.NET MAUI is a C# and XAML framework for building native mobile and desktop applications from one shared codebase. Developers use it to create apps for Android, iOS, iPadOS, macOS, and Windows. The catalogue entries provide skills, instructions, and agents for working with .NET MAUI.
Borrowing it
Nothing to install: this file belongs to dotnet/maui. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/dotnet/maui/main/.github/skills/learn-from-pr/SKILL.mdgit clone --depth 1 https://github.com/dotnet/mauiWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/dotnet/maui/learn-from-pr)<a href="https://agentmods.dev/skills/dotnet/maui/learn-from-pr"><img src="https://agentmods.dev/badge/skills/dotnet/maui/learn-from-pr/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/dotnet/maui/learn-from-pr"><img src="https://agentmods.dev/badge/skills/dotnet/maui/learn-from-pr.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk warn
- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Agent Snooping · line 264 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00058 | $0.02173 |
| Opus 5.5 | $0.00023 | $0.00869 |
| Sonnet 5.5 | $0.00012 | $0.00435 |
| Haiku 4.5 | $0.00006 | $0.00217 |
Grade A, and why
learn-from-pr scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 277 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Learn From PR
Extracts lessons learned from a completed PR to improve repository documentation and agent capabilities.
Inputs
| Input | Required | Source |
|---|---|---|
| PR number or Issue number | Yes | User provides (e.g., "PR #33352" or "issue 33352") |
Outputs
-
Learning Analysis - Structured markdown with:
- What happened (problem, attempts, solution)
- Fix location analysis (attempted vs actual)
- Failure modes identified
- Prioritized recommendations
-
Actionable Recommendations - Each with:
- Category, Priority, Location, Specific Change, Why It Helps
Completion Criteria
The skill is complete when you have:
- Gathered PR diff and metadata
- Analyzed fix location (attempted vs actual)
- Identified failure modes
- Generated at least one concrete recommendation
- Presented findings to user
When to Use
- After agent failed to find the right fix
- After agent succeeded but took many attempts
- After agent succeeded quickly (to understand what worked)
- When asked "what can we learn from PR #XXXXX?"
When NOT to Use
- Before PR is finalized (use
pr-finalizefirst) - For trivial PRs (typo fixes, simple changes)
- When no agent was involved (nothing to analyze)
Workflow
Step 1: Gather Data
# Required: Get PR info
gh pr view XXXXX --json title,body,files
gh pr diff XXXXX
Analyze the PR to extract learning:
- PR discussion - Comments reveal what was tried
- Commit history - Multiple commits may show iteration
- Code complexity - Non-obvious fixes suggest learning opportunities
- Similar past issues - Search for related bugs
Focus on: "What would have helped an agent find this fix faster?"
Step 2: Fix Location Analysis
Critical question: Did agent attempts target the same files as the final fix?
# Where did final fix go?
gh pr view XXXXX --json files --jq '.files[].path' | grep -v test
| Scenario | Implication |
|---|---|
| Same files | Agent found right location |
| Different files | Major learning opportunity - document why |
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 277 lines · 58 tokens per session scan A a469a3c25050
learn-from-pr is a skill published in the GitHub repository dotnet/maui (23,322 stars, last pushed today), licensed MIT. It adds 58 tokens to every session and 2,173 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
generate-code-cs
Generate the code from typespec for C#. Parameter: C# SDK repository root location .
build-sample-matrix
Build the matrix of all the samples throughout different frameworks.
translate-a-sample
Translate the Azure SDK sample from python to C# and generate markdownn file for the sample. Parameters: C# SDK repository root; python SDK repository root; Package name: one of Azure.AI.Projects, Azure.AI.Projects.Agents or Azure.AI.Extensions.OpenAI; The name of sample file in Python SDK repository.
author-test
Generate a test given sample. Parameters: C# SDK repository root; Package name: one of Azure.AI.Projects, Azure.AI.Projects.Agents or Azure.AI.Extensions.OpenAI; the sample to use as a starting point for the test.
update-changelog
Update the CHANGELOG.md files of C# projects. Parameters: csroot C# SDK repository root.
budget-checker
Validates that a proposed trip fits within the user's stated budget.