan 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 CI Scan 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 CI Scan?

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 CI Scan free to use?

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

Who published CI Scan?

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

CI Scan by dotnet — Agent Skill

Name: CI Scan
Author: .NET (Microsoft)

CI Scan

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.

.NET (Microsoft) 0c72067 3 files · 29.3 KB Updated Sep 17, 2026 4k repo stars

File contents

ML.NET CI scan

Run the ML.NET CI failure scanner from a local Copilot CLI session. This skill replaces scheduled GitHub Actions execution for now.

Inputs

Input	Required	Description
Build ID	No	Specific AzDO build to analyze. Otherwise select the source build using the playbook.
Apply approved drafts	No	Defaults to false. GitHub mutations require explicit approval in the current conversation.

Workflow

Confirm the working repository is dotnet/machinelearning.
Verify local GitHub access with gh auth status. Use anonymous dnceng-public/public AzDO and Helix endpoints from the references.
Create local state under /tmp/mlnet-ci-scan/.
Read references/playbook.md and references/ci-scan.instructions.md.
Follow the playbook once for the selected build window.
Treat every issue-writing instruction as a request to prepare a draft. Do not create issues, add labels, or post comments while analyzing.
Present each proposed issue with its exact title, body, labels, evidence links, and dedup result. Cap the run at three drafts.
Apply drafts only after the user explicitly approves the exact GitHub writes. Never close or modify unrelated issues.

Local substitutions

Store all intermediate state under /tmp/mlnet-ci-scan/.
Replace aw_<id> placeholders with draft-<n> until an issue is created.
Report no-op and skip outcomes directly in chat. Do not create status issues.
Use the authenticated local user rather than COPILOT_GITHUB_TOKEN or a PAT pool.

Validation

Every failure signature has exactly one outcome.
Every draft passes the occurrence, follow-up, specificity, sanitization, match-count, and dedup gates.
The final response includes the full tally table and all skipped reasons.

---name: ci-scandescription: 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.---# ML.NET CI scanRun the ML.NET CI failure scanner from a local Copilot CLI session. This skill replaces scheduled GitHub Actions execution for now.## Inputs| Input | Required | Description ||---|---|---|| Build ID | No | Specific AzDO build to analyze. Otherwise select the source build using the playbook. || Apply approved drafts | No | Defaults to false. GitHub mutations require explicit approval in the current conversation. |## Workflow1. Confirm the working repository is `dotnet/machinelearning`.2. Verify local GitHub access with `gh auth status`. Use anonymous `dnceng-public/public` AzDO and Helix endpoints from the references.3. Create local state under `/tmp/mlnet-ci-scan/`.4. Read [`references/playbook.md`](references/playbook.md) and [`references/ci-scan.instructions.md`](references/ci-scan.instructions.md).5. Follow the playbook once for the selected build window.6. Treat every issue-writing instruction as a request to prepare a draft. Do not create issues, add labels, or post comments while analyzing.7. Present each proposed issue with its exact title, body, labels, evidence links, and dedup result. Cap the run at three drafts.8. Apply drafts only after the user explicitly approves the exact GitHub writes. Never close or modify unrelated issues.## Local substitutions- Store all intermediate state under `/tmp/mlnet-ci-scan/`.- Replace `aw_<id>` placeholders with `draft-<n>` until an issue is created.- Report no-op and skip outcomes directly in chat. Do not create status issues.- Use the authenticated local user rather than `COPILOT_GITHUB_TOKEN` or a PAT pool.## Validation- Every failure signature has exactly one outcome.- Every draft passes the occurrence, follow-up, specificity, sanitization, match-count, and dedup gates.- The final response includes the full tally table and all skipped reasons.

Frequently asked questions