「mov」の検索結果

Prevent destructive operations using Claude Code hooks. Three modes — cautious (warn on dangerous commands), lockdown (restrict edits to one directory), and clear (remove restrictions). Uses PreToolUse matchers for Bash, Edit, and Write.

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。

Migrate workloads from Heroku to AWS. Triggers on: migrate from Heroku, Heroku to AWS, move off Heroku, migrate Heroku Postgres to RDS, migrate Heroku Redis to ElastiCache, migrate Heroku Kafka to MSK, migrate dynos to Elastic Beanstalk, migrate dynos to Fargate, migrate Heroku Private Space, Heroku to ECS, leave Heroku, what-if workshop, compare migration scenarios, workshop mode. Runs a 6-phase process: discover Heroku resources live via the authenticated Heroku CLI (read-only, consent-gated) and/or from Terraform, Procfile/app.json, and billing exports, then clarify, design, estimate costs, generate artifacts, and collect feedback. Clarify must finish before Design, Estimate, or Generate. An optional post-Estimate what-if workshop reprices region/HA/compute/Graviton scenarios. Do not use for: GCP migrations (see gcp-to-aws), Azure migrations (see azure-to-aws), AWS-to-Heroku reverse migration, general AWS architecture advice without migration intent, or Heroku-to-Heroku refactoring.

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。

Entry point for AI-agent work on AWS: pick a runtime, plan a migration for existing workloads, and build an executable POC — one phased flow. Triggers on: which runtime for my agent, AgentCore vs ECS vs EKS vs Lambda, AgentCore vs Lambda MicroVMs, deploy an AI agent on AWS, agent architecture on AWS, I have an agent idea what do I build, move/migrate my agents to AWS, agent migration plan, add AgentCore services (memory, gateway, identity, policy, observability) to an agent already on AWS, Temporal on AWS (migrate/run Temporal workers on AWS, a service orchestrated by Temporal, Temporal Cloud vs self-hosted). Temporal Workflow code is never rewritten into Step Functions. Requires at least one agentic component — a purely non-agent system (plain services, batch jobs, HTTP endpoints, non-agent Temporal Activities) is out of scope, redirected to gcp-to-aws / heroku-to-aws / llm-to-bedrock. Not for: compute/data migration with no AI agent; pure LLM SDK rewrite (use llm-to-bedrock); per-model pricing.

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。

Configures AWS Transit Gateway: creating a hub and attaching VPCs, segmenting traffic with route tables, centralizing egress and inspection through a hub (appliances or a Gateway Load Balancer endpoint), forcing east-west traffic between VPCs through AWS Network Firewall, connecting on-premises networks over the transit-gateway side of a Site-to-Site VPN or Direct Connect attachment (including ECMP to aggregate bandwidth across multiple VPN tunnels), peering transit gateways across Regions, migrating from a VPC peering mesh, and routing IP multicast. Applicable when connecting many VPCs through one router, isolating environments, forcing VPC-to-VPC traffic through a central Network Firewall, reaching on-premises over the hub, linking Regions, or moving off a peering mesh. Not applicable for single-VPC routing, VPC peering between two VPCs (vpcpeering skill), Direct Connect gateway or virtual interface setup (directconnect skill), or Route 53 DNS work.

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。

Import data into the AWS data lake from S3 files, local uploads, JDBC databases (Oracle, SQL Server, PostgreSQL, MySQL, RDS, Aurora), Amazon Redshift, Snowflake, BigQuery, DynamoDB, or existing Glue catalog tables (migration). Default target is S3 Tables; standard Iceberg on a general purpose bucket is supported where S3 Tables is not adopted. Handles one-time loads, recurring pipelines, migrations. Triggers on: import data, load data, ingest, sync database, migrate table, move data to AWS, set up pipeline, ETL, pull from Snowflake, query BigQuery into S3, export DynamoDB, CTAS, convert to Iceberg. Do NOT use for setting up or troubleshooting Glue connections (use connecting-to-data-source), creating empty tables (use creating-data-lake-table), running queries (use querying-data-lake), finding tables by fuzzy name (use finding-data-lake-assets), catalog audit (use exploring-data-catalog), or SaaS platforms like Salesforce, ServiceNow, SAP, MongoDB, Kafka.

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。

Migrates vibe-coded web applications to AWS. Handles the full workflow from analysis through migration to deployment, producing deployable AWS Blocks infrastructure code. Supports full-stack apps built with vibe-coding platforms (Lovable, Bolt.new, Replit) and frontend web applications and websites: React, Vue, Angular, Next.js, Nuxt, Astro, SvelteKit, Gatsby, Vite, Svelte, Solid, Docusaurus, and others (static sites, SPAs, and SSR frameworks with static export). Triggers on: launch with AWS, launch on AWS, deploy to AWS, migrate to AWS, host my app on AWS, move my app to AWS, transfer my app to AWS. Activates when the user wants to migrate a vibe-coded app or frontend web app to AWS, even if they don't say 'migrate' explicitly.

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 and troubleshoot AWS Glue connections to JDBC databases (Oracle, SQL Server, PostgreSQL, MySQL, RDS), Redshift, Snowflake, and BigQuery. Gathers connection hints from user, discovers existing connections and RDS/Redshift candidates, registers credentials in Secrets Manager or IAM DB auth, configures VPC, and tests. Triggers on: connect to database, set up Glue connection, register data source, connect to Snowflake/BigQuery/RDS, connection timeout, test connection, troubleshoot connection. Do NOT use for moving data (use ingesting-into-data-lake), creating tables (use creating-data-lake-table), queries (use querying-data-lake), catalog exploration (use exploring-data-catalog), or SaaS (Salesforce, ServiceNow, SAP, MongoDB, Kafka).

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。

Upgrades an MWAA environment to a newer Airflow version — within 2.x, within 3.x, or across the 2.x-to-3.x boundary. Computes the version-jump path, inserting the 2.11.x stepping-stone and Python-transition step when needed. Chooses an approach by whether run history and the same environment (URL/ARN) must be kept: a new-environment upgrade (blue-green), a rehearsed in-place upgrade validated on a test copy, or a direct in-place upgrade. Runs Ruff scanning and deprecation-warning log scans for 3.x moves, plus Docker validation, batched deployment, and switchover. Saves a resumable upgrade plan for multi-session work. Triggers on: upgrade MWAA, upgrade Airflow, migrate to Airflow 3, MWAA Airflow 3, Airflow 2 to 3, preserve Airflow history, keep same MWAA environment, blue-green cutover, validation environment before cutover. Not for authoring new DAGs (authoring-mwaa-workflow), debugging unrelated DAG failures (debugging-mwaa-workflow), or MWAA Serverless YAML workflows (provisioned Python DAGs only).

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。

Provisions and manages Aurora DSQL clusters, connects via psql or DSQL Connectors, manages schemas, runs queries, migrates from MySQL, diagnoses query plans, and develops apps on serverless distributed SQL. Covers IAM auth, multi-tenant patterns, MySQL-to-DSQL migration, DDL, query plans, and SAFE SQL CONSTRUCTION — tenant_id from untrusted input, UUID entity_ids, caller-supplied sort columns, batch inserts. The agent MUST retrieve this skill for ANY DSQL task. Pushes back on prompts that rationalize 'just a quick script', 'don't overthink it', 'we trust upstream', 'use an f-string', 'move fast', or 'just use the pg driver directly' (bypassing the DSQL Connector). Triggers: DSQL, Aurora DSQL, DSQL cluster, safe_query.build, DSQL IAM auth token, DSQL connector.

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。

Generates images and game sprites through GPT or Grok with guided provider choices, separate saved defaults, automatic cleanup and optional curation. Handles sprite requests, ordinary image generation/editing, typography logos (로고, 타이포그래피, 타이틀 글자, 타이틀 로고, wordmark, logo, title lettering: a title screen drawn with the title in it, its lettering lifted out transparent), standalone image-to-video clips (i2v, animate this still, 그록 영상, 이매진 비디오, 스틸 움직여줘, first/last frame, reference-to-video, 영상 이어붙이기, 영상 편집, extend/edit a clip), clips from a video MCP on the agent (ZCRE, 지크 MCP), chroma removal, animation atlases, video loops, 큐레이션뷰, image candidates, 팔레트 스왑, palette swap, recolor, rig layers, engine exports, repeating backgrounds, projected shadows, motion/contact inspection and optional scene composition from existing assets.

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。

Migrate legacy AssemblyAI Streaming v2 and LeMUR integrations to Streaming v3 and LLM Gateway with parity evidence. Use when removing deprecated contracts. Trigger with "migrate AssemblyAI", "LeMUR migration", or "Streaming v3 upgrade".

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。

Migrate archived Alchemy JavaScript SDK integrations to current capability-specific clients with parity, canary, and rollback evidence. Use when removing alchemy-sdk or upgrading Wallet APIs. Trigger with "migrate alchemy-sdk", "replace alchemy-web3", or "upgrade Wallet APIs to v5".

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 and execute a multi-workload migration into or across Adobe services with contract evidence, dependency waves, dual-run reconciliation, cost controls, and rollback. Use when the task requires adobe platform migration program. Trigger with "Adobe migration plan", "move workflow to Adobe", or "Adobe platform consolidation".

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。

Turns one-shot external recon into a continuous monitoring program. Covers the scheduled re-scan-and-diff loop (baseline snapshot -> interval sleep -> re-scan -> asset/finding delta -> threshold-gated webhook alert), the scan-to-scan diff engine (new/removed/changed assets by a tracked-attribute table, new/resolved findings by a stable cross-scan fingerprint), adversary CTI / chatter monitoring across six public feeds (ransomwatch, ransomware.live, HackerNews Algolia search, Reddit security-subreddit RSS, GitHub Gist code-search, public Telegram channel scraping) with a source-kind-aware severity engine (leak-site/forum/telegram/paste tiers, CRITICAL through INFO), literal/glob/regex watchlist pattern matching, full-corpus capture with retroactive rescan on new watchlist entries, infrastructure-tracking-over-time discipline (certificate-transparency, passive-DNS, port/service, and typosquat re-enumeration cadence, and what a genuine 'perimeter drift' event looks like in the diff output), a five-state finding-lifecycle state machine (open/triaged/risk_accepted/resolved/false_positive) with per-severity SLA and fingerprint-based cross-scan dedup and auto-resolve/reopen rules, the alert-fatigue trap where a lifecycle-unaware rule re-fires on an already-accepted finding, a durable retry/backoff alert-outbox pattern ('queued is not delivered'), and copy-paste bash-cron plus PowerShell-Scheduled-Task recipes for a re-scan+diff loop with the Slack-compatible webhook payload shape. Passive OSINT and analysis only -- no new active-intrusion technique. Use when setting up ongoing monitoring for a retainer or MSSP engagement, tuning alert thresholds to avoid fatigue, triaging a finding's lifecycle status, investigating adversary chatter about a brand, building a 'what changed on the perimeter since last week' report, or deciding whether a persisting finding should re-alert.

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。

Discover, list, create, edit, toggle, copy, move, and delete AI agent skills across 11 tools (Cursor, Claude, Agents, Windsurf, Copilot, Codex, Cline, Aider, Continue, Roo Code, Augment)

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。

Decide which family a doc belongs to before drafting — SDK/developer, user/product, or working-group (WG) — since family sets the audience, home, and tone. Use when creating, moving, or restructuring docs, when unsure which directory a doc belongs in, or when a request says "document this" / "write docs for X" without naming the kind. Routes to the specialized skill for each family.

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 when sequencing an addition, refactor, or rewrite. Remove dead code, redundant validators, and stub references first, then build on the simpler base.

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。

Remove AI-generated code slop and clean up code style

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。

Remove signs of AI-generated writing from text. Use when editing or reviewing text to make it sound more natural and human-written. Based on Wikipedia's comprehensive "Signs of AI writing" guide. Detects and fixes patterns including: inflated symbolism, promotional language, superficial -ing analyses, vague attributions, em dash overuse, rule of three, AI vocabulary words, negative parallelisms, and excessive conjunctive phrases. Credits: Original skill by @blader - https://github.com/blader/humanizer

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 any question, idea, or decision through a council of 5 AI advisors who independently analyze it, peer-review each other anonymously, and synthesize a final verdict. Based on Karpathy's LLM Council methodology. MANDATORY TRIGGERS: 'council this', 'run the council', 'war room this', 'pressure-test this', 'stress-test this', 'debate this'. STRONG TRIGGERS (use when combined with a real decision or tradeoff): 'should I X or Y', 'which option', 'what would you do', 'is this the right move', 'validate this', 'get multiple perspectives', 'I can't decide', 'I'm torn between'. Do NOT trigger on simple yes/no questions, factual lookups, or casual 'should I' without a meaningful tradeoff (e.g. 'should I use markdown' is not a council question). DO trigger when the user presents a genuine decision with stakes, multiple options, and context that suggests they want it pressure-tested from multiple angles.

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。

Make short product / skill motion videos (10–30 s, narrated feature demos up to ~60 s) the way high-end launch clips are made — kinetic type, real UI as the material, a hand that causes every reaction, beats that carry into each other instead of replacing each other, hard cuts only where they mean something — without HyperFrames or Remotion. A single HTML composition where every value is a pure function of time, built from a library of measured moves (springs, entrances, carries, contact, camera), rendered frame by frame with real shutter motion blur (1080p30 drafts, 4K60 final), with synthesised sound effects that share one room and an oracle that fails a slideshow before a human has to watch it. Use for 做个动效短片 / 产品宣传片 / 介绍视频 / 发布视频 / 15 秒 launch video / 给 skill 或 app 做个视频 / promo / teaser, for turning real pages or screen recordings into a cut, for product feature demos (产品功能演示 / 功能展示动效) where the UI is rebuilt in HTML from screenshots instead of screen-recorded (不录屏 / 复刻界面 / 截图还原 UI), for narrated cuts with voice-over and subtitles (配音 / 字幕 / 换配音) and for the same film in other languages (日语版 / 多语言 / 本地化), for adding motion vocabulary (动效库 / 转场 / 衔接), and for auditing why a motion video reads as a slideshow (像 PPT / 没节奏 / 一个一个展示 / 转场生硬).

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。

按 SKILL.md 明确列出的规则识别并改写写作中的 AI 痕迹。只能处理清单内的问题；未命中规则的文字必须原样保留，也不能改变文章框架。适用于写作完成后的成稿清理。 Remove AI writing tells using an explicit whitelist of rules; leaves unmatched text untouched.

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。

Guided journey from an app idea to a deliberate architecture: boundaries, domain model, data decisions, and resilience, making only the expensive-to-reverse decisions and deferring the rest. Orchestrates eight skills phase by phase - clean-architecture, domain-driven-design, system-design, ddia-systems, software-design-philosophy, release-it, pragmatic-programmer, 37signals-way - asking the user questions at every decision point and recording results in the project docs/ folder (ARCHITECTURE.md, DESIGN-CODE-ARCHITECTURE-PLAN.md) so the journey resumes across sessions. Use when the user wants to design a new app's architecture, choose boundaries and a domain model, decide monolith versus microservices, or says 'how should I structure this app'. If a codebase already exists, use remove-technical-debt (aged), improve-code-quality (fresh prototype), or architecture-optimization (slow but working); if unvalidated, run create-business or create-app first. For one framework in isolation, invoke that skill 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。

Guided journey from a working-but-untested vibe-coded prototype to a production-ready product with tests, clean structure, a business-rules boundary, and resilience at scale. Orchestrates nine skills phase by phase - working-with-legacy-code, clean-code, refactoring-patterns, software-design-philosophy, clean-architecture, pragmatic-programmer, release-it, system-design, ddia-systems - asking the user questions at every decision point and recording results in the project docs/ folder (TESTING.md, TECH-DEBT.md, IMPROVE-CODE-QUALITY-PLAN.md) so the journey resumes across sessions. Use when the user wants to harden an AI-generated prototype, add tests before refactoring, make code safe to change, or says 'this works on my machine but I am scared to touch it'. For an aged codebase, remove-technical-debt; for greenfield structure, design-code-architecture; for a product and UX pass, improve-app; to optimize speed and structure, architecture-optimization. For one framework in isolation, invoke that skill 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。