「systems」の検索結果

Optimizes database queries and improves performance across PostgreSQL and MySQL systems including index design, query rewrites, configuration tuning, and partitioning strategies.

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。

Designs chaos experiments, creates failure injection frameworks, and facilitates game day exercises for distributed systems with runbooks, experiment manifests, and rollback procedures.

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。

Writes, optimizes, and debugs C++ applications using modern C++20/23 features, template metaprogramming, and high-performance systems 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。

System decomposition and dependency mapping

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。

Integrate Java business systems with ReachAI SDK registration, SDK instance heartbeat, gateway/embed access, and optional API Management handoff. Use when asked to connect a Spring Boot service to ReachAI, add reachai-capability-sdk or reachai-spring-boot2-starter, configure reachai.registry/reachai.project/reachai.capability, prepare @ReachCapability metadata for later manual SDK sync, or verify SDK onboarding from a ReachAI manifest.

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。

UI design system toolkit covering design tokens, component documentation, responsive calculations, and developer handoff. Use for creating design systems, maintaining visual consistency, or facilitating design-dev handoff.

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 and enforce brand identity systems: visual identity, voice and tone, typography, color, and cross-channel consistency. Use when creating style guides, defining brand voice, auditing brand consistency, or setting visual standards.

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。

Audit whistleblower systems and draft compliant reporting policies. Use when assessing or building whistleblower programs.

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。

System architecture design and review. Use when designing architecture, evaluating microservices vs monolith, writing ADRs, choosing a database, planning for scalability, reviewing system design, or generating architecture diagrams.

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。

ML engineering skill for productionizing models, building MLOps pipelines, and integrating LLMs. Covers model deployment, feature stores, drift monitoring, RAG systems, and cost 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。

Prompt engineering and LLM evaluation. Use when optimizing prompts, designing prompt templates, evaluating LLM outputs, building agentic systems, implementing RAG, creating few- shot examples, or designing structured-output 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。

This skill should be used when the user asks to "scan AI systems for security threats", "check for prompt injection vulnerabilities", "assess model security posture", "detect data poisoning risks", or "audit AI/ML pipeline security".

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 AI agent communication protocols: MCP tool schemas, A2A, function calling, and inter- agent messaging. Use when building multi-agent systems, defining tool interfaces, or implementing agent-to-agent communication.

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。

Designs multi-agent system architectures with orchestration patterns, tool schemas, and performance evaluation. Use when building AI agent systems, designing agent workflows, creating tool schemas, or evaluating agent 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。

Context management engine for AI coding agents. Use when building agent memory systems, optimizing context windows, allocating token budgets, designing RAG pipelines for code, or managing persistent multi-session agent state.

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。

Remote command execution and file transfer on SageMaker HyperPod cluster nodes via AWS Systems Manager (SSM). This is the primary interface for accessing HyperPod nodes — direct SSH is not available. Use when any skill, workflow, or user request needs to execute commands on cluster nodes, upload files to nodes, read/download files from nodes, run diagnostics, install packages, or perform any operation requiring shell access to HyperPod instances. Other HyperPod skills depend on this skill for all node-level operations.

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。

Engineer growth systems: PLG motions, referrals, viral and retention loops. "design a referral program"

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。

When the user wants to reduce churn, build cancellation flows, set up save offers, recover failed payments, or implement retention strategies. Also use when the user mentions 'churn,' 'cancel flow,' 'offboarding,' 'save offer,' 'dunning,' 'failed payment recovery,' 'win-back,' 'retention,' 'exit survey,' 'pause subscription,' 'involuntary churn,' 'people keep canceling,' 'churn rate is too high,' 'how do I keep users,' or 'customers are leaving.' Use this whenever someone is losing subscribers or wants to build systems to prevent it. For post-cancel win-back email sequences, see emails. For in-app upgrade paywalls, see paywalls.

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 TSA cybersecurity compliance advisor for critical infrastructure owners and operators. Use this skill whenever a user asks about TSA Security Directives for pipelines, freight railroads, passenger rail, public transit, or bus operators; the TSA Cyber Risk Management Program (CRMP); Cybersecurity Implementation Plan (CIP); Cybersecurity Operational Implementation Plan (COIP); Cybersecurity Assessment Plan (CAP); incident reporting to CISA; designation of a Cybersecurity Coordinator; Critical Cyber Systems (CCS); OT/IT network segmentation; the TSA November 2024 NPRM; or any directive in the SD Pipeline-2021 series, SD 1580-21-01 (freight rail), or SD 1582-21-01 (public transit/passenger rail). Also trigger for questions like "are we covered by TSA directives?", "what does the TSA require for pipeline cybersecurity?", "how do I build a CIP?", "what must I report to CISA?", or any request involving transportation critical infrastructure cybersecurity compliance.

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。

Adjudicates whether a hit generated by sanctions, PEP, or adverse-media screening is a true positive, false positive, or requires human escalation. Use whenever a user presents a screening alert, a name match against a watchlist (OFAC SDN, EU consolidated list, UK OFSI, UN list, PEP list, adverse media hit, etc.), or asks to clear a screening hit / reduce false positives / determine whether a flagged name is actually the listed party. Use even when the user describes the task casually — "is this person actually on the sanctions list", "did we get a real match", "clear this alert", "I have a hit on X" — these are all screening-adjudication tasks. Produces a deterministic determination with full audit trail (structured JSON + human-readable narrative). Designed for use by compliance analysts and screening 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。

Assess, report, and manage serious incidents involving high-risk AI systems under Article 73 EU AI Act. Covers incident qualification, deadline buckets (2/10/15 days), authority notification, deployer duties (Art. 26(5)), cross-regulation mapping (GDPR/NIS2/MDR), corrective measures, and DACH-specific routing. Use when asked to evaluate whether an AI incident is reportable, draft an Article 73 notification, identify the right authority, or build an AI incident response process.

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。

Assess and operationalize implementation readiness for high-risk AI systems under the EU AI Act Annex III, including provider and deployer obligations, conformity assessment, post-market monitoring, and EU database registration. Use when users say things like “we classified this as high-risk, what now?”, “build an EU AI Act readiness plan”, “assess our Annex III compliance gaps”, “what do providers/deployers of high-risk AI need to implement?”, “prepare for conformity assessment”, or “create a high-risk AI implementation roadmap.”

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 GDPR compliance assistant covering all four core workflows: (1) auditing code and systems for GDPR violations, (2) drafting GDPR-compliant documents such as privacy policies, Data Processing Agreements (DPAs), and consent notices, (3) answering GDPR compliance questions with authoritative article citations, and (4) reviewing data flows and PII handling practices. Use this skill whenever the user mentions GDPR, data protection, privacy compliance, lawful basis, data subject rights, DPA, privacy notices, consent management, data breaches, DPIAs, controller/ processor relationships, cross-border data transfers, or any EU/UK data privacy topic. Also trigger for questions like "is this GDPR compliant?", "how do I handle personal data?", "what does a privacy policy need?", or any request involving PII, personal data, or data retention in a regulatory context.

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。

Classify AI systems under the EU AI Act (Regulation (EU) 2024/1689) and determine compliance obligations. Use when asked to assess AI risk level, check if an AI system is prohibited, determine high-risk status, identify GPAI model obligations, map provider/deployer responsibilities, or build an AI Act compliance roadmap. Covers the full classification decision tree (Prohibited → High-Risk → Limited → Minimal → GPAI), obligations by role (provider, deployer, importer, distributor), compliance timelines, and DACH-specific considerations (German works council, BaFin, BSI, BNetzA). Triggers on phrases like "classify this AI system", "is this high-risk under the AI Act", "AI Act risk assessment", "EU AI Act compliance", "prohibited AI practice", "GPAI obligations", "AI Act timeline".

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。