「engine」の検索結果

Automated enforcement of GDPR Article 5(1)(e) storage limitation principle. Covers TTL-based deletion, retention policy engines, archival workflows, legal hold exemptions, and lifecycle automation. Includes technical implementation patterns for automated data expiry and defensible deletion across distributed systems.

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。

Senior embedded software engineer specializing in firmware and driver development for ARM Cortex-M microcontrollers (Teensy, STM32, nRF52, SAMD).

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。

Android APK reverse engineering: unpacking, Java decompilation, smali modification, repacking and signing, Frida dynamic hooking, and native .so analysis with jadx, apktool, adb, and related tools.

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。

Core UI/UX engineering skill for building highly interactive, spatial, weightless, and glassmorphism-based web interfaces using GSAP and 3D CSS.

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。

Optimize content for AI search and LLM citations across AI Overviews, ChatGPT, Perplexity, Claude, Gemini, and similar systems. Use when improving AI visibility, answer engine optimization, or citation readiness.

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 production-ready LLM applications, advanced RAG systems, and intelligent agents. Implements vector search, multimodal AI, agent orchestration, and enterprise AI integrations.

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。

Rules for when an agent should recall from long-term memory before acting and when it should save decisions, corrections and failures afterwards. Works with any memory backend.

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。

Systematic improvement of existing agents through performance analysis, prompt engineering, and continuous iteration.

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。

Harness patterns for coding agents — memory, permissions, context engineering, delegation, skills, hooks, bootstrap.

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。

Create professional, consultant-grade PowerPoint presentations from scratch using MckEngine (python-pptx wrapper) with McKinsey-style design. Use when user asks to create slides, pitch decks, business presentations, strategy decks, quarterly reviews, board meeting slides, or any professional PPTX. AI calls eng.cover(), eng.donut(), eng.timeline() etc — 67 high-level methods across 12 categories (structure, data, framework, comparison, narrative, timeline, team, charts, images, advanced viz, dashboards, visual storytelling), consistent typography, zero file-corruption issues, BLOCK_ARC native shapes for circular charts (donut, pie, gauge), production-hardened guard rails for spacing, overflow, legend consistency, title style uniformity, dynamic sizing for variable-count layouts, horizontal item overflow protection, chart rendering, and AI-generated cover images via Tencent Hunyuan 2.0 with professional cutout, cool grey-blue tint, and McKinsey-style Bézier ribbon decoration.

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。

Owns repositories, data sources and mapping for Compose and Compose Multiplatform apps: DTO to domain to UiModel boundaries, Ktor clients and bearer auth, WebSocket and SSE, Room, DataStore, Paging 3, offline-first and data-layer tests. Use when writing or reviewing a repository, data source, DTO, mapper, HttpClient, bearer token, WebSocket, SSE, Room DAO, migration, DataStore, PagingSource, RemoteMediator, offline cache or MockEngine test. Do NOT use for ViewModel or UI wiring (compose-feature), composables (compose-ui), the MVI contract or error tiers (compose-architecture), Gradle modules (compose-project), or commonMain vs platform splits (compose-platform).

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。

Web search using curl + multiple search engines (Baidu, Google, Bing, DuckDuckGo). Activates when user asks to search, look up, or query something online. Includes security enhancements: input sanitization, command injection protection, and URL encoding.

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。

Investigate how a concept, code path, application, network flow, system, incident, document, or local artifact actually works, then deliver a two-depth visual HTML explainer with a dead-simple big-picture first layer and source-backed technical depth behind it. Use when the user invokes $xray, asks how something works under the hood, wants a module or app behavior traced across boundaries, requests web, code, network, or safe static artifact research before explanation, or asks for an evidence-backed explainer. Do not use for answer-only definitions, general web design, full codebase audits, or invasive binary reverse engineering without explicit authorization.

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Plan and verify software releases with versioning, changelogs, release branches, feature flags, canaries, migration gates, rollback, deployment checks, and release readiness. Use when preparing a release, shipping a risky PR, coordinating app/backend/database rollout, recovering from a bad deploy, or defining release policy for a repo.

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。

React and Next.js performance playbook distilled from Vercel Engineering guidance. Use when building, reviewing, or refactoring React/Next.js code for waterfalls, bundle size, rendering cost, and client/server data flow.

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。

Route full software-development architecture work from product intent through design, implementation, testing, release, and operations. Use when the user asks for a complete development architecture, wants to know which Spellbook skills to combine, needs an execution path across PRD/spec/API/data/security/performance/release/SRE, or asks to turn an idea or repo into a production-ready engineering plan.

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。

Optimize content for search engines with keyword analysis, readability scoring, meta descriptions, and competitor comparison. Use this when users want to improve SEO, optimize blog posts, or analyze content for search performance.

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。

Research-first content creation optimized for both human readers and AI search engines (Claude, ChatGPT, Perplexity, Gemini). Creates authentic, authoritative content that becomes the go-to citation source for AI models answering user questions. Use this skill when: - Creating content that should appear in AI search results (Perplexity, ChatGPT, Claude) - Building topical authority to become THE source AI cites for a topic - Launching a new product and need compelling, citable content - Creating blog posts, articles, social media, or press releases - Need content that references real trends, people, and recent events - Want AI-assisted content that doesn't sound AI-generated - Creating thought leadership content in any industry Triggers: "create content for", "write about", "research and write", "find experts for", "content for launch", "blog post about", "article on", "press release for", "AI search", "show up in AI", "Perplexity", "be cited by AI"

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。

Applies the reasoning, principles, and frameworks of Andrew Ng (machine learning pioneer, co-founder of Coursera and DeepLearning.AI, Stanford University, and former Google Brain lead). Use this skill whenever the user is navigating AI application development, agentic workflows, automation strategy, AI-native software engineering, or rapid prototyping. Trigger this skill when discussing career advice in the AI era, evaluating AI regulations, structuring machine learning projects, or deciding how to integrate AI into a business. It emphasizes task-based automation, data-centric ML, and driving the cost of proof-of-concepts to zero.

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。

Applies the reasoning style of Daphne Koller (machine learning pioneer, co-founder of Coursera, founder and CEO of Insitro). Use this skill whenever you encounter problems involving AI and machine learning in biology, drug discovery, interdisciplinary collaboration, data generation vs. data mining, or transitioning from academia to industry. Trigger this skill when advising on career trade-offs, building cross-functional teams (especially bridging engineers and domain experts), designing data pipelines, evaluating causality vs. correlation, or applying AI to physical systems ('where bits meet atoms'). Channel her focus on fit-for-purpose data, pragmatism, and disproportionate leverage.

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-aware Word engine. Use to (1) EXTRACT a company's brand from a Word template into a reusable "Brand Profile", (2) COMPREHEND the template with the model (optional), (3) VERIFY it, (4) GENERATE new on-brand .docx documents FROM a saved profile. Trigger on "extract our brand", "learn/match this template", "use our brand kit", "generate a branded report from our profile", or when a ./brand-kit exists. For one-off Word edits with NO saved brand profile, use the docx skill instead. NOT for .pptx (brand-pptx), .xlsx (brand-xlsx), or PDFs.

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-aware Excel engine. Use to (1) EXTRACT a company's brand from a .xlsx template into a reusable "Brand Profile", (2) COMPREHEND the template with the model (optional), (3) VERIFY it, (4) GENERATE a new on-brand .xlsx from a GridDocument fill manifest. Trigger on "extract our brand", "use our workbook template", "generate a branded workbook from our profile", or when a ./brand-kit exists. For one-off spreadsheet edits with no saved brand profile, use the normal xlsx skill instead. NOT for .docx (brand-docx), .pptx (brand-pptx), or PDFs.

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-aware PowerPoint engine. Use to (1) EXTRACT a company's brand from a .pptx template into a reusable "Brand Profile", (2) COMPREHEND the template with the model (optional), (3) VERIFY it, (4) GENERATE a new on-brand .pptx from an IntermediateDocument. Trigger on "extract our brand", "use our deck template", "generate a branded deck from our profile", or when a ./brand-kit exists. For one-off slide edits with no saved brand profile, use the normal pptx skill instead. NOT for .docx (brand-docx), .xlsx (brand-xlsx), or PDFs.

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 or picking up a non-trivial effort (feature, investigation, perf probe, refactor, migration) in a shared repo where teammates or future agents must coordinate or hand off; when a repo has no durable in-tree record of decisions and dead-ends; when you're about to drop a well-measured negative result; or when bootstrapping a journal from a repo's commit history.

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。