.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/evaluate-pr-tests/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/evaluate-pr-tests)<a href="https://agentmods.dev/skills/dotnet/maui/evaluate-pr-tests"><img src="https://agentmods.dev/badge/skills/dotnet/maui/evaluate-pr-tests/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/evaluate-pr-tests"><img src="https://agentmods.dev/badge/skills/dotnet/maui/evaluate-pr-tests.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk warn
- NVIDIA SkillSpector pass
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 | $0.00094 | $0.02890 |
| Opus 5.5 | $0.00038 | $0.01156 |
| Sonnet 5.5 | $0.00019 | $0.00578 |
| Haiku 4.5 | $0.00009 | $0.00289 |
Grade A, and why
evaluate-pr-tests 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 — 330 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Evaluate PR Tests
Evaluates the quality, coverage, and appropriateness of tests added in a PR. Produces a structured report with actionable findings.
When to Use
- ✅ PR has tests and you want to evaluate their quality
- ⚠️ PR has no test files -- output a ❌ Fix Coverage verdict noting no tests were added; skip remaining criteria
- ✅ Reviewing whether tests adequately cover the fix
- ✅ Checking if a lighter test type could be used instead
- ✅ Before merging a PR, as part of review
Quick Start
# Auto-detect PR and base branch
pwsh .github/skills/evaluate-pr-tests/scripts/Gather-TestContext.ps1
# With explicit base branch
pwsh .github/skills/evaluate-pr-tests/scripts/Gather-TestContext.ps1 -BaseBranch "origin/main"
Workflow
Step 1: Gather Automated Context
Run the script to get file categorization, convention checks, and anti-pattern detection:
pwsh .github/skills/evaluate-pr-tests/scripts/Gather-TestContext.ps1
This produces a report at CustomAgentLogsTmp/TestEvaluation/context.md with:
- File categorization (fix files vs test files by type)
- Convention compliance checks (naming, attributes, anti-patterns)
- AutomationId consistency (HostApp ↔ test)
- Existing similar tests
- Platform scope analysis
Step 2: Understand the Fix
Read the fix files to understand:
- What changed — which code paths were modified
- Why it changed — the bug being fixed (from PR description or linked issue)
- Edge cases — what boundary conditions exist in the changed code
Step 3: Evaluate the Tests
Read each test file and evaluate against all criteria below. For each criterion, provide a verdict (✅ Pass, ⚠️ Concern, ❌ Fail) with explanation.
Step 4: Produce the Report
Output a structured evaluation report (see Output Format below).
Evaluation Criteria
1. Fix Coverage
Question: Does the test exercise the actual code paths changed by the fix?
How to check:
- Trace the test's actions through the code to the fix location
- Would the test fail if the fix were reverted?
- Does the test assert on the specific behavior that was broken?
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 330 lines · 94 tokens per session scan A a30612c77a79
evaluate-pr-tests is a skill published in the GitHub repository dotnet/maui (23,322 stars, last pushed today), licensed MIT. It adds 94 tokens to every session and 2,890 once invoked, about $0.0004 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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