「assess」の検索結果

Expert-level guidance for Flutter production applications: architecture decisions (Clean/Layered/Hexagonal/Feature-First), advanced state management (BLoC/Riverpod/GetX), performance optimization (eliminating jank, memory profiling, reducing rebuild overhead), code review with design pattern enforcement, scalability assessments, native platform integration (MethodChannel/EventChannel/FFI), complex UI challenges (custom render objects, slivers), testing strategies (unit/widget/integration/golden), CI/CD pipeline architecture, build optimization, and accessibility compliance. Triggers: 'architecture decision', 'design pattern', 'performance issue', 'code review', 'production problem', 'scaling concerns', 'best practices', 'technical debt', 'refactoring strategy'. Essential for complex enterprise apps, critical production debugging, architectural refactors, or when choosing between competing technical approaches.

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。

사회복지 기관(복지관, 생활시설, 센터, 비영리단체)의 실무자가 업무용 앱을 만들거나, 업무를 자동화하거나, 엑셀·양식을 전산화하고 싶다고 할 때 코드를 만들기 전에 업무의 필요성, 개인정보 등급, 사람에 관한 기록의 윤리, 플랫폼 경로, 운영 조건을 판단하고 DECISION.md로 정리하는 절차. "프로그램일지 앱", "이용자 명부", "출석 체크", "사례관리 기록", "후원자 관리", "차량 운행일지", "복지 앱", "바이브코딩", "희망이음 자동 입력", "에이전트 브라우저로 동기화" 같은 말이 나오거나 사회복지 현장의 행정 업무를 자동화하려 할 때 사용한다. Use before writing any code when a Korean social-welfare practitioner wants to build an app or automate a workflow. Not for general coding tasks.

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。

사용자가 HWP나 HWPX 양식 파일을 주면서 "이 양식으로 앱을 만들어 달라", "양식을 전산화하자", "이 서식에 입력하고 출력하고 통계 내는 웹앱"이라고 할 때 사용한다. 양식의 칸을 그대로 데이터베이스로 옮기지 않고, 칸을 분류해 이용자 명부와 연결하고, 판정을 거쳐 앱과 HWP 출력을 만드는 절차다. 사회복지 기관의 일지, 명단, 대장, 보고서 서식에 쓴다. Use when a Korean social-welfare practitioner hands over an HWP/HWPX form and wants an app built from it. For app ideas without a form file, use welfare-app-assess instead.

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 performing security audits, vulnerability assessments, penetration testing, or when asked to find security issues. Use when encountering code that handles user input, executes commands, reads files, or makes network requests.

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 to inspect, audit, review, assess, or evaluate a codebase/project for maintainability, extensibility, architecture, reliability, security, testing, engineering quality, or AI-agent/prompt/tooling risks. Also use this when invoked by `/inspect`. It performs a read-only, evidence-backed, multi-agent project inspection with complete file coverage, per-dimension scoring, blunt technical findings, and explicit degraded-mode disclosure.

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。

This skill should be used when the user asks to "perform cloud penetration testing", "assess Azure or AWS or GCP security", "enumerate cloud resources", "exploit cloud misconfiguratio...

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。

You are a technical debt expert specializing in identifying, quantifying, and prioritizing technical debt in software projects. Analyze the codebase to uncover debt, assess its impact, and create acti

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。

This skill should be used when the user asks to \\\"analyze competitors", "assess competitive landscape", "identify differentiation", "evaluate market positioning", "apply Porter's Five Forces",...

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。

You are a technical debt expert specializing in identifying, quantifying, and prioritizing technical debt in software projects. Analyze the codebase to uncover debt, assess its impact, and create acti

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。

This skill should be used when the user asks to "test for broken authentication vulnerabilities", "assess session management security", "perform credential stuffing tests", "evaluate ...

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。

Testing and benchmarking LLM agents including behavioral testing, capability assessment, reliability metrics, and production monitoring—where even top agents achieve less than 50% on re...

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。

Cascade approved evolution changes through downstream layers, assess impact, run consistency checks, and write evolution audit record

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 with Coreweave — coreWeave GPU cloud management covering Kubernetes namespace inventory, GPU workload status, virtual server instances, persistent volume claims, node allocation, billing analysis, and network configuration. Use for comprehensive CoreWeave infrastructure assessment and GPU workload 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。

Use when working with Crowdstrike — crowdStrike Falcon endpoint detection and response, threat intelligence, host management, and incident investigation. Covers detection queries, host inventory, IOC searches, real-time response sessions, and vulnerability assessment. Use when investigating security incidents, reviewing endpoint health, analyzing threat detections, or managing CrowdStrike Falcon sensors.

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 with Confluence Deep — deep Confluence management covering spaces, pages, content analytics, permissions, and workspace health. Use when performing deep audits of Confluence usage, analyzing content freshness, reviewing space permissions, or assessing documentation coverage across an Atlassian Confluence instance.

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 with Codeball — codeBall AI-powered pull request risk assessment including automatic PR quality scoring, risk classification, and auto-approval for low-risk changes. Use when configuring CodeBall for automated PR triage, risk-based review routing, and review workload 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。

Use when working with Black Duck — synopsys Black Duck software composition analysis for open-source risk management, vulnerability detection, and license compliance. Covers project scanning, BOM component analysis, vulnerability tracking, license risk assessment, and policy management. Use when reviewing SCA scan results, analyzing open-source component risks, tracking license compliance, or managing Black Duck project configurations.

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 with Banana Dev — banana.dev ML inference platform management covering model inventory, deployment status, API call history, GPU allocation, scaling configuration, build logs, and latency metrics. Use for comprehensive Banana.dev model deployment assessment and inference performance 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。

Use when working with Azure Boards Deep — deep Azure Boards management covering work items, sprints, backlogs, queries, and team velocity analytics. Use when performing deep audits of Azure DevOps Boards, analyzing sprint health, reviewing backlog grooming, or assessing team capacity and velocity across Azure DevOps 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。

Use when working with Baseten — baseten ML deployment platform management covering model inventory, deployment status, autoscaling configuration, inference call history, GPU allocation, environment management, and performance metrics. Use for comprehensive Baseten workspace assessment and ML serving 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。

Use when working with Appwrite — appwrite backend platform management covering project inventory, database collections, user authentication stats, storage bucket usage, function deployments, webhook configuration, and platform health. Use for comprehensive Appwrite project assessment and resource monitoring.

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 with Azure Policy — azure Policy compliance assessment, policy and initiative assignment management, remediation task tracking, and definition analysis via Azure CLI.

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 with Aws Organizations — aWS Organizations account management, OU structure analysis, SCP policy review, and service access control. Covers organizational hierarchy, account inventory, policy inheritance analysis, delegated administrator status, and service control boundary assessment.

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。

Retrieve and analyze AlphaFold predicted structures for a protein. Use when the user provides a specific UniProt Accession ID and wants structural confidence metrics (pLDDT), domain boundary analysis, or disorder assessment. Do not use if the user only has a protein name, gene name, or amino acid sequence — ask for a UniProt ID 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。