1.5万 件 ・ 関連度順
概要と使いどころ
Systematic debugging: production-level reproduce, isolate, instrument, verify. Use when implementing, reviewing or testing systematic debugging for React, Vite, Next.js or Perfect_AI projects.
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。
Semantic design tokens: production-level roles, themes and semantic states. Use when implementing, reviewing or testing semantic design tokens for React, Vite, Next.js or Perfect_AI projects.
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。
Drag and spring: production-level createDraggable, bounds, friction and spring. Use when implementing, reviewing or testing drag and spring for React, Vite, Next.js or Perfect_AI projects.
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。
Color science: production-level linear sRGB, Oklab, OKLCH gamut mapping. Use when implementing, reviewing or testing color science for React, Vite, Next.js or Perfect_AI projects.
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。
Premium monochrome design: production-level neutral ramp, sheen, materials and lighting. Use when implementing, reviewing or testing premium monochrome design for React, Vite, Next.js or Perfect_AI projects.
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。
Color selection engine: production-level product intent -> palette -> contrast -> tokens. Use when implementing, reviewing or testing color selection engine for React, Vite, Next.js or Perfect_AI projects.
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。
Stagger motion: production-level grid/time/value stagger with deterministic ordering. Use when implementing, reviewing or testing stagger motion for React, Vite, Next.js or Perfect_AI projects.
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。
Brand palette reasoning: production-level category, trust, culture, hierarchy. Use when implementing, reviewing or testing brand palette reasoning for React, Vite, Next.js or Perfect_AI projects.
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。
Color vision and contrast: production-level WCAG contrast on composited layers. Use when implementing, reviewing or testing color vision and contrast for React, Vite, Next.js or Perfect_AI projects.
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。
WAAPI and compositing: production-level GPU-friendly transform/opacity, offscreen pausing. Use when implementing, reviewing or testing waapi and compositing for React, Vite, Next.js or Perfect_AI projects.
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。
Typographic sequences: production-level splitText, words/chars, reflow and RTL. Use when implementing, reviewing or testing typographic sequences for React, Vite, Next.js or Perfect_AI projects.
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。
Easing mathematics: production-level cubic bezier, damped spring, velocity continuity. Use when implementing, reviewing or testing easing mathematics for React, Vite, Next.js or Perfect_AI projects.
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。
Anime.js v4 core animations: production-level target selection, property keyframes, duration, ease, loop, play/pause. Use when implementing, reviewing or testing anime.js v4 core animations for React, Vite, Next.js or Perfect_AI projects.
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。
Scroll-driven choreography: production-level sticky progress, sync, enter/leave, bidirectional timeline. Use when implementing, reviewing or testing scroll-driven choreography for React, Vite, Next.js or Perfect_AI projects.
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。
Accessibility automation: production-level axe audits and manual keyboard checks. Use when implementing, reviewing or testing accessibility automation for React, Vite, Next.js or Perfect_AI projects.
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。
Responsive scope: production-level createScope lifecycle, media query gating, cleanup. Use when implementing, reviewing or testing responsive scope for React, Vite, Next.js or Perfect_AI projects.
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。
Layout changes: production-level FLIP layout, additive composition, measurement. Use when implementing, reviewing or testing layout changes for React, Vite, Next.js or Perfect_AI projects.
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。
Rewrite AI-sounding text so it reads like the writer without changing what it says. Use when editing or reviewing prose for AI tells: not-X-but-Y contrasts, one-line closers, staged openers, forced triads, dashes everywhere, inflated claims, sales language, stock AI words, bold labels, or filler. Based on Wikipedia's "Signs of AI writing."
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。
Manual-only orchestrator for medium-to-large software development. Turns a broad goal into PR-sized and operational nodes with verification gates, then coordinates approved execution across follow-up prompts. Activate only when the user explicitly invokes my-epic; once activated, keep orchestrating that epic for the rest of the session. Do NOT use for an uninvoked planning or implementation request, a single small bug fix, one obvious PR, pure code review, ordinary SDD work that fits in one spec/PR, or implementing an epic during the same first call that created 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。
Delegate independent research, implementation, or verification in parallel when it can save time after dispatch and review overhead, or when explicitly requested. Keep decisions and final integration in the main agent.
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。
Disciplined diagnosis workflow for hard, intermittent, or multi-component bugs and performance regressions: inspect logs and existing evidence, build a feedback loop, test falsifiable hypotheses, instrument gaps, and verify the outcome. Use when the root cause is unclear, reproduction is unreliable, several causes or components are plausible, or performance has regressed. Do NOT use for obvious single-cause errors, straightforward configuration or syntax fixes, planned feature work, or broad architecture review. Diagnosis-only requests stop after reporting the root cause and evidence; apply a fix only when the user requested one.
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。
Detects insider data exfiltration by analyzing DLP policy violations, file access patterns, upload volume anomalies, and off-hours activity in endpoint and cloud logs. Uses pandas for behavioral analytics and statistical baselines. Use when investigating insider threats or building user behavior analytics for data loss prevention.
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。
Detect and prevent QR code phishing (quishing) attacks that bypass traditional email security by embedding malicious URLs in QR code images within emails.
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。
Detects and prevents code injection attacks targeting serverless functions (AWS Lambda, Azure Functions, Google Cloud Functions) through event source poisoning, malicious layer injection, runtime command execution, and IAM privilege escalation via function modification. The analyst combines static analysis of function code, CloudTrail event correlation, runtime behavior monitoring, and IAM policy auditing to identify injection vectors across the expanded serverless attack surface including API Gateway, S3, SQS, DynamoDB Streams, and CloudWatch event triggers. Activates for requests involving Lambda security assessment, serverless injection detection, function event poisoning analysis, or serverless privilege escalation investigation.
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。