「zat」の検索結果

Route BERTopic topic modeling, embedding, vectorizer, labeling, visualization, and serialization workflows.

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 AIMET install, PyTorch/ONNX quantization, GenAILab, model access, cluster/Pod, Qualcomm SDK, optimization, export, and repository tasks to focused operating guidance.

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。

Insecure deserialization hunting across Java / .NET / PHP / Python / Ruby / Node. Gadget-chain RCE, OOB-gated blind detection, magic-byte fingerprinting. Wiki-first, FIND schema output.

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。

Implementing server-side tracking with privacy controls using Google Tag Manager server containers. Covers first-party data collection, IP anonymization, consent-aware event forwarding, and reducing client-side third-party cookie exposure.

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 executive reporting and visualization from RoPA data including processing activity counts, risk heatmaps, compliance scores, trend analysis, and supervisory authority readiness indicators. Activate for RoPA dashboard, executive reporting, risk heatmap, compliance score, trend analysis, board reporting, KPI.

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。

Implements the GDPR Article 17 right to erasure (right to be forgotten) workflow, covering all six grounds for erasure, five exceptions, technical deletion versus anonymization decisions, and third-party notification under Article 19. Activate for erasure request, deletion request, right to be forgotten, Art. 17 queries.

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 methodology for auditing website cookies and tracking technologies. Covers automated scanning, cookie categorization, lifecycle documentation, and compliance gap analysis referencing the Planet49 CJEU ruling (C-673/17).

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。

Complete App Store Optimization (ASO) toolkit for researching, optimizing, and tracking mobile app performance on Apple App Store and Google Play Store

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 end-to-end application performance with profiling, observability, and backend/frontend tuning. Use when coordinating performance optimization across the stack.

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。

Actorization converts existing software into reusable serverless applications compatible with the Apify platform. Actors are programs packaged as Docker images that accept well-defined JSON input, perform an action, and optionally produce structured JSON output.

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 the user needs product management workflows such as RICE prioritization, customer interview analysis, PRD templates, discovery frameworks, go-to-market strategy, feature prioritization, research synthesis, or requirements 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。

Create diagrams and visualizations using Mermaid syntax. Use when generating flowcharts, sequence diagrams, class diagrams, entity-relationship diagrams, Gantt charts, or any visual documentation. Triggers on Mermaid, flowchart, sequence diagram, class diagram, ER diagram, Gantt chart, diagram, visualization.

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。

Master prioritization frameworks, roadmap planning, timeline estimation, and resource allocation. Create executable roadmaps that drive focus and alignment.

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 ranking backlogs, deciding what to do first based on effort vs impact (quick wins vs big bets), prioritizing feature roadmaps, triaging bugs or technical debt, allocating resources across initiatives, identifying low-hanging fruit, evaluating strategic options with 2x2 matrix, or when user mentions prioritization, quick wins, effort-impact matrix, high-impact low-effort, big bets, or asks "what should we do first?".

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。

Synthesize user feedback from multiple channels and identify patterns to inform product decisions. Use when analyzing feedback, prioritizing feature requests, conducting NPS surveys, or understanding user sentiment. Covers feedback collection, categorization, prioritization frameworks, and closing the feedback loop.

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 optimize pricing strategies for SaaS products including tiering, packaging, value metrics, and experimentation. Use when setting initial pricing, optimizing conversion, expanding revenue, or when users ask about pricing strategy, monetization, or revenue 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。

Analyze user conversion funnels, calculate step-by-step conversion rates, create interactive visualizations, and identify optimization opportunities. Use when working with multi-step user journey data, conversion analysis, or when user mentions funnels, conversion rates, or user flow 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。

Applies the mental models and frameworks of Andrej Karpathy (deep learning, former Director of AI at Tesla, founding member of OpenAI, Eureka Labs). Use this skill whenever you are helping the user build neural networks from scratch, debug deep learning pipelines, evaluate AI agent workflows, design LLM apps, or navigate the transition to Software 3.0 (vibe coding). It is highly relevant for pedagogy (untangling complex knowledge), assessing AI capabilities vs. limitations (jagged intelligence, tokenization limits), and architectural decisions (end-to-end optimization vs. complex pipelines). Reach for this whenever discussing LLM training, autonomous systems, or AI-assisted coding.

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。

Ad Performance Auditor — analyze existing ad campaigns and identify optimization opportunities

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。

Debug ClickHouse deserialization errors in Rust caused by column ORDER mismatch when using SELECT alias.* on a VIEW that adds computed columns. Use when: (1) Some query variants (e.g. sort=trending) return 500 but others (sort=recent) succeed, (2) Queries use SELECT view_alias.*, extra_cols FROM view JOIN ..., (3) The Rust Row struct has the right number and types of fields but deserialization fails anyway (wrong type error, not "not enough data"), (4) The VIEW was recently extended with a computed column (e.g. trending_score).

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。

Profile programs at the function/method level to identify performance hotspots, bottlenecks, and optimization opportunities. Records execution time, memory usage, and call frequency for each interval. Generates actionable recommendations and visualizations. Use when users need to (1) analyze program performance, (2) identify slow functions or bottlenecks, (3) optimize execution time or memory usage, (4) profile Python, Java, or C/C++ programs with test cases or workload scenarios, or (5) generate performance reports with flame graphs and recommendations.

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。

Analyze detected vulnerabilities to assess realistic exploitability by examining control flow, input sources, sanitization logic, and execution context. Use when users need to: (1) Determine if a vulnerability is actually exploitable in practice, (2) Assess severity and impact of security issues, (3) Prioritize vulnerability remediation, (4) Understand attack vectors and exploitation conditions, (5) Generate exploitability reports with proof-of-concept scenarios. Focuses on injection vulnerabilities (SQL, command, XSS, path traversal, LDAP) with detailed analysis of reachability, controllability, sanitization, and impact.

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 Dockerfiles, Docker Compose configurations, and Kubernetes manifests for containerizing applications. Use when: (1) Creating Dockerfiles for Node.js, Python, Java, Go, or other applications, (2) Setting up multi-service environments with Docker Compose, (3) Generating Kubernetes deployments, services, and ingress configurations, (4) Optimizing container images for production, (5) Implementing containerization best practices. Provides both ready-to-use templates and custom-generated configurations based on project requirements.

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。