「systems」の検索結果

Use when reviewing, designing, or modifying Java enterprise systems that may support EU Digital Markets Act gatekeeper-platform concerns, core platform services, interoperability, business-user data access, consent-dependent data combination, ranking, self-preferencing, advertising transparency, or anti-circumvention controls. This should trigger for requests such as Review a Java platform for DMA controls; Design interoperability and business-user data access evidence; Add ranking, consent, preference, or anti-circumvention audit controls; Assess gatekeeper-platform engineering evidence before production release. Part of Plinth Toolkit

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 reviewing, designing, or modifying Java enterprise systems that may support EU Market Abuse Regulation concerns, market surveillance, suspicious order and transaction reports, insider dealing controls, unlawful disclosure controls, market manipulation detection, inside information disclosure workflows, insider-list evidence, PDMR transaction notifications, alert explainability, model or rule provenance, reviewer decisions, or compliance escalation. This should trigger for requests such as Review a Java trading surveillance system for MAR controls; Design suspicious order and transaction monitoring evidence; Add alert explainability and reviewer-decision audit trails; Assess AI-assisted market-abuse detection before production release. Part of Plinth Toolkit

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 reviewing, designing, or modifying Java enterprise systems that use GenAI, LLMs, AI-assisted coding, RAG, AI agents, generated code, generated dependencies, prompt workflows, external model providers, or AI-enabled business logic and need ISO/IEC 42001 AI management system-aware engineering guidance. Part of Plinth Toolkit

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 reviewing, designing, or modifying Java enterprise systems and need a structured, repeatable ISO/IEC 25010:2023 quality-attribute review covering Functional Suitability, Performance Efficiency, Compatibility, Interaction Capability, Reliability, Security, Maintainability, Flexibility, and Safety, producing an engineering review report rather than an interactive ADR discovery. Part of Plinth Toolkit

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 you need to apply Java concurrency best practices — including thread safety fundamentals, ExecutorService thread pool management, concurrent design patterns like Producer-Consumer, asynchronous programming with CompletableFuture, immutability and safe publication, deadlock avoidance, virtual threads, structured concurrency, scoped values, backpressure, cancellation discipline, and observability for concurrent systems. This should trigger for requests such as Review Java code for concurrency; Review Java code for thread safety; Fix race conditions in Java concurrency code; Choose ExecutorService or virtual threads in Java; Improve synchronization and shared mutable state handling; Apply structured concurrency for related Java subtasks. Part of Plinth Toolkit

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 designing, implementing, reviewing, testing, or cleaning up feature toggles, feature flags, kill switches, runtime configuration gates, canary controls, staged rollouts, experiments, or temporary compatibility switches in Java enterprise systems. This should trigger for requests such as Design a feature toggle strategy; Review this feature flag; Add a kill switch safely; Test toggle on and off paths; Clean up an expired feature toggle; Plan controlled rollout and rollback behavior. Part of Plinth Toolkit

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。

Machine learning development patterns, model training, evaluation, and deployment. Use when building ML pipelines, training models, feature engineering, model evaluation, or deploying ML systems to production.

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。

Expert prompt optimization for LLMs and AI systems. Use when building AI features, improving agent performance, crafting system prompts, or optimizing LLM interactions. Masters prompt patterns and techniques.

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。

Responsible AI development and ethical considerations. Use when evaluating AI bias, implementing fairness measures, conducting ethical assessments, or ensuring AI systems align with human values.

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。

Implement authentication and authorization using @delon/auth. Use this skill when adding login/logout flows, JWT token management, role-based access control (RBAC), route guards, HTTP interceptors, and session management. Integrates with Firebase Auth and custom permission systems. Ensures secure token storage, automatic token refresh, and consistent authorization checks across components and services.

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。

PostgreSQL database documentation - SQL queries, database design, administration, performance tuning, and advanced features. Use when working with PostgreSQL databases, writing SQL, or managing database 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。

This skill should be used when performing maintenance or diagnostics on remote Linux systems. Triggers on "remote server", "Linux maintenance", "Ubuntu cleanup", "Debian", "disk space", "apt cleanup", "journal vacuum", "snap cleanup", "system diagnostics". Provides structured three-phase checklists with quantification.

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 systems, plan implementations, review architecture decisions - Use when you need to plan a complex feature, design system architecture, or make high-level technical decisions.

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。

Creating algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request creating art using code, generative art, algorithmic art, flow fields, or particle systems. Create original algorithmic art rather than copying existing artists' work to avoid copyright violations.

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。

Security-first SOP for multi-tenant RAG systems. Activate when a calling agent is building, reviewing, or debugging any retrieval pipeline whose vector store is shared across more than one user, organisation, workspace, customer, or permission scope. Encodes the single non-negotiable rule — **filter at the vector store query, never after retrieval / never after rerank** — together with the per-vendor query-time filter APIs (Pinecone namespaces + `$eq`/`$in`, Weaviate `multiTenancyConfig` + tenant handle, Qdrant `is_tenant` payload index + `Filter.must`, Chroma `where`, pgvector RLS), and the cross-framework adapters (LlamaIndex `MetadataFilters`, LangChain `filter=` dict). Frame the work as preventing CVE-2024-41892 / EchoLeak / Slack-AI-class cross-tenant leakage, not as "adding a filter for relevance".

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。

SOP for building multi-agent systems with CrewAI — role-based collaboration, sequential/hierarchical processes, Flows, memory, delegation. Use when modeling agent teams with clear roles and task pipelines.

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。

Decision protocol for building, debugging, and operating LangGraph-based agent systems. Activates when a coder agent is asked to design a stateful LLM workflow, add human-in-the-loop, choose a multi-agent pattern (supervisor / swarm / hierarchical), pick a checkpoint backend, or migrate a fragile chain into a durable graph. LangGraph is positioned by its maintainers as a "low-level orchestration framework for building, managing, and deploying long-running, stateful agents" — this skill encodes the *when* and *why*, not the API.

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。

FHIR API development guide for building healthcare endpoints. Use when: (1) Creating FHIR REST endpoints (Patient, Observation, Encounter, Condition, MedicationRequest), (2) Validating FHIR resources and returning proper HTTP status codes and error responses, (3) Implementing SMART on FHIR authorization and OAuth scopes, (4) Working with Bundles, transactions, batch operations, or search pagination. Covers FHIR R4 resource structures, required fields, value sets (status codes, gender, intent), coding systems (LOINC, SNOMED, RxNorm, ICD-10), and OperationOutcome error handling.

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 the NIST AI Risk Management Framework (AI RMF 1.0) and adjacent guidance to AI / ML systems — model lifecycle governance, fairness and bias evaluation, robustness, transparency, accountability, third-party model risk, monitoring for drift, and AI incident response. Broader than prompt-injection (which is the security slice). Use when the user mentions 'AI risk,' 'AI governance,' 'NIST AI RMF,' 'AI compliance,' 'ML governance,' 'model risk management,' 'AI fairness,' 'AI bias,' 'algorithmic accountability,' 'AI Bill of Rights,' 'EU AI Act,' 'AI transparency,' 'model card,' 'AI red team,' 'AI safety,' 'responsible AI,' 'model drift,' 'concept drift,' 'AI monitoring,' 'AI incident,' or needs to assess or govern an AI / ML system.

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, scope, and execute an authorized red-team engagement — distinct from a penetration test. Covers engagement methodology, assumed-breach scenarios, ATT&CK emulation plans, rules of engagement, deconfliction with the blue team, post-engagement debriefs, and the program-level work that makes red teams actually improve defenses. Use when the user mentions 'red team,' 'red team engagement,' 'red teaming,' 'adversary emulation,' 'ATT&CK emulation,' 'assumed breach,' 'purple team exercise,' 'tabletop with technical execution,' 'red team scope,' 'rules of engagement,' 'red team RoE,' 'deconfliction,' 'red team debrief,' or wants to design or run a red-team engagement against systems with 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。

Analyze disk images, file systems, and memory captures for digital evidence recovery in forensic investigations and CTF challenges. Use when the user mentions 'disk forensics,' 'forensic analysis,' 'disk image,' 'file carving,' 'deleted files,' 'evidence recovery,' 'timeline analysis,' 'memory forensics,' 'volatility,' 'autopsy,' 'sleuthkit,' 'plaso,' 'log2timeline,' 'artifact analysis,' 'chain of custody,' or needs to examine a forensic image.

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。

Shared rules for test runs that drive the running app or run a test target: isolation from concurrent runs, stubs for infrastructure that cannot be stood up, provisioning privileged state, approval before writes to shared external systems, and cleanup of only what the run created. Not typically invoked directly.

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。

Implement PCI DSS compliance requirements for secure handling of payment card data and payment systems. Use when securing payment processing, achieving PCI compliance, or implementing payment card security measures.

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。

Implement NFT standards (ERC-721, ERC-1155) with proper metadata handling, minting strategies, and marketplace integration. Use when creating NFT contracts, building NFT marketplaces, or implementing digital asset 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。