「quality」の検索結果

Use when preparing your agent for production — IAM scoping, inbound auth (JWT, SigV4), secrets management, cold start optimization, session lifecycle, rate limiting, input validation, and quota guidance. Triggers on: "production checklist", "harden agent", "production ready", "secure agent", "inbound auth", "going live", "cold start optimization", "session lifecycle", "StopRuntimeSession", "quota", "throttling", "maxVms", "rate limit", "security audit of outbound API calls", "gateway target audit for production", "restrict who can call", "lock down endpoint", "only our app can call". Not for Cedar tool-restriction policies — use agents-connect. Not for quality measurement — use agents-optimize. Not for outbound credential storage or API key wiring — use agents-connect. Not for A2A agent-to-agent auth — use agents-build. Cold start observation and diagnosis (not optimization) routes to agents-debug.

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 wants to migrate code that calls OpenAI, Gemini/Google AI, or the Anthropic API to Amazon Bedrock — a pure model/SDK rewrite. End-to-end: assesses the codebase, rewrites SDK calls, evaluates output quality against Bedrock, and delivers a ready-to-merge git branch. Also has an access-only mode for checking or enabling Bedrock model access for specific models (e.g. 'request access to Claude on Bedrock', 'which models do I turn on') with no code migration — it checks each model and walks the right access step, then stops. Not for agent runtime selection or agent migration planning (use agent-advisor), nor standalone cost estimates or infra-only migration. The full migration REQUIRES gcp-to-aws OR azure-to-aws installed alongside this skill (gcp-to-aws preferred when both are present); access-only mode needs neither.

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Selects, deploys, and customizes AI models on Amazon SageMaker. Training or Processing jobs, fine-tuning (SFT/DPO/RLVR/RLAIF), model selection, dataset preparation, evaluation, SageMaker or Bedrock deployment, inference optimization and endpoint diagnostics. Covers the full lifecycle from planning through production. Use when fine-tuning models on SageMaker, choosing which base model to customize, fine-tune, or deploy from SageMaker JumpStart or Hub, SageMakerPublicHub, or the SageMaker public model catalog, transforming or validating training data, evaluating model quality, deploying or optimizing endpoints, configuring IAM/S3 for training, or managing SageMaker Managed MLflow. Use for endpoint health, failures, latency, logs, metrics, errors. Covers Serverless Model Customization, Nova and OSS deployment paths, and PySDK v3. NOT for Ground Truth labeling, Feature Store, or general-purpose AWS infrastructure.

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Builds, configures, debugs, and optimizes AWS observability - operator-symptom questions and detecting Omni vs classic CloudWatch. CloudWatch on an already-reporting service: Log Insights, metric/composite/anomaly alarms, custom metrics/EMF, dashboards, X-Ray/ADOT tracing, canaries, CloudTrail, Dynamic Instrumentation (live breakpoints/snapshots), the Application Signals service map, and fleet health views. CloudWatch Omni on an existing Space: SQL over logs and traces, PromQL over metrics, Omni dashboards, Omni alerts, context graph for root cause, programmatic/IaC access (API/SDK/CLI/CloudFormation), driving Omni from a coding agent or skills, and evaluating AI agent quality from traces - on-demand and online scoring of live traffic, readback, custom evaluators. For first-time setup - creating an Omni Space, granting access, ingestion, or ADOT instrumentation for Application Signals (ServiceEvents, CI/CD metadata) or Omni - use setting-up-cloudwatch-observability. Not for app logging or threat detection.

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。

Evaluate Expo skills in this repo end-to-end - trigger accuracy, generated code quality, and runtime screenshots on iOS simulator and Android emulator via Expo Go (web optional). Use when the user wants to eval an Expo skill, test that a skill produces working code, benchmark a skill with device screenshots, or verify a skill's output renders correctly.

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 native-feeling, benchmark-quality mobile app screens (Expo / React Native). Use when designing or implementing any mobile UI — screens, flows, onboarding, paywalls, tab bars, sheets, settings, empty states — or when polishing motion, navigation, typography, dark mode, or perceived performance. Enforces Apple HIG fidelity, semantic colors, native controls, anti-slop discipline, navigation semantics (push vs replace, modal vs sheet vs overlay, the one-way doors where back must not exist), purposeful Reanimated motion, a full-motion simulator-verified iteration loop, and a study-real-apps-first workflow (pairs with the Appllama MCP). Trigger on "build a screen", "make this screen better", "design the onboarding", "wire up this flow", "polish the UI", "make it feel native", or any mobile design/implementation task.

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。

Diagnose and fix common Anima SDK design-to-code errors. Use when encountering Figma token errors, code generation failures, node not found issues, or output quality problems. Trigger with: "anima error", "anima not working", "anima debug", "figma to code error".

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 Anima code generation performance with caching, parallelism, and output tuning. Use when reducing generation latency, optimizing batch component generation, or improving generated code quality for production use. Trigger with: "anima performance", "anima slow", "anima optimization", "anima caching".

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。

Production readiness checklist for Anima design-to-code pipelines. Use when deploying automated design-to-code services, preparing CI/CD Figma-to-code automation, or validating output quality before production. Trigger with: "anima production", "anima go-live", "anima prod checklist".

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 complex Claude API issues including context window overflow, tool use failures, streaming corruption, and response quality problems. Trigger with phrases like "anthropic advanced debug", "claude complex issue", "claude tool use failing", "claude context overflow".

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 production-grade agent .md files aligned with the current Anthropic subagent contract. Also validates existing agents against the marketplace compliance rules. Use when building custom subagents, reviewing agent quality, or creating parallel agent architectures for orchestrator skills. Trigger with "/agent-creator", "create an agent", "build a subagent", or "validate my agent". Make sure to use this skill whenever creating agents/*.md files for plugins or standalone use.

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。

Launch both thermo-nuclear review subagents in parallel, then synthesize their findings. Use for thermos, double thermo review, or combined bug/security and code-quality branch audits.

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。

Data engineering patterns for ETL pipelines, data warehousing, Apache Spark, and data quality validation

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 wants to generate detailed English prompts for AI image tools (NanoBanana / Gemini / DALL-E / Midjourney) to produce top-conference-quality academic figures — including framework diagrams, network architecture diagrams, pipeline flowcharts, module detail diagrams, comparison/ablation figures, and data pattern grids — especially when the user says "论文配图提示词", "生成论文配图", "学术论文生图", "架构图提示词", "框架图提示词", "顶会风格配图", "CVPR 风格图", "NeurIPS 风格图", "paper figure prompt", "academic diagram prompt", or provides a LaTeX/PDF/Word paper and asks for figure prompts. If the user has not specified a color scheme, present the 8 preset palette options and color tool links before generating any prompt.

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 high-quality git commits: review/stage intended changes, split into logical commits, and write clear commit messages (including Conventional Commits). Use when the user asks to commit, craft a commit message, stage changes, or split work into multiple commits.

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 professional Marp presentation slides with 7 beautiful themes (default, minimal, colorful, dark, gradient, tech, business). Use when users request slide creation, presentations, or Marp documents. Supports custom themes, image layouts, and "make it look good" requests with automatic quality 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。

Evaluate Agent Skill design quality against official specifications and best practices. Use when reviewing, auditing, or improving SKILL.md files and skill packages. Provides multi-dimensional scoring and actionable improvement suggestions.

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 building or maintaining a personal LLM-powered knowledge base. Triggers: ingesting sources into a wiki, querying wiki knowledge, linting wiki quality, 'add to wiki', 'what do I know about', or any mention of 'LLM wiki' or 'Karpathy wiki'.

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。

Provide qualitative-first, evidence-traceable developmental review of scholarly works and audit low-stakes research-assessment rubrics with optional local quality controls. Never use for ranking people or consequential 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。

Create professional infographics using Nano Banana Pro AI with smart iterative refinement. Uses Gemini 3.6 Flash for quality review. Integrates research-lookup and web search for accurate data. Supports 10 infographic types, 8 industry styles, and colorblind-safe palettes.

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。

PHP type juggling and weak comparison (`==`) bypass. Use when authentication, HMAC/signature checks, or token validation uses loose equality, numeric coercion, or hash comparisons without strict types — common in legacy PHP and CTF-style code paths.

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。

Safely change and test untested codebases using Feathers' "Working Effectively with Legacy Code". Use when the user mentions "legacy code", "no tests", "untested codebase", "how do I test this", "seams", "characterization tests", "golden master", "sprout method", "afraid to change this code", "monster method", "dependency breaking", or "inherited a messy codebase". Also trigger when changing code without tests safely, getting a class under test when constructors, statics, or singletons block it, adding features to tangled modules, or planning incremental test coverage for an old codebase. Covers the legacy-code change algorithm, seams, characterization tests, sprout/wrap, and dependency-breaking techniques. For refactoring code that already has tests, see refactoring-patterns. For day-to-day code quality, see clean-code.

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。

Manage software complexity through deep modules, information hiding, and strategic programming. Use when the user mentions "module design", "API too complex", "shallow class", "complexity budget", "strategic vs tactical", "deep module", "information leakage", "pass-through method", "this code is over-engineered", or "simplify this design". Also trigger when reviewing an interface for simplicity, evaluating whether an abstraction is pulling its weight, deciding whether a comment is worth writing, or choosing between general-purpose and special-purpose approaches. Covers deep vs shallow modules, red flags for complexity, and comments as design documentation. For code quality, see clean-code. For architecture boundaries, see clean-architecture.

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 award-winning, immersive web experiences at the level of Awwwards-featured agencies. Use when the user mentions "Awwwards quality", "make my site stunning", "scroll animations", "parallax storytelling", "cinematic web design", "portfolio site", or "brand experience". Also trigger when elevating a standard landing page into a memorable digital experience. Covers dramatic typography, purposeful motion, scroll-based composition, and performance-optimized animation. For foundational UI, see refactoring-ui. For type selection, see web-typography.

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。