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Use when performance budgets or SLAs exist, users or monitoring report slowness, Core Web Vitals fall below thresholds, a change may have caused a regression, or a feature handles large datasets or high traffic. Measure-first workflow: profile, fix the proven bottleneck, re-measure. Not for speculative optimization.

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。

Profiles native macOS and iOS apps from the command line only: xctrace recording, Time Profiler samples, hotspot ranking, and symbolication with atos and vmmap. Use for headless or SSH-based performance analysis without opening the Instruments GUI.

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。

Produces a concrete plan before a multi-file refactor: scope, sequence, risks, and verification for each step. Use when the user asks to plan, sequence, or safely execute a refactor across several files, before any code changes are made.

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, reviewing, or refactoring production code where reliability, portability, maintainability, explicit contracts, resource safety, and cross-language coding standards matter across Python, TS/JS, C, C++, Rust, or Go.

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。

Profiles macOS and iOS apps with Instruments and xctrace: choosing templates such as Time Profiler, launching binaries with arguments, recording traces, and exporting results. Use when a Mac or iOS app needs CPU, memory, or responsiveness profiling.

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。

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。

Behavioral guidelines to reduce common LLM coding mistakes. Use when writing, reviewing, or refactoring code to avoid overcomplication, make surgical changes, surface assumptions, and define verifiable success criteria.

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。

Conducts a multi-axis code review (correctness, readability, architecture, security, performance) before merging. Use before merging any change, after finishing a feature or bug fix, when reviewing code another agent or person wrote, or when asked to review a diff or pull request.

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 working code feels heavier than it needs to be — deeply nested logic, long functions, unclear names, duplication after a merge, or a review that flagged complexity. Reduces complexity while preserving behavior exactly. Not for code you do not yet understand, or code you are about to rewrite.

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。

Subjects every non-trivial decision to a fresh-context adversarial review before it stands. Use when you want every assumption cross-examined before proceeding, when stress-testing a plan for hidden failure modes, when correctness matters more than speed, when working in unfamiliar code, when stakes are high (production auth, security-sensitive logic, a high-stakes migration, irreversible operations), or any time a confident output would be cheaper to verify now than to debug later.

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。

Guides systematic root-cause debugging. Use when tests fail, builds break, behavior doesn't match expectations, or you encounter any unexpected error. Use when you need a systematic approach to finding and fixing the root cause rather than guessing.

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。

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。

Use when starting a coding session, setting up a project for AI-assisted development, switching between parts of a codebase, or when agent output quality degrades — hallucinated APIs, wrong patterns, ignored project conventions. Structures what the agent loads and when.

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。

Establishes a project's quality bar as a written contract and stops agents quietly lowering it. Interviews the user on which dimensions matter, supplies sane default thresholds when they have no number in mind, records everything in CONSTRAINTS.md, and watches the diff for a weakened bar — new @ts-ignore or eslint-disable suppressions, skipped or deleted tests, assertions stripped out, unimplemented stubs, thresholds edited down. Use when no quality bar is written down, when the user says "set up constraints" or "define our standards", when the user wants dimensions they care about — accessibility, web performance, coverage — set up as enforced constraints, when an agent keeps silencing checks or skipping tests to get to green, when you need a coverage or performance threshold and don't know what number to pick, or when an agent writes more code than anyone will read.

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。

Generate personalized status briefings on demand. Pulls from your configured data sources (GitHub, email, Teams, Slack, and more), synthesizes across them, and drafts updates in your own communication style for any audience you define

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。

Extracts what the user actually wants instead of what they think they should want. Achieves this through one-question-at-a-time interview until ~95% confidence about the underlying intent. Use when an ask is underspecified ("build me X" without "for whom" or "why now"), when the user explicitly invokes ("interview me", "grill me", "are we sure?", "stress-test my thinking"), or when you catch yourself silently filling in ambiguous requirements before any plan, spec, or code exists.

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。

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。

Interactive onboarding that learns your communication style, audiences, and data sources to configure personalized status briefings. Paste in examples of updates you already write, answer a few questions, and roundup calibrates itself to your workflow. Use the first time Roundup is used, or to recalibrate its style and sources.

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。

Refactor given method `${input:methodName}` to reduce its cognitive complexity to `${input:complexityThreshold}` or below, by extracting helper methods. Use when a method exceeds a cognitive-complexity threshold flagged by a linter or review.

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。