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dotnet
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learn-from-pr

Analyze completed agent-driven pull requests to extract lessons and recommendations.

23.3k|2.0k|Updated May 8, 2020
Search Tags:#documentation#learning#agent#patterns#pr#analyze#learn-from-pr
One-click install
npx skills add https://github.com/dotnet/maui --skill learn-from-pr
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: learn-from-pr
Source: https://github.com/dotnet/maui/tree/main/.github/skills/learn-from-pr
Command: npx skills add https://github.com/dotnet/maui --skill learn-from-pr

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill analyzes completed pull requests involving an automated agent to identify patterns, failure modes, and actionable lessons to improve future automation, documentation, and instruction files.

Core Features & Use Cases

  • Learning analysis: Generates a structured markdown report detailing what happened, how it was handled, failure modes, and opportunities for improvement.
  • Actionable recommendations: Produces prioritized recommendations for instructions, skills, and documentation to prevent similar issues.
  • Use Case: After a PR with agent involvement, run this skill to extract learnings and implement improvements across tools and guides.

Quick Start

Provide the PR number (and optional session markdown path) to generate a Learning Analysis and a set of actionable recommendations.

Frequently Asked Questions about learn-from-pr

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

FAQPage Schema
How do I extract lessons learned from an automated agent's pull request?▼

To extract lessons learned from an automated agent's pull request, analyze the completed PR metadata and diffs to identify behavioral patterns and failure modes. This generates a structured Learning Analysis report detailing what happened and opportunities for future workflow improvement.

What is the best way to improve agent performance based on previous PR workflows?▼

The best way to improve agent performance based on previous PR workflows is to analyze completed agent-driven PRs for failure modes and generate prioritized recommendations. These actionable recommendations are then applied directly to update instructions, skills, and documentation.

Can I analyze agent behavior in a PR using only the PR number?▼

Yes, you can analyze agent behavior in a PR using only the PR number. The analysis gathers PR metadata and diffs directly from the PR to evaluate agent behavior, though providing an optional session markdown path adds deeper context for the Learning Analysis.

How do I generate actionable recommendations after an automated agent fails in a PR?▼

To generate actionable recommendations after an automated agent fails in a PR, analyze the PR diff and metadata to identify failure modes. The analysis outputs a structured Learning Analysis and prioritized recommendations to prevent similar issues in future automation workflows.

Does analyzing a PR for agent lessons require session markdown files?▼

Analyzing a PR for agent lessons does not require session markdown files. The analysis gathers PR metadata and diffs directly to produce a structured Learning Analysis, but optionally accepting a session markdown path provides deeper behavioral insights.

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