「engineer」の検索結果

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。

Design and implement Purpose-Based Access Control (PBAC) architecture including purpose ontology definition, policy engine configuration, audit logging of purpose verification at query time, and integration with existing IAM systems. Enforces GDPR Article 5(1)(b) purpose limitation technically.

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。

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.

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。

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。

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。

Applies the engineering and research philosophies of Jeff Dean, Chief Scientist at Google DeepMind and Google Research. Reach for this skill whenever you are designing large-scale distributed systems, optimizing latency and energy efficiency, or making architectural decisions about machine learning infrastructure. It should trigger automatically for topics involving hardware-ML co-design, model distillation, sparse activation, massively multi-task models, or scaling systems by 5x to 10x. Use this skill to evaluate system bottlenecks, transition from specialized to unified models, and optimize experimental velocity. Apply his mental models to avoid premature 100x scaling and to treat AI models as reasoning engines rather than memorization databases.

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.

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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。

Competitive Ad Intelligence. Analyzes competitor advertising strategies including platform identification, landing page analysis, targeting reverse-engineering, positioning gaps, creative approach evaluation, and builds a "beat the competition" strategy with a competitor ad swipe file template.

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 optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React/Next.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.

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。

Apply Andrej Karpathy-inspired coding-agent guidelines in Codex. Use when writing, reviewing, debugging, or refactoring code to surface assumptions, avoid overengineering, keep edits surgical, 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。

Structure and package support escalations for engineering, product, or leadership with full context, reproduction steps, and business impact. Use when an issue needs to go beyond support, when writing an escalation brief, or when assessing whether an issue warrants escalation.

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 Code implementation and refactoring, architecturing or designing systems, process and workflow improvements, error handling and validation. Provide tehniquest to avoid over-engineering and apply iterative improvements.

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。

Turn vague or partially formed AI, computer-science, data-driven, and engineering-method research directions into evidence-grounded, falsifiable, paper-ready Research Idea Briefs through heuristic questioning, staged confirmation, literature positioning, novelty attack, and method-figure planning. Use when the user needs to clarify a research idea, formalize inputs/outputs/objectives/constraints, frame background and motivation, select 3-5 anchor papers, identify a defensible gap, derive 2-3 contributions, stress-test novelty, or design an input-to-output method framework and core-paper figure. Also trigger on requests such as 明确科研 idea、启发式梳理 idea、互动追问科研问题、梳理论文创新点、定义研究任务、找对标论文、把研究方向变成论文方案、画方法框架图. Do not use as the primary workflow for running experiments, exhaustive systematic reviews, drafting full manuscript sections, or polishing finished prose.

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。

UE (user experience engineering) skill for defining end-to-end user experience behavior and interaction logic. Use for tasks like flow design, interaction models, state machines, error recovery, input validation, usability heuristics, accessibility, and turning UX intent into testable acceptance criteria for 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。

generate, refine, validate, and render diagrams from natural language, notes, code snippets, schemas, tables, or existing diagram source. use for flowcharts, swimlanes, sequence diagrams, state diagrams, er diagrams, class diagrams, architecture/c4-style diagrams, dependency graphs, gantt charts, mind maps, user journeys, sankey-style flows, org charts, network graphs, and other visual models. supports mermaid by default, graphviz dot for complex graph layout, plantuml for uml-heavy engineering diagrams, and svg output when direct markup is more reliable.

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 this skill whenever the user asks for an HTML-first visual design deliverable or design judgment: interactive prototype, slide deck, motion demo, infographic, dashboard, landing page, whitepaper, changelog, business card, social cover, brand system, design critique, multi-variant exploration, or export to MP4, GIF, PPTX, PDF, or SVG. It is optimized to make AI agents do the routing, template selection, verification, and export prep so humans spend less time prompt-engineering. Do not use for production web apps, SEO sites, backend systems, or pure copy edits.

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。

An engineering-grade marketing system organized around two surface modes — a deep black canvas for hero and footer chapters and a flat paper-white canvas for body content — connected by a single, almost violently saturated NVIDIA Green accent that carries every CTA, every active tab, and the small decorative corner squares that mark out cards. The system is unapologetically angular: 2px radius across every surface, tight bold sans-serif typography in NVIDIA's proprietary EMEA cut, and a hairline gray rule that separates dense multi-column technical content. There is no decorative gradient, no atmospheric mesh, no soft drop shadow — just black, white, gray, and green stacked into a structured editorial grid that scales from product cards to massive industry landing pages without bending its rules.

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 task-oriented technical documentation with progressive disclosure. Use when writing READMEs, API docs, architecture docs, or markdown documentation. Also use this skill at the END of any completed reverse engineering, penetration testing, CTF, or security analysis task to generate a formal report in the user's project directory. Trigger keywords: 写报告, 写文档, 出报告, writeup, 技术文档, report, documentation.

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。

Routes reverse engineering, exploitation, penetration testing, malware, mobile, firmware, browser automation, documentation, and security tasks to the appropriate specialist skill. Use when a task spans modules or the correct reverse-skill entrypoint is unclear.

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, RAG systems, and intelligent agents. Use when implementing AI features, chatbots, vector search, or agent orchestration.

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。