Skill UI

Safely modify live Roblox Studio projects: inspect Explorer and execution locations, reuse modules/remotes, create meaningful Instances, test server/client and devices, verify Output, then clean up debug objects.

Control Android and iOS devices, emulators, and simulators to launch apps, tap, swipe, type, take screenshots, and read accessibility trees. Ideal for mobile UI testing, app debugging, and reproducing phone bugs.

This skill provides a stable standalone fixture for Qoder, allowing users to inspect the active project using standard QwenPaw tools. It is designed for developers to verify project status and structure without external dependencies. Ideal for debugging and project analysis tasks.

This skill helps maintainers verify reported bugs in the mcp-toolbox project. It involves analyzing issue reports, checking versions and configurations, and attempting local reproduction to determine if the bug is real, fixed, or misconfigured. The process requires citing specific evidence like file paths and commit SHAs to support verdicts such as confirmed, already-fixed, or client-side issues. It ensures efficient bug triage without unnecessary infrastructure setup.

MobileBuildMCP CLI is an official utility for iOS, macOS, watchOS, tvOS, and visionOS development. It simplifies build, test, run, debug, log, and UI automation tasks by wrapping native Xcode tools like xcodebuild and simctl. Use this skill to manage mobile project workflows efficiently without memorizing complex command-line arguments.

MobileBuildMCP provides tools for iOS, macOS, watchOS, tvOS, and visionOS development, including building, testing, running, debugging, logging, and UI automation. Use it to streamline Apple platform workflows.

Close defects and incidents with a structured BUG RECEIPT output including VERIFIED, PARTIAL, or BLOCKED status. Establishes evidence boundary, traces root cause, makes responsible changes, and closes the proof loop with explicit checks and gaps.

A skill for mistake-proofing code, interfaces, schemas, and state machines so that misuse cannot be expressed. It helps design type-safe APIs, audit existing code for footguns, and fix recurring bugs by closing entire error classes. Covers concepts like making invalid states unrepresentable, parsing instead of validating, and distinguishing concepts that share a primitive.

Instrument AI agents with tracing, token metrics, latency, cost visibility, and PII redaction for reliability, debugging, and SLO monitoring.

This skill enables debugging and patching failed Agent QA runs using MCP evidence, artifacts, logs, and local code. It ensures defects are not hidden by rewriting tests. The process involves collecting evidence, classifying failures, inspecting local files, applying minimal changes, and verifying fixes through reruns. It supports self-healing of stale definitions while maintaining safety boundaries.

This skill classifies failed Agent QA runs by analyzing recorded evidence, artifacts, and logs. It determines failure categories, confidence levels, and suggests actionable next steps for debugging. It helps identify issues like timeouts, element not found, or infrastructure problems without guessing, ensuring accurate triage before fixing.

Use this skill to deeply inspect and audit the entire process of local AI coding-agent sessions. It tracks critical operational metrics such as token consumption, cost spikes, tool failures, latency anomalies, and overall health scores. Ideal for debugging unreliable code runs, establishing CI/CD health gates, or comparing different attempts to ensure semantic accuracy.