.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/agents/learn-from-pr.agent.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/agents/dotnet/maui/learn-from-pr)<a href="https://agentmods.dev/agents/dotnet/maui/learn-from-pr"><img src="https://agentmods.dev/badge/agents/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/agents/dotnet/maui/learn-from-pr"><img src="https://agentmods.dev/badge/agents/dotnet/maui/learn-from-pr.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00025 | $0.00719 |
| Opus 5.5 | $0.00010 | $0.00288 |
| Sonnet 5.5 | $0.00005 | $0.00144 |
| Haiku 4.5 | $0.00003 | $0.00072 |
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 — 113 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Learn From PR Agent
Extracts lessons from completed PRs and applies improvements to the repository.
When to Invoke
- "Learn from PR #XXXXX and apply improvements"
- "Update the repo based on what we learned from PR #XXXXX"
- After any PR with agent involvement (failed, slow success, or quick success)
When NOT to Invoke
- For analysis only without applying changes → use
/learn-from-prskill - Before PR is finalized
- For trivial PRs with no learning value
Workflow
Phase 1: Analysis
Run the /learn-from-pr skill workflow (Steps 1-6) to generate recommendations.
The skill covers three outcome types:
- Agent failed - What was missing that caused wrong attempts
- Agent succeeded slowly - What would have gotten to solution faster
- Agent succeeded quickly - What patterns to reinforce
Phase 2: Apply Changes
For each High or Medium priority recommendation:
| Category | Action |
|---|---|
| Instruction file | Edit existing or create new .github/instructions/*.instructions.md |
| Skill enhancement | Edit .github/skills/*/SKILL.md |
| Architecture doc | Edit /docs/design/*.md (detailed) or create quick-reference in .github/architecture/ |
| General AI guidance | Edit .github/copilot-instructions.md |
| Code comment | Add comment to source file (don't modify behavior) |
Before each edit:
- Read the target file first
- Check for existing similar content (don't duplicate)
- Match the existing style/format
- Find the appropriate section
Skip applying if:
- Content already exists
- Recommendation is too vague
- Would require major restructuring
Phase 2.5: Verify Changes
After applying changes:
- Run
git diffto review all edits - Verify no syntax errors in modified files (valid markdown)
- Confirm style matches existing content
- If issues found, fix or revert before reporting
Phase 3: Report
Present a summary:
## Changes Applied
| File | Change |
|------|--------|
| [path] | [what was added/modified] |
## Not Applied
| Recommendation | Reason |
|----------------|--------|
| [rec] | [why skipped] |
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 · 113 lines · 25 tokens per session scan A 67c42ad5d5ad
learn-from-pr is an agent published in the GitHub repository dotnet/maui (23,322 stars, last pushed today), licensed MIT. It adds 25 tokens to every session and 719 once invoked, about $0.0001 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.
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