独自の画像データで物体検出・画像分類・領域分割モデルを追加学習し、データ検証からクラウドGPUの選定、評価、学習結果の保存まで支援します。
- 独自画像で物体検出モデルを学習
- 画像分類モデルを追加学習したいとき
- 独自マスクでSAM/SAM2を調整
4.3万 件 ・ 関連度順
概要と使いどころ
独自の画像データで物体検出・画像分類・領域分割モデルを追加学習し、データ検証からクラウドGPUの選定、評価、学習結果の保存まで支援します。
PDFの文字・表・画像の抽出から、新規作成、結合・分割、ページ回転、透かしやパスワードの追加、スキャン資料の文字認識までを扱うスキル。
広告のリンク先ページを点検し、訴求の一致、スマホでの使いやすさ、フォーム、計測、安全性などから、成果につながる行動を妨げる要因と検証案を整理するスキル。
Deep-dive offensive methodology for dependency confusion and namespace attacks across all major package ecosystems. Covers npm scope confusion exploiting the gap between public and private scoped packages and .npmrc misconfigurations where registry mappings fail to pin internal scopes exclusively. Addresses PyPI namespace attacks through --extra-index-url resolution ordering, NuGet feed priority exploitation when multiple package sources are configured without clear directives, Maven and Gradle repository ordering where artifact resolution traverses repositories sequentially, Go module proxy abuse through GOPROXY misconfiguration, Ruby gems namespace squatting, and Docker image tag confusion with unqualified image references. Provides complete proof-of-concept methodology using safe callbacks including DNS canary via interactsh or Burp Collaborator and HTTP beacon with no destructive payload. Covers reconnaissance techniques for discovering internal package names through GitHub repository analysis, error message harvesting, JavaScript source map extraction, lock file parsing, job postings mentioning internal tools, and package manifest inspection. Directly references and builds upon Alex Birsan's seminal 2021 dependency confusion research. Each ecosystem section includes registry-specific exploitation mechanics, configuration vulnerabilities, and defensive countermeasures for engagement reporting.
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。
Creates Cursor-specific AI subagents with isolated context for complex multi-step workflows. Use when creating subagents for Cursor editor specifically, following Cursor's patterns and directories (.cursor/agents/). Triggers on "cursor subagent", "cursor agent". Do NOT use for generic subagent creation outside Cursor (use subagent-creator instead).
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。
Amazon search and category listing scraper: extract product listings from any Amazon search results page, keyword search URL, or category browse page and return per-item cards (asin, title, url, image, price, listPrice, stars, reviewCount, badges, isAmazonChoice, isBestSeller, isSponsored, delivery, boughtInPast, positionIndex) plus pagination state (currentPage, hasNextPage, nextPageUrl, totalResultsApprox). Works across all Amazon regional TLDs (amazon.com, amazon.co.uk, amazon.de, amazon.co.jp, amazon.in, amazon.fr, amazon.ca, amazon.com.au, amazon.it, amazon.es, amazon.com.mx, amazon.com.br, amazon.nl, amazon.se, amazon.sg, amazon.ae, amazon.sa, amazon.pl, amazon.tr, amazon.eg). Use when user mentions Amazon, amazon.com, Amazon SERP, Amazon search results, Amazon keyword search, Amazon category page, Amazon subcategory URL, scrape Amazon listings, extract Amazon search, Amazon product cards, Amazon browse page, Amazon /s URL, Amazon /s?k= URL, Amazon /b/ category URL, Amazon /gp/browse category, Amazon bbn category filter, Amazon rh filter, Amazon search pagination, Amazon 7 pages limit, Amazon subcategory scraper, Amazon keyword scraper, Amazon SERP scraper, Amazon by URL, Amazon by category, Amazon by keyword. Also applies to price monitoring across a keyword or category, competitive listing tracking on Amazon, brand keyword ranking on Amazon, category catalog audits, best-of category discovery from a search, paginated bulk extraction of Amazon search hits, and building ASIN lists from a search.
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。
This skill should be used when the user wants to "create an agent project", "start a new ADK project", "build me a new agent", "add CI/CD to my project", "add deployment", "enhance my project", or "upgrade my project". Part of the agents-cli skills suite. Covers `agents-cli scaffold create`, `scaffold enhance`, and `scaffold upgrade` commands, template options, deployment targets, and the prototype-first workflow. Do NOT use for writing agent code (ADK projects: use google-agents-cli-adk-code) or deployment operations (use google-agents-cli-deploy).
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。
Convert .NET projects and solutions (.sln, .slnx) to NuGet Central Package Management (CPM) using Directory.Packages.props. USE FOR: converting to CPM, centralizing or aligning NuGet package versions across multiple projects, inlining MSBuild version properties from Directory.Build.props into Directory.Packages.props, resolving version conflicts or mismatches across a solution or repository, updating or bumping or syncing package versions across projects. Also activate when packages are out of sync, drifting, or inconsistent -- even without the user mentioning CPM. Provides baseline build capture, version conflict resolution, build validation with binlog comparison, and a structured post-conversion report. DO NOT USE FOR: packages.config projects (must migrate to PackageReference first) or repositories that already have CPM fully enabled.
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。
Plug-in vision for text-only models. Hard rule: when a file path or URL with an image extension (.png, .jpg, .jpeg, .webp, .gif, .heic, .heif) appears anywhere in the conversation (typed by the user, injected as a `[Image: source: <path>]` line, or inside a tag) and you cannot see that image's content, run this skill on it before any other approach: no self-built OCR, no PIL, no tesseract. Also triggers on pasted-image placeholders such as `[Image #1]` and `[Unsupported Image]`. If you can actually see the image, do not use this skill. When unsure, run `modlens guard` before the first read of a session: a deny verdict means the active model has native vision and must read the image itself. Runs the modlens CLI to convert the image into structured JSON evidence: every word transcribed, layout regions, semantics, visual clues. Also use when the user asks how to install, configure, or switch modlens providers (Gemini API key, OpenAI-compatible endpoints, Claude API or Claude Code CLI).
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。
Write SEO-optimized blog posts, landing pages, and content improvements following Google's E-E-A-T and Helpful Content guidelines. Handles new content creation from a keyword or topic, and improving existing pages. Use when asked to "write a blog post", "create a landing page", "improve this page", "write content about X", "content for keyword X", "draft an article", "blog post about", "landing page for", "service page", "product page copy", "rewrite this page", "make this page rank better", "content brief", "how-to guide", "listicle", or any content creation or improvement task for a website.
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。
Search Experience Optimization (SXO), the bridge between SEO and UX/CRO. Audits the journey from the SERP click to the on-page goal: click-through factors (title, meta, rich results), then post-click signals that keep users and drive conversions: above-the-fold relevance and intent match, page speed / Core Web Vitals, readability, clear CTAs, mobile usability, and what causes pogo-sticking back to Google. Use when the user has decent rankings but poor CTR or poor on-page conversion, wants better dwell time / engagement, less bounce, or a page experience aligned with search intent. Trigger on: "SXO", "search experience optimization", "good rankings but low CTR", "high bounce rate from search", "pogo-sticking", "improve dwell time", "SEO and conversion", "ranking but not converting", "engagement signals", "make this page convert search traffic". For pure landing-page conversion in ads use /google-ads-landing; for content quality use /seo-page.
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。
E-commerce SEO audit for online stores (WooCommerce, Shopify, Magento, custom). Covers category / product listing pages (PLP), product detail pages (PDP), faceted navigation and its crawl-budget and duplicate-content traps, pagination, canonicals for variants and filters, out-of-stock / discontinued products, internal linking and breadcrumbs, and Product / Offer / Review / AggregateRating / BreadcrumbList structured data. Use whenever the user runs an online store and asks about product or category page SEO, filter URLs being indexed, product rich results, or thin/duplicate product pages. Trigger on: "e-commerce SEO", "ecommerce SEO", "product page SEO", "category page SEO", "WooCommerce SEO", "Shopify SEO", "faceted navigation", "filter URLs in Google", "product schema", "rich snippets for products", "out of stock SEO", "duplicate product pages". For a single URL use /seo-page; for full-site use /seo-analysis.
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 or evolve a complex agent-enabled Tutti workspace app repository. Use for Tutti apps with web/server/shared monorepos, @tutti-os/agent-acp-kit local agent runtimes, kit-owned TUTTI_CLI agent/composer discovery, dynamic agent catalogs, run-scoped MCP tool gateways, app-owned package builders, web-first debugging, i18n harnesses, and production package validation. For simple package creation or repair, use tutti-workspace-app-factory instead.
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] Agent is stuck, looping, or drifting: capture a stuck, looping, drifting, or repeatedly failing agent run, diagnose the likely failure pattern, and prepare the smallest safe recovery action. Use when the user says: agent-debug, agent debug, agent debugging, agent introspection, agent self-debug, self-debug, self debugging, looping agent.
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] Coordinating several agents or profiles: coordinate multiple Hermes profiles or agents with task, handoff, heartbeat, blocker, and completion states. Use when the user says: agent-board, agent board, kanban, multi-agent, multi agent, multi agent board, multiple hermes agents, multiple hermes profiles.
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 AI Agents Persistent SDK for Java. Low-level SDK for creating and managing AI agents with threads, messages, runs, and tools. Triggers: "PersistentAgentsClient", "persistent agents java", "agent threads java", "agent runs java", "streaming agents java".
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。
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.
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。
Grida AI agent system work: `@grida/daemon` (DaemonServer, loopback HTTP perimeter, files/workspaces, secrets store, daemon discovery) and `@grida/agent` (the agent tenant: sessions, providers/BYOK, runtime/tool execution, skills discovery, prompts, tiers, sandbox hosts). Use for `packages/grida-daemon/**`, `packages/grida-ai-agent/**`, desktop sidecar protocol changes, agent chat transport, and bugs in agent state or streams. For pure Electron window, preload, menu, deep-link, or CDP work, use `desktop`.
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。
Designs orchestrator-and-subagent hierarchies for a repository — splitting agents by exclusive write surface, pairing every producer with an independent auditor, and enforcing the split with a script that runs in CI. Use this whenever the user wants to set up, expand, audit, or fix a multi-agent or subagent structure for a codebase; asks how to divide work between agents; wants agent charters, roles, or a surface map written; or is hitting agents that collide on the same files, review their own work, or drift from their remit. Also use when sizing a roster or deciding whether a new agent is justified.
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。
Recommend suitable prompts from 10,000+ Nano Banana Pro image generation prompts based on user needs. Optimized for Nano Banana Pro (Gemini), but prompts also work with Nano Banana 2, Seedream 5.0, GPT Image 1.5, Midjourney, DALL-E, Flux, Stable Diffusion, and any text-to-image AI model. Use this skill when users want to: - Generate images with AI (any model — Nano Banana Pro, Gemini, GPT Image, Seedream, etc.) - Find proven AI image generation prompts and prompt templates - Get prompt recommendations for specific use cases (portraits, products, social media, posters, etc.) - Create illustrations for articles, videos, podcasts, or marketing content - Browse a curated prompt library with sample images - Translate and understand prompt techniques Also available: "ai-image-prompts" skill — a model-agnostic version of this library for universal image generation.
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。
Generate or edit images using OpenAI GPT Image API (gpt-image-2, gpt-image-1, etc). Use ONLY when the user explicitly names OpenAI or GPT as the provider: "gpt image", "openai image", "generate image with openai", "用 openai 画图", "用 GPT 生成图片". For generic image requests without a provider, use nanobanana-skill instead. Do NOT use for diagrams (架构图/流程图) — draw those with Mermaid or 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。
Generate one or more standalone Meta image-ad creatives via ChatGPT Image 2 (gpt-image-2) through the Arcads external API. Locks the model, auto-strips platform chrome, enforces edge-safe layouts and glyph-safety inside body text. Use when the user asks for a "gpt-image-2 ad", "ChatGPT Image ad", "Image 2 ad creative", "make a static image ad with GPT", or anchors on a need for typography-heavy / dense-text / UI-mimicry ad creatives (chat threads, comparison tables, fake search results, iOS dialogs, Slack snapshots, ChatGPT-conversation ads, Apple Notes lists). Does NOT trigger on Nano Banana cues — use nano-banana-image-ad for those.
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 types /prompts:hep-build or /agentlas-build, mentions @Hephaestus for build work, asks to create a single Agentlas agent, create a multi-agent team, or package an existing local/external agent into Agentlas 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。
Use when the user types /agentlas-upload, /agentlas upload, $hephaestus-upload, or /hep-upload to scan, gate, and publish an agent/team package to Agentlas Cloud (private) or Agentlas Hub (public).
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。