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Audits SwiftUI performance: rendering, scrolling, CPU and memory, unnecessary view updates, body re-evaluations, and layout thrashing, with Instruments guidance when code review is inconclusive. Use when a SwiftUI screen stutters, scrolls poorly, or uses too much CPU.

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Implements, adopts, and reviews iOS 26+ Liquid Glass in SwiftUI: glassEffect, GlassEffectContainer, correctness, performance, and design fit. Use when adding Liquid Glass to a SwiftUI app or refactoring existing UI to the new design language.

日本語の概要は準備中です。原文の説明を表示しています。

Reviews and fixes Swift concurrency: Swift 6 strict-concurrency compiler errors, Sendable conformance, actor isolation, @MainActor, and data-race safety. Use when migrating async code to Swift 6 or resolving concurrency diagnostics.

日本語の概要は準備中です。原文の説明を表示しています。

Use when turning GitHub issues or bug reports into reproducible coding-agent repair loops with sandboxed execution, focused tests, patch generation, and regression verification.

日本語の概要は準備中です。原文の説明を表示しています。

Use before writing code on anything non-trivial: a new project or feature, ambiguous or incomplete requirements, a change spanning multiple modules, an architectural decision, or work over ~30 minutes. Runs a gated Specify → Plan → Tasks → Implement workflow with human review at each phase. Skip for single-line fixes.

日本語の概要は準備中です。原文の説明を表示しています。

Grounds every implementation decision in official documentation. Use when you want to verify an approach against the official docs before implementing it, or when you want authoritative, source-cited code free from outdated patterns. Use when building with any framework or library where correctness matters.

日本語の概要は準備中です。原文の説明を表示しています。

Use when building anything that accepts user input, implementing authentication or authorization, storing or transmitting sensitive data, integrating external APIs, or adding file uploads, webhooks, or callbacks. Covers input validation, secrets, authz checks, and PII and payment handling.

日本語の概要は準備中です。原文の説明を表示しています。

When user is asking for guidance for which role to assign to an identity given desired permissions, this agent helps them understand the role that will meet the requirements with least privilege access and how to apply that role

日本語の概要は準備中です。原文の説明を表示しています。

Design and plan end-to-end Azure IoT and Smart City solutions: requirements, architecture, security, operations, cost, and a phased delivery plan with concrete implementation artifacts. Use when planning an IoT or Smart City platform on Azure (traffic, lighting, waste, sensors) from requirements to a delivery roadmap.

日本語の概要は準備中です。原文の説明を表示しています。

Creates, configures, and deploys Azure Static Web Apps with the SWA CLI, including local emulation, API backends, auth simulation, and staticwebapp.config.json. Use when hosting a static frontend with optional serverless APIs on Azure or debugging an SWA deployment.

日本語の概要は準備中です。原文の説明を表示しています。

Cloud design patterns for distributed systems architecture covering 42 industry-standard patterns across reliability, performance, messaging, security, and deployment categories. Use when choosing architecture patterns for reliability, performance, messaging, or security in a distributed cloud system.

日本語の概要は準備中です。原文の説明を表示しています。

Build and operate Python Azure IoT Edge modules with robust messaging, deployment manifests, observability, and production readiness checks. Use when writing, deploying, or hardening a Python module for Azure IoT Edge, including telemetry filtering and offline retries.

日本語の概要は準備中です。原文の説明を表示しています。

Updates Azure Verified Module (AVM) references in a Bicep file to their latest versions, applying only non-breaking changes and reporting the result as a table. Use when Bicep templates pin outdated AVM versions or before an infrastructure release.

日本語の概要は準備中です。原文の説明を表示しています。

Use when writing, editing, or reviewing code, READMEs, docs, commits, changelogs, or any repo content. Pre-2020 human style — terse, opinionated, restrained, with project voice. Blocks the LLM tics (emoji headers, "leverage", "robust", "seamless", "delve", defensive bloat, type-hint overkill, marketing prose, "Whether you're X or Y", verbose error messages) catalogued from cpython, requests, flask, git, ripgrep, express, kubernetes, serde, and the Go stdlib. Triggers on "anti-ai", "human-style", "no slop", "write it like a human", or any time code/docs should look hand-written.

日本語の概要は準備中です。原文の説明を表示しています。

Enforce output quality, evidence verification, and quality gates across security audits. Use this skill when you perform SAST, SCA, threat modeling, or security code reviews. Use this skill to verify code locations and taint traces for all findings. Use this skill to prevent the suppression of valid security findings. Use this skill to evaluate reports against quality thresholds (score ≥ 8/10) before delivery.

日本語の概要は準備中です。原文の説明を表示しています。

Comprehensive guide for setting up and configuring CodeQL code scanning via GitHub Actions workflows and the CodeQL CLI. This skill should be used when users need help with code scanning configuration, CodeQL workflow files, CodeQL CLI commands, SARIF output, security analysis setup, or troubleshooting CodeQL analysis.

日本語の概要は準備中です。原文の説明を表示しています。

Performs security-focused differential review of code changes. Adapts analysis depth to codebase size, uses git blame for context, calculates blast radius by counting callers, checks test coverage of modified code, and generates a markdown report. Use when reviewing a PR, commit, or diff for security vulnerabilities, checking whether a change re-introduces a previously fixed bug, asking what else a change could break, or finding which modified code has no test covering it.

日本語の概要は準備中です。原文の説明を表示しています。

Three-layer verification pipeline for AI output. Extracts verifiable claims, finds supporting or contradicting sources via web search, runs adversarial review for hallucination patterns, and produces a structured verification report with source links for human review. Use when AI-generated text with factual claims must be checked before it is published or relied on.

日本語の概要は準備中です。原文の説明を表示しています。

Document analysis with inline source screenshots. When you ask Copilot to analyze a document, Eyeball generates a Word doc where every factual claim includes a highlighted screenshot from the source material so you can verify it with your own eyes.

日本語の概要は準備中です。原文の説明を表示しています。

Find bugs, security vulnerabilities, and code quality issues in local branch changes. Use when asked to review changes, find bugs, security review, or audit code on the current branch.

日本語の概要は準備中です。原文の説明を表示しています。

Deep, evidence-first review of GitHub bugs and PRs: reads the code before forming opinions, finds root causes, decides whether an issue is stale, duplicate, or real, and proposes the best fix. Use when an issue or PR needs real investigation rather than a summary.

日本語の概要は準備中です。原文の説明を表示しています。

Grill the user relentlessly about a plan, decision, or idea. Use when the user wants to stress-test their thinking, or uses any 'grill' trigger phrases.

日本語の概要は準備中です。原文の説明を表示しています。

Use when auditing a CPython C/C++/Cython/Rust extension for free-threaded Python, a `t`-suffixed ABI wheel, GIL opt-out, native race, callback crash, or free-threaded release gate.

日本語の概要は準備中です。原文の説明を表示しています。

Use when receiving code review feedback, before implementing suggestions, especially if feedback seems unclear or technically questionable - requires technical rigor and verification, not performative agreement or blind implementation

日本語の概要は準備中です。原文の説明を表示しています。