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maui-devflow-session-review

Review MAUI DevFlow sessions to classify friction and generate privacy-scrubbed markdown reports.

209|27|Updated Mar 10, 2026
Search Tags:#product feedback#session review#friction analysis#maui devflow#environment fingerprint#github issue reporting
One-click install
npx skills add https://github.com/dotnet/maui-labs --skill maui-devflow-session-review
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: maui-devflow-session-review
Source: https://github.com/dotnet/maui-labs/tree/main/plugins/dotnet-maui/skills/maui-devflow-session-review
Command: npx skills add https://github.com/dotnet/maui-labs --skill maui-devflow-session-review

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

MAUI DevFlow session reviews are needed to identify where MAUI DevFlow caused friction, repeated failures, missing/failed advertised features, and workarounds—so product teams can improve the tooling without re-running bug fixes during the review.

Core Features & Use Cases

  • Scope-controlled session review: focuses on user-approved session subsets such as a current session, recent sessions, or a platform/feature/date window to avoid broad telemetry mining.
  • Friction classification with evidence: identifies repeatable friction patterns using an explicit rubric and separates confirmed DevFlow friction from app/environment causes and unknowns.
  • Environment fingerprinting and privacy scrub: generates a best-available environment metadata set (platform, CLI, DevFlow package versions, targets) while requiring PII removal before reporting or issue filing.
  • Reporting and optional GitHub issue guidance: prepares a markdown feedback report by default and supports turning findings into targeted dotnet/maui-labs GitHub issues when requested.

Quick Start

Ask for an opt-in review of your recent MAUI DevFlow session(s) focused on your Android UI inspection and connection attempts, and request a markdown feedback report with top friction points and a privacy-scrubbed environment fingerprint.

Frequently Asked Questions about maui-devflow-session-review

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I prepare product feedback from past AI debugging sessions?▼

To prepare product feedback from past AI debugging sessions, review your session history to identify friction, repeated failures, and workarounds, then compile the findings into a markdown report. This process separates confirmed tooling friction from environment causes.

How do I capture environment fingerprint data for a GitHub issue report?▼

Capturing environment fingerprint data for a GitHub issue report involves extracting platform, CLI, and package versions from your session. You must scrub personally identifiable information from this metadata before submitting the issue.

Can I limit a session review to a specific platform or feature window?▼

Yes, you can limit a session review to a specific platform or feature window. Scope-controlled reviewing focuses strictly on user-approved session subsets, such as recent sessions or a specific date range, to avoid broad telemetry mining.

What is the best way to classify friction points from failed debugging attempts?▼

The best way to classify friction points from failed debugging attempts is to apply an explicit rubric against your session history. This identifies repeatable friction patterns and categorizes them as confirmed tooling issues, environment causes, or unknowns.

How do I report advertised features that failed during my development session?▼

To report advertised features that failed during your development session, analyze your session history for failed attempts and resulting workarounds. You can then format this evidence into a markdown feedback report or targeted GitHub issue.

Does the session friction analysis remove personal data before generating reports?▼

Yes, session friction analysis requires privacy scrubbing before generating reports. It removes personally identifiable information from environment fingerprints and session evidence to ensure safe feedback submission.

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