「environment」の検索結果

既存コード・既存関数・既存モジュールの暗黙契約を抽出するスキル。 戻り値型、副作用、例外、前提条件、境界条件、環境依存挙動を確認し、 reuse前のリスクを可視化する。名前やコメントではなく実際の挙動から 契約を読み取り、不一致を分類し、再利用可否を判定する。 Use when reusing legacy code, verifying actual behavior of existing functions, or extracting implicit contracts before implementation. Covers: hidden contract, implicit contract, reuse risk, legacy behavior, 暗黙契約, 既存資産調査. Analyzes return types, side effects, exceptions, preconditions, boundary conditions, and environment-dependent behavior.

EPA air quality data and HUD foreclosure listings. 2 tools for environmental and housing intelligence.

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Use when working with Chamber — chamber secrets management using AWS Systems Manager Parameter Store, service-scoped secret organization, environment variable export, and parameter auditing. Covers reading and writing secrets by service, comparing environments, listing services, version tracking, and IAM permission validation. Use when managing AWS SSM-based secrets with Chamber, auditing parameter changes, or comparing configs across services.

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Use when working with Aws Codebuild — aWS CodeBuild project management and build analysis. Covers build project inventory, build history, build phase details, environment configurations, source credentials, report groups, and build metrics. Use when inspecting build projects, debugging build failures, reviewing build environments, or analyzing build performance.

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Use for environment validation: .env, config.yaml, paths, Python, Git — check + auto-fix + cron every 60min.

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。

Configure Netlify projects via netlify.toml and the _headers/_redirects files — covering build settings and deploy contexts alongside environment variables/scopes and the Secrets Controller plus redirect/rewrite/proxy and custom-header rules. Use when setting a build command or publish directory, adding redirect or rewrite or proxy rules, configuring custom headers or basic auth, setting or scoping environment variables and secrets, wiring up a monorepo or SPA fallback, or skipping unnecessary builds. Reach for this whenever you touch netlify.toml or ask "why is my env var undefined in a function" or "how do I redirect this path".

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Set up an isolated Python environment for SageMaker / AWS work, with the right Python version and current boto3. Use this skill whenever Python code will be executed for a SageMaker deployment, training job, or any AWS automation — including when about to run `pip install`, when about to invoke `boto3`, when creating or activating a virtualenv, or when the user asks to "set up the environment". Never use system Python and never `pip install` into it. Always isolate. This skill prevents the most common failure modes: wrong Python version, dependency conflicts, and stale SDKs.

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Build SwiftUI views with modern MV architecture, state management, and view composition patterns. Covers @Observable ownership rules, @State/@Bindable/@Environment wiring, view decomposition, custom ViewModifiers, environment values, async data loading with .task, iOS 26+ APIs, Writing Tools, and performance guidelines. Use when structuring a SwiftUI app, managing state with @Observable, composing view hierarchies, or applying SwiftUI best practices.

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Use this skill when handling secrets, API keys, environment variables, or ensuring server-only code never reaches the client bundle. Trigger words: シークレット, 秘密鍵, API キー, 環境変数, env, server-only, クライアント漏洩, NEXT_PUBLIC, バンドル.

Use this skill when validating API input, environment variables, form data, or DB responses at runtime boundaries. Trigger words: バリデーション, 入力検証, Zod, スキーマ, env 検証, 型強制, parse, safeParse, ランタイム境界.

Guide for installing Tinker, setting up the environment, getting an API key, and verifying everything works. Use when the user is getting started, setting up their environment, or troubleshooting installation issues.

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Set up and run multi-turn RL training for interactive environments (terminal tasks, tool use, search/RAG, games) using the Tinker API. Use when the user wants multi-turn RL, agentic training, tool-use RL, or interactive environment training.

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Activate for any ticket that adds or changes a Supabase migration, environment variable, edge function deployment, or runtime configuration. Ensures infrastructure is live in the target QA environment before state:ready-for-qa.

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Guide developers through setting up development environments with proper tools, dependencies, and configurations

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bu skill guides organizations through implementing zero trust architecture in cloud environments following NIST SP 800-207 and Google BeyondCorp principles. It covers identity-centric access controls, micro-segmentation, continuous verification, device trust assessment, and Dağıt:ing Identity-Aware Proxy to eliminate implicit network trust in AWS, Azure, and GCP environments.

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High-performance reinforcement learning framework optimized for speed and scale. Use when you need fast parallel training, vectorized environments, multi-agent systems, or integration with game environments (Atari, Procgen, NetHack). Achieves 2-10x speedups over standard implementations. For quick prototyping or standard algorithm implementations with extensive documentation, use stable-baselines3 instead.

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Production-ready reinforcement learning algorithms (PPO, SAC, DQN, TD3, DDPG, A2C) with scikit-learn-like API. Use for standard RL experiments, quick prototyping, and well-documented algorithm implementations. Best for single-agent RL with Gymnasium environments. For high-performance parallel training, multi-agent systems, or custom vectorized environments, use pufferlib instead.

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Operate E2B agent sandboxes using the CLI. Use when user needs to run code in isolation, test packages, execute commands safely, or work with binary files in a sandbox environment. Keywords: sandbox, e2b, isolated environment, run code, test code, safe execution.

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TanStack Start (TanStack Router ベースのフルスタック React フレームワーク、 @tanstack/react-start) の API リファレンス。Next.js とは別フレームワーク。 createServerFn (validator / handler), createMiddleware, server routes, createServerOnlyFn / createIsomorphicFn、SSR / selective SSR / SPA mode / static prerendering / ISR、streaming、hydration、 server entry / client entry、hosting (Cloudflare Workers / Netlify / Vercel / Node / Bun / Docker)、authentication / session、Tailwind、SEO、 environment variables。ルーティングは tanstack-router スキルを参照。

Claude Code (code.claude.com) の権限・セキュリティ・企業管理リファレンス。 permissions (allow / ask / deny), permission modes, auto mode, sandboxing (sandboxed Bash), sandbox environments, zero data retention, data usage, admin setup, server-managed settings, corporate launcher, network config, authentication (SSO), analytics, monitoring (OpenTelemetry), costs, large codebases。

Claude Managed Agents (platform.claude.com/docs/en/managed-agents) リファレンス。 Agents API, Sessions API, Environments API, SSE event streaming, threads, deployments (cron スケジュール), dreams (メモリ統合), vaults (credentials), memory stores, self-hosted sandboxes, webhooks, multiagent orchestration, user profiles, Agent Skills 添付, MCP connector 接続, permission policies。

Sentry の Issue 一覧 URL（例 `https://<org>.sentry.io/issues/?project=<id>&environment=<env>`）を受け取り、未解決（unresolved）の Issue を列挙して1件ずつサブエージェントで並列調査する。コード上で既に解消済みと確認できたものは Sentry 側を resolved にし、未解消のものは `create-issue` スキルで GitHub Issue を作成する。「Sentryのissueをトリアージして」「Sentryの未解決エラーをチケット化して」といった依頼で使用する。

Tests genotype-environment associations and identifies adaptive loci while correcting for the four-confound landscape (structure, demography, background selection, sampling design) using LFMM2 with mandatory K via sNMF cross-entropy elbow (LEA 3), BayPass Core/AUX/C2/IS with Omega covariance matrix, RDA / pRDA for polygenic adaptation (Forester 2018; requires imputed genotypes), OutFLANK with trimmed FST null, pcadapt, gradient forests (Ellis-Smith-Pitcher 2012, NOT mis-cited Ellis-Manel), Capblancq & Forester 2021 RDA Swiss-army-knife, genomic-offset prediction with Lind & Lotterhos 2025 three-regime caveat, Lotterhos-Whitlock sampling optima, Wang & Bradburd 2014 IBD vs IBE, and Circuitscape + ResistanceGA. Use when identifying adaptive loci across gradients, choosing K for LFMM2, deciding among GEA methods, predicting maladaptation with the novel-environment caveat, distinguishing IBD vs IBE, or optimizing sampling design.

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Deploy and optimize applications on Vercel with preview deployments, edge functions, environment variables, and performance monitoring. Use when deploying Next.js applications, setting up preview deployments per pull request, configuring environment variables, using Vercel Edge Functions, optimizing build performance, implementing redirects and rewrites, configuring custom domains, or monitoring application performance with Vercel Analytics.

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。