「age」の検索結果

技術文書やエージェント向け指示ファイルを作成・レビューするスキル。READMEや貢献手順も調べ、説明の分かりやすさ、運用上の整合性、多言語版の差分を確認します。

• 技術文書を作成・更新したいとき
• READMEや文書の変更レビュー
• エージェント指示と貢献手順の整備

複数のAIエージェントに担当範囲と完了条件を割り当て、並行作業を管理するスキル。カンバンで進捗や障害を共有し、テストとレビューを経て成果を統合します。

• 複数AIエージェントへの並行作業の割当
• セッションをまたぐ進捗と障害の共有
• ブランチやworktreeでの編集分離

複数のAIエージェントに開発や調査を分担させ、dmuxの画面で並行して進める手順をまとめます。担当範囲の切り分け、作業場所の分離、成果の統合を支援します。

• 調査と実装を並行して進めたいとき
• データベース・API・画面開発の分担
• テスト監視と修正を分担したいとき

追加の学習データを用意せず、文章で指定した候補に画像を分類したり、内容に合う画像を検索したりします。画像と文章の類似度比較や、複数画像の一括処理も扱うスキルです。

• 文章のラベルで写真を分類したいとき
• 画像の内容を言葉で指定して探したいとき
• 画像と説明文の対応を比較したいとき

AIエージェントの導入やモデル設定、外観変更、機能拡張を案内するスキル。Hermes Agentを複数の画面やメッセージサービスで使うための運用を支援します。

• 導入とモデル選択を進めたいとき
• メッセージサービスとの接続
• テーマや画面の拡張を行いたいとき

Elite frontend image-direction skill for generating premium, conversion-aware website design references. CRITICAL OUTPUT RULE — generate ONE separate horizontal image FOR EVERY section. A landing page with 8 sections produces 8 images. Never compress multiple sections into one image. Enforces composition variety (not always left-text / right-image), background-image freedom, varied CTAs, varied hero scales (giant / mid / mini minimalist), narrative concept spine, second-read moments, and a single consistent palette across all images. Optimized for landing pages, marketing sites, and product comps that developers or coding models can accurately recreate.

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。

Elite frontend image-direction skill for generating premium, conversion-aware website design references. CRITICAL OUTPUT RULE — generate ONE separate horizontal image FOR EVERY section. A landing page with 8 sections produces 8 images. Never compress multiple sections into one image. Enforces composition variety (not always left-text / right-image), background-image freedom, varied CTAs, varied hero scales (giant / mid / mini minimalist), narrative concept spine, second-read moments, and a single consistent palette across all images. Optimized for landing pages, marketing sites, and product comps that developers or coding models can accurately recreate.

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。

Building one page that looks like a page the course already has — copy the model page with `duplicate_scene`, then rewrite the copy's words with one `patch_stage` op per content slot. Use when a new page has to inherit an existing page's design, whether the model page came from an imported deck or the course wrote it earlier. Not for revising a page that is already the page you want (that is `pro-editing`), and not for the course-level work of cataloguing layouts and assigning one per outline entry (that is `style-clone`).

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 a landing page, sales page or checkout page for conversion leaks and return a fix list ordered by expected revenue impact. Use when asked to review, critique or improve a landing page, sales page, opt-in page, product page or checkout flow, when conversion rate is low, when paid traffic is not converting, or when someone asks "why isn't this page converting" or wants a CRO / landing page review.

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。

Patterns and techniques for adding governance, safety, and trust controls to AI agent systems. Use this skill when: - Building AI agents that call external tools (APIs, databases, file systems) - Implementing policy-based access controls for agent tool usage - Adding semantic intent classification to detect dangerous prompts - Creating trust scoring systems for multi-agent workflows - Building audit trails for agent actions and decisions - Enforcing rate limits, content filters, or tool restrictions on agents - Working with any agent framework (PydanticAI, CrewAI, OpenAI Agents, LangChain, AutoGen)

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。

OpenAI's model connecting vision and language. Enables zero-shot image classification, image-text matching, and cross-modal retrieval. Trained on 400M image-text pairs. Use for image search, content moderation, or vision-language tasks without fine-tuning. Best for general-purpose image understanding.

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。

Mounts Cloud Storage buckets on Google Cloud as a POSIX file system using Cloud Storage FUSE (and the gcsfuse CLI tool). Use when interacting with the gcsfuse CLI or Cloud Storage FUSE mounts: decide whether FUSE, native gs:// reads, or Filestore/Managed Lustre fits a workload, deploy tuned mounts on GKE, Compute Engine, or Cloud Run, enable and size file, stat, and list caches, tune mount flags (--implicit-dirs) or config-file settings, apply workload profiles, keep ML checkpointing safe (rename atomicity, hierarchical namespace/HNS, close-time finalization, concurrent writers), or diagnose slow training, low throughput, or bill spikes with gcsfuse metrics. Covers mount semantics, gcsfuse CLI and config files, GKE gcsfuse CSI driver (Workload Identity principal:// bindings, profile StorageClasses, sidecar sizing), and Cloud Run volume mounts. Don't use for bucket administration or data management without a mount (google-cloud-storage-basics) or fully POSIX-compliant shared file systems (Filestore, Managed Lustre; use gke-storage).

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 publish a self-contained HTML page to Superset, then answer the comments readers pin to it. Use this instead of publishing a Claude artifact whenever the reader is a teammate: a page is listed in the org, every publish mints a version, and pinned comments come back to the agent. Use when the user asks to make or publish a page, turn a report, dashboard, chart, doc, or analysis into a shareable link, update or re-version a page already published, or work through comments left on one, including "make me a page for this", "publish this as a page", "share it as a link", "add a version", "address the comments on that page". Also use it unprompted to offer a page when you finish output a teammate will read: a change or PR walkthrough, a comparison, a report or digest, an incident writeup, or verification screenshots.

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。

OpenAI's model connecting vision and language. Enables zero-shot image classification, image-text matching, and cross-modal retrieval. Trained on 400M image-text pairs. Use for image search, content moderation, or vision-language tasks without fine-tuning. Best for general-purpose image understanding.

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。

Required before calling image_generate. Create and edit images from a prompt — generate an image, make a picture / logo / illustration / icon / banner / poster / thumbnail / hero image / mockup / product shot / social graphic, or edit / restyle / combine existing images from reference images. Uses OpenAI gpt-image-2 through the Hive image service, billed to the user's Hive credits like an LLM call (no API key needed). Teaches the exact call shape, the quality/cost tradeoff (quality="low" is the default and cheapest), reference-image editing, how to show the result to the user with attach_file, and the failure modes (out of credits, model unavailable, moderation).

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。

ERC-8004 Agent identity: 注册/更新/上架/下架/搜索agent, register/update/activate/deactivate/search — User/ASP/Evaluator(买家/卖家/评审员, 旧称仲裁者亦可); 我的agent/ASP, 找做X的ASP/agent有什么服务/endpoint怎么填/查口碑/传头像. + Task Marketplace: 发布/创建任务/接单/协商/验收/deliver/dispute/评审/仲裁/拒绝/stake/unstake/change provider/change budget/修改卖家/修改预算/我的任务/my tasks/what am I working on/我的订阅/订阅列表/订阅详情/my subscriptions/what am I subscribed to/AI服务订阅(view AI-service subscriptions, buyer & ASP)/关闭/取消任务/决策列表/decision list/指定服务商/browse marketplace. + task watch: 监听任务进展/历史消息/未读消息/未决策/outstanding decisions. + device routing: 设备列表/我登录了哪些设备/device list, 本设备或指定设备订阅消息开关. + okx-a2a missing/uninitialized. Match by meaning. MUST ACTIVATE on inbound envelopes: (1) {agentId, message:{source:"system", event, jobId,...}} system event; (2) {msgType:"a2a-agent-chat", jobId, sender:{role},...} agent-to-agent task chat (sender.role = COUNTERPARTY, not you); (3) literal "Read the okx-ai skill" (or legacy "Read the okx-agent-task skill") in the envelope.

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。

Give the AI agent its own EVM wallet with admin-controlled policies the agent CANNOT bypass even under prompt injection. Encrypted keystore (AES-256-GCM, scrypt KDF), policy file the agent has no tool to write, deterministic policy gate on every signing operation, optional local HTTP dashboard. Triggers: agent wallet, give the agent a wallet, agent address, fund the agent, agent autonomy, policy gate, kill switch, agent permissions, bounded autonomy, ERC-4337 alternative, session-key alternative.

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 Internet Court — the trust layer for agent-to-agent commerce. Use whenever an agent needs to transact with another agent or a paid service, or a user mentions agent payments, paid APIs (HTTP 402/x402), wallet custody or trust concerns, spending mandates, delegated permissions (ERC-7710/7715), escrow, agent identity or reputation (ERC-8004), negotiation between agents (A2A), agent jobs (ERC-8183), machine payments (MPP, AP2), supervision of agent behavior, revocation, verification, or dispute resolution (GenLayer) — even if they never say "Internet Court". Routes to the vendored protocol skills and connector skills in this package.

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 a notebook run fails on a missing package (ImportError, ModuleNotFoundError, "there is no package called"), when you need to inspect an installed package version, or when you need to install, add, or manage Python or R packages for the notebook runtime. Covers inspect_packages, routing Python vs R through manage_packages, why in-cell %pip/!pip/install.packages() and OS installers are forbidden, restarting the kernel after an install, and when to stop and ask the user.

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。

Competitor page gap analysis — take a query you want to win and the pages currently outranking you, and produce a concrete brief on what to add or change to compete. Compares content depth and coverage (subtopics they cover that you don't), structure (headings, format, tables, media), search-intent match, E-E-A-T signals, schema/rich-result eligibility, internal linking, and on-page optimization — across your page vs. the top-ranking competitors. Use this skill whenever the user wants to know why a competitor outranks them on a specific query, what a top-ranking page does better, a content-gap analysis against specific competitor URLs, or a brief to beat a specific SERP. Trigger on: "why does competitor X outrank me", "content gap analysis", "competitor page analysis", "what does the top result have that I don't", "how do I beat this page", "analyze competitor pages", "SERP gap", "compete for [keyword]". For seed keyword discovery use /keyword-research; for a content calendar use /content-planner.

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。

Score and diagnose Google Ads landing pages. Use when asked to audit a landing page, check landing page quality, diagnose high-CTR but low-conversion-rate ad groups, improve Quality Score's Landing Page Experience component, or compare an ad group's messaging against its landing page. Trigger on "landing page audit", "landing page score", "landing page quality", "why is my conversion rate low", "LPX", "landing page experience", "ad to page match", or when `/google-ads-audit` surfaces a high-CTR / low-CVR ad group.

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。

[omh] AI agent progress for managers: help managers inspect AI-agent progress, blockers, quality gates, and throughput levers. Use when the user says: agent-ops-review, agent ops review, agent productivity, operator productivity, manager view, quality dashboard, throughput review, agent work quality.

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。

Azure Queue Storage JavaScript/TypeScript SDK (@azure/storage-queue) for message queue operations. Use for sending, receiving, peeking, and deleting messages in queues. Supports visibility timeout, message encoding, and batch operations. Triggers: "queue storage", "@azure/storage-queue", "QueueServiceClient", "QueueClient", "send message", "receive message", "dequeue", "visibility timeout".

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。

Microsoft 365 Agents SDK for .NET. Build multichannel agents for Teams/M365/Copilot Studio with ASP.NET Core hosting, AgentApplication routing, and MSAL-based auth. Triggers: "Microsoft 365 Agents SDK", "Microsoft.Agents", "AddAgentApplicationOptions", "AgentApplication", "AddAgentAspNetAuthentication", "Copilot Studio client", "IAgentHttpAdapter".

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。