「eks」の検索結果

Real-time log monitoring, crash detection, and auto-hotfixing code without human intervention / Pemantauan log real-time, deteksi kerusakan, dan perbaikan kode hotfix otomatis tanpa intervensi manusia.

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 guide for synthetic dataset generation, LLM-as-a-judge filtering, QLoRA fine-tuning (Unsloth), DPO alignment, and GGUF/Ollama export for local SLMs / Panduan ahli generasi data sintetis, fine-tuning QLoRA, DPO, dan ekspor GGUF/Ollama.

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 guide for converting UI screenshots, mockups, Figma frames, and whiteboard sketches into production-ready frontend code using vision-capable frontier models (Gemini 4 Pro, Claude 5.5, GPT Astra 6) — multi-pass visual extraction, component decomposition, responsive generation, and design token mapping / Panduan ahli konversi screenshot UI, mockup, frame Figma, dan sketsa whiteboard menjadi kode frontend siap produksi menggunakan model frontier berkemampuan vision — ekstraksi visual multi-pass, dekomposisi komponen, generasi responsif, dan pemetaan design token.

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Expert guide for reverse-engineering UI/UX interactions from screen-recordings and spatial video, extracting animation spring curves, and generating pixel-perfect Tailwind CSS v4 and Framer Motion code / Panduan ahli rekayasa balik interaksi UI/UX dari rekaman layar dan video spasial, ekstraksi kurva pegas animasi, dan pembuatan kode presisi Tailwind CSS v4 serta Framer Motion.

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 guide for complex form handling with React Hook Form, server-side validation (useActionState + Zod), multi-step wizards, and accessible form patterns / Panduan ahli penanganan formulir kompleks dengan React Hook Form, validasi server-side, wizard multi-langkah, dan pola formulir aksesibel.

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Expert guide for ephemeral WebAssembly (WASM) and isolated micro-runtime sandboxes to securely execute AI-generated code, validate database migrations in-memory, and perform sub-millisecond fuzzing / Panduan ahli sandbox WebAssembly (WASM) efemeral dan micro-runtime terisolasi untuk eksekusi kode AI yang aman, validasi migrasi database in-memory, dan fuzzing sub-milidetik.

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Deeply analyzes application architecture and structure to perform audit, bottleneck detection, and code/performance optimization / Mempelajari arsitektur dan struktur aplikasi secara mendalam untuk melakukan audit, deteksi bottleneck, serta optimasi performa dan kode.

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Expert guide for Composable MACH Architecture (Microservices, API-first, Cloud-native, Headless) — dynamic UI composition, federated API mesh, Backend-for-Frontend patterns, plugin/extension architectures, event-driven composition, and composable AI routing / Panduan ahli Arsitektur MACH Komposabel — komposisi UI dinamis, API mesh federasi, pola Backend-for-Frontend, arsitektur plugin/ekstensi, komposisi event-driven, dan routing AI komposabel.

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Expert guide for autonomous API schema drift detection, OpenAPI / gRPC breaking change monitoring, self-healing SDK adapter generation, and zero-downtime client-server synchronization / Panduan ahli deteksi drift skema API otonom, pemantauan breaking change OpenAPI / gRPC, pembuatan adapter SDK self-healing, dan sinkronisasi client-server tanpa downtime.

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Expert guide for long-term episodic memory integration (Mem0 v2, Letta/MemGPT, Zep v2), memory tier architecture, pgvector HNSW storage, and unified context management for autonomous AI agents / Panduan ahli untuk integrasi memori episodik jangka panjang (Mem0 v2, Letta/MemGPT, Zep v2), arsitektur tier memori, penyimpanan pgvector HNSW, dan manajemen konteks terpadu untuk agen AI otonom.

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。

AI-driven dynamic security fuzzing, exploit generation (XSS, SQLi, SSRF, Prompt Injection), and automated patch remediation / Fuzzing keamanan dinamis berbasis AI, eksploitasi, dan remediasi otomatis.

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Expert guide for autonomous web agents (Browser-Use, Stagehand v0.4+), hardcore anti-bot evasion (Playwright Stealth, WebGL masking), and Vision LLM visual QA / Panduan ahli agen web otonom, penghindaran deteksi bot, dan QA visual berbasis Vision LLM.

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Expert guide for intelligent model cascading and routing — complexity-scored task routing from Flash/Haiku to Sonnet/Opus/Astra, dynamic escalation with quality gates, 40-60% token cost reduction while maintaining output quality / Panduan ahli untuk kaskade dan routing model cerdas — routing tugas berbasis skor kompleksitas dari Flash/Haiku ke Sonnet/Opus/Astra, eskalasi dinamis dengan gerbang kualitas, pengurangan biaya token 40-60% dengan kualitas output terjaga.

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Expert guide for multi-pass autonomous AI code self-review — syntax validation, logic correctness, architectural conformance, security audit, and performance analysis without external tooling — enabling the agent to catch its own errors before presenting code / Panduan ahli review kode otonom multi-pass oleh AI — validasi sintaks, kebenaran logika, konformitas arsitektur, audit keamanan, dan analisis performa tanpa tooling eksternal — memungkinkan agen menangkap errornya sendiri sebelum menampilkan kode.

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Run a marketing objective to completion over hours, days, or weeks — not a single deliverable. Takes a business outcome ("40 qualified demos a month from organic by Q4", "cut blended CAC below $90", "launch the product in September"), decomposes it into work items with declared ECO floors, executes them across context windows with resumable state, and stops only when an independent gate returns SHIPPED or CLOSED. Use when the request is an outcome rather than an artifact, when work must continue in the background or across sessions, or when the user says "goal", "run this until", "keep working on", "autonomous", "long-running", "over the next month", or "get us to <number>".

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Evaluates convertible bond and mandatory convertible structures with equity sensitivity, credit floor, and Greeks analysis. Use when analyzing convertible offerings, pricing equity-linked instruments, or modeling conversion economics.

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Deploy applications to Kubernetes clusters using kubectl manifests for Deployments, Services, ConfigMaps, Secrets, and Ingress resources. Implement health checks, resource limits, rolling updates, and Helm chart packaging for production deployments. Use when deploying new applications to EKS, GKE, AKS, or self-hosted clusters, migrating from Docker Compose to container orchestration, implementing zero-downtime rolling updates, or setting up multi-environment deployments across dev, staging, and production.

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Foundation obedience training for dogs — sit, stay, come, heel, and down using positive reinforcement and marker training. Covers timing, reward hierarchy, session structure, distraction proofing, and common handler errors. Use when a new puppy (8+ weeks) is ready for foundation training, an adult dog lacks reliable basic commands, a rescue dog needs to learn the household's command vocabulary, before advancing to complex behaviors or off-leash work, or when existing commands have degraded and need re-establishing.

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Migrate workloads from Google Cloud Platform to AWS — including AI and agentic workloads regardless of cloud provider. Triggers on: migrate from GCP, GCP to AWS, move off Google Cloud, migrate Terraform to AWS, migrate Cloud SQL to RDS, migrate GKE to EKS, migrate Cloud Run to Fargate, Google Cloud migration, migrate from OpenAI to Bedrock, move off OpenAI, switch from ChatGPT API to AWS, migrate from Gemini to Bedrock, migrate LangChain to Bedrock, migrate LangGraph to AWS, migrate agentic workloads to AWS, move AI workloads to AWS, migrate my AI app to AWS. Runs a 6-phase process: discover GCP resources from Terraform files, app code, or billing exports, clarify migration requirements, design AWS architecture, estimate costs, generate migration artifacts, and collect optional feedback. Clarify must finish before Design, Estimate, or Generate. Includes AI provider migration guidance (for example, OpenAI to Amazon Bedrock) by selecting closest-fit Bedrock model families for required modality, latency/quality targets, context windows, and cost constraints. Model mapping is compatibility-guided, not 1:1 parity; validate prompts, tool-calling behavior, and eval metrics before cutover. Do not use for: Azure or on-premises migrations to AWS, AWS-to-GCP reverse migration, general AWS architecture advice without migration intent, GCP-to-GCP refactoring, or multi-cloud deployments that do not involve migrating off GCP.

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Ultra-fast proof-of-concept and MVP specialist. Use this agent when you need to validate an idea quickly working prototype before the meeting's over. Pragmatic, speed-focused, validation-oriented. Ships in days, not weeks.

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Begeleidt het opnemen, nabewerken en publiceren van de opgenomen rondleiding door de demo (/rondleiding) met de `just walkthrough`-commando's. Gebruik dit bij "ik heb een opname gemaakt", "neem hoofdstuk X opnieuw op", "zet de rondleiding op de preview", vragen over knippen, ondertitels, diateksten, de stemkloon of FAQ-antwoorden, en wanneer de replay iets niet goed nadoet (een klik, een scroll).

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Mergt één pull request of een reeks achter elkaar op main, met bijwerken zonder handtekeningverlies, wachten op de juiste checks, en controleren dat de uitrol geslaagd is. Gebruik dit bij "merge PR N", "laat de trein rijden" of het wegwerken van een stapel mergebare PR's.

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。

Run an explicitly requested, unusually strict dygo maintainability audit that seeks structural simplification, removes accidental complexity, and challenges weak abstractions without changing behavior.

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Maps GWAS-implicated loci to candidate effector (causal) genes by integrating variant-to-gene (V2G) features via Open Targets L2G (Mountjoy 2021), MAGMA gene-based association (de Leeuw 2015), FUMA SNP2GENE, cS2G combined SNP-to-gene scores (Gazal 2022), Polygenic Priority Scores (PoPS, Weeks 2023), FLAMES, INQUISIT, DEPICT, and enhancer-gene predictors (ABC, ENCODE-rE2G). Use when narrowing a GWAS lead locus to a candidate causal gene, picking between proximity, eQTL-based, and similarity-based prioritizers, integrating multi-evidence streams (fine-mapping, colocalization, ABC enhancer-gene, distance, chromatin), reconciling discordant L2G vs PoPS calls, prioritizing tissue-specific eQTL evidence, or triangulating across at least three independent lines of evidence for a publication-grade effector-gene nomination.

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。