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概要と使いどころ
Queries and downloads public cancer imaging data from NCI Imaging Data Commons. Supports IDC collection discovery, DICOM access, radiology (CT, MR, PET) and pathology AI datasets, metadata SQL, visualization, licensing, and citations. Uses public metadata and download routes without authentication; optional BigQuery and Google Healthcare routes require Google credentials.
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。
Bulk-migrate metadata to GitHub issue fields from two sources: repo labels (e.g. priority labels to a Priority field) and Project V2 fields. Use when users say "migrate my labels to issue fields", "migrate project fields to issue fields", "convert labels to issue fields", "copy project field values to issue fields", or ask about adopting issue fields. Issue fields are org-level typed metadata (single select, text, number, date) that replace label-based workarounds with structured, searchable, cross-repo fields.
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Remove multi-vendor AI provenance marks: invisible Unicode (Layer A), statistical text watermarks via rewrite (Layer B, always offer), and C2PA/EXIF/XMP/container metadata on PNG/JPEG/WebP/SVG/PDF/DOCX/ODT/HTML/MD/TEX. Covers Claude, Gemini/SynthID-class, OpenAI provenance, and open-LLM sampling marks. Use when the user asks to strip watermarks, remove C2PA/Content Credentials, clean AI metadata, remove invisible Unicode, anti-detect clean AI output, or runs /remove-ai-marks (aliases: /remove-claude-marks).
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 and diagnoses Workload Identity Federation for GKE authentication failures for Pods (403 "iam.serviceAccounts.getAccessToken" / permission denied, "could not find default credentials", or GKE metadata server unreachable) by verifying cluster and node-pool Workload Identity configuration, the Kubernetes ServiceAccount (KSA) to IAM binding (direct principal binding and legacy Google ServiceAccount impersonation), target-resource IAM roles, and gke-metadata-server health. Use when setting up KSA/GSA bindings (`roles/iam.workloadIdentityUser`, `iam.gke.io/gcp-service-account`) or when a Pod cannot authenticate to Google Cloud APIs. Don't use for in-cluster Kubernetes RBAC errors (API-server authorization), general workload crashes (use gke-workload-troubleshooting), or Pod Security Standards and NetworkPolicies (use gke-workload-security).
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。
Image optimization analysis for SEO and performance. Checks alt text, file sizes, formats, responsive images, lazy loading, CLS prevention, image SERP rankings (via DataForSEO), and image file optimization (WebP/AVIF conversion, IPTC/XMP metadata injection). Use when user says "image optimization", "alt text", "image SEO", "image size", "image audit", "optimize images", "image metadata", "image SERP", "convert to webp", or "image file optimize".
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 Next.js 14+ applications with App Router, server components, or server actions. Invoke to configure route handlers, implement middleware, set up API routes, add streaming SSR, write generateMetadata for SEO, scaffold loading.tsx/error.tsx boundaries, or deploy to Vercel. Triggers on: Next.js, Next.js 14, App Router, RSC, use server, Server Components, Server Actions, React Server Components, generateMetadata, loading.tsx, Next.js deployment, Vercel, Next.js performance.
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。
Submit compact NCBI Entrez E-Utilities requests for PubMed, Gene, Protein, Nucleotide, PMC metadata, and GEO metadata workflows. Use when a user wants concise Entrez search, fetch, summary, or link results; save raw JSON or XML only on request.
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 helps users automatically extract YouTube video transcripts and metadata via the BrowserAct API. The Agent should proactively apply this skill when users express needs like extracting full transcript from a specific YouTube video, getting subtitles and metadata for video content analysis, gathering video titles and likes counts, summarizing YouTube videos without watching them, collecting channel details from a video URL, tracking transcript automation for specific videos, scraping YouTube subtitles for internal knowledge bases, fetching full video content for AI summarization pipelines, downloading structured transcripts from YouTube links, analyzing video text content for media research, monitoring video publisher information and channel links, or building datasets from YouTube video transcripts.
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。
YouTube channel business email and contact extractor: accepts a channel id (UCxxx), handle (@name), or URL; navigates the channel About view; extracts the business email from the description text plus full channel metadata (name, id, country, subscriber count, view count, video count, joined date, external links classified as social/aggregator/personal). When the description has no email, follows non-social outbound links (personal site, business site, link aggregator) and scans those pages for an email so creators who place their inquiry email on their own site are still covered. Use when user mentions youtube channel email, youtube business email, youtube creator email, youtube contact email, youtube channel contact, youtube channel scraper email, youtube email finder, youtube influencer email, youtube outreach, youtube sponsorship contact, youtube partnership email, scrape email from youtube, get email from youtube channel, find youtube creator contact, extract youtube channel emails in bulk, youtube channel inquiry email, business inquiries youtube, brand deal email youtube, lead generation youtube creators, youtube creator outreach list, youtube channel about email, ytInitialData about, youtube channel id to email, youtube handle to email, youtube channel url to email, youtube about page scraper, channel about page email, youtube channel metadata, youtube channel social links, youtube channel external links, youtube channel description email, follow channel website for email, scrape creator personal website from youtube. Also applies to building creator/KOL contact databases for influencer marketing, agency lead lists, sponsorship prospecting, brand-creator collaboration sourcing, and enriching existing YouTube channel lists with contact info.
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。
Fetches Instagram user profile metadata including bio, follower count, following count, post count, verification status and other profile details. Use when user mentions Instagram profile info, user stats, account details, follower count, bio scraping, Instagram user data, IG profile, check Instagram account, Instagram user info, how many followers does someone have on Instagram, get Instagram profile, Instagram account metadata.
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 helps users automatically extract YouTube video transcripts and metadata in batch via the BrowserAct API. The Agent should proactively apply this skill when users express needs like batch extract full transcripts from YouTube videos for specific keywords, scrape YouTube subtitles for a list of videos, get batch video metadata and likes counts for analysis, automate YouTube search and subtitle extraction, collect multiple video transcripts published this week, download bulk YouTube video subtitles without writing crawler scripts, build a dataset of transcripts from top YouTube videos, extract YouTube video URLs and publisher info in batch, gather full video content for AI summarization pipelines, monitor recent YouTube videos and extract their transcripts, batch retrieve structured subtitle data for media research, extract transcripts from trending YouTube content automatically.
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。
Trustpilot company profile lookup on trustpilot.com — input a company domain (e.g. apple.com, shopify.com, shopwagandtail.com) and extract company metadata: official display name, businessUnitId, TrustScore (1-5), star rating, total review count, last-12-months review count, primary category and all categories, verification flags (verifiedByGoogle, verifiedPaymentMethod, verifiedUserIdentity), claimed/closed status, website URL, contact info (email, phone, country, city, address, zip), profile image, and optional reply behavior metrics (averageDaysToReply, replyPercentage, negativeReviewsWithRepliesCount, companyUsesAIResponses). Detects company-not-found and returns isCompanyFound=false with the searched domain. Use when user mentions Trustpilot company info, Trustpilot TrustScore, look up Trustpilot rating, check Trustpilot stars, Trustpilot business profile, Trustpilot review count, Trustpilot company lookup, Trustpilot verification status, Trustpilot reply percentage, Trustpilot AI responses, business unit ID Trustpilot, get company rating from Trustpilot, scrape Trustpilot company, batch enrich domains with Trustpilot, bulk company info Trustpilot, fast domain enrichment with TrustScore, Trustpilot company metadata, Trustpilot brand monitoring, competitor TrustScore comparison, trustpilot.com domain enrichment, trustpilot company profile, trustpilot domain check, trustpilot business listing, check if company is on Trustpilot, Trustpilot category lookup, company contact info from Trustpilot. Also applies to lead-list enrichment with reputation scores, supplier vetting, agency or KOL vetting via review reputation, aggregator sites that need TrustScores for many domains, due-diligence workflows, and pre-outreach reputation checks.
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。
Prepare, audit, or revise Nature-ready Data Availability statements, data repository plans, dataset citations, and FAIR metadata checklists for manuscripts. Use when the user asks about Nature data availability, research data sharing, repository selection, accession numbers, restricted or sensitive data, source data, supplementary datasets, DataCite-style dataset references, FAIR metadata for academic publication, or Chinese-to-English data availability wording for Chinese-speaking authors preparing Nature-family submissions.
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。
Imperative guidance for the System.Text.Json APIs added in .NET 11: the built-in `JsonNamingPolicy.PascalCase` naming policy, and the strongly-typed generic `JsonSerializerOptions.GetTypeInfo` and `JsonSerializerOptions.TryGetTypeInfo` metadata accessors (generic overloads that return a typed `JsonTypeInfo`). USE ONLY when the user is targeting net11.0 or later and needs PascalCase JSON property or dictionary-key names without writing a custom naming policy, a strongly-typed generic `JsonTypeInfo` instead of the non-generic `JsonTypeInfo`, or a no-throw way to probe whether a type's serialization metadata is resolved. DO NOT USE when the target is earlier than net11.0, the requested behavior uses an established pre-net11 naming policy, or the user explicitly selected another JSON library.
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。
Own concrete MSBuild ItemGroup and item-expression questions. USE FOR: Include, Remove, Update, item identity and metadata, transforms, filtering, batching, duplicate or overlapping items, and reviews that verify whether those operations are correct. Generated items stay in scope when the central defect is item identity, metadata, batching, duplicate declarations, or glob/Remove/Update semantics. For a generated artifact missing from compilation or output, wrong target timing/path, or FileWrites clean tracking without a broader item-semantics defect, use including-generated-files. The item operation may be broken, suspected, or already correct. Exclude property-only issues, general incrementality with no item question, broad reviews with no concrete item concern, and non-MSBuild systems.
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。
Hunting skill for ssrf vulnerabilities. Built from 15 public bug bounty reports including AWS metadata SSRF (HackerOne $25k Analytics PDF, Shopify Exchange $25k, Capital One 106M-record breach, Dropbox/HelloSign $4,913), GCP metadata SSRF (Snapchat $4k), Azure IMDS SSRF (Azure DevOps $15k chain, ChatGPT Custom Actions MSRC), DNS rebinding SSRF (Concrete CMS, GitLab UrlBlocker), gopher-protocol-to-Redis-RCE (Yahoo Mail $15k), link-preview SSRF (Reddit Matrix $6k), and headless-browser PDF-generator SSRF chains. Use when hunting SSRF on any target — OOB Collaborator confirmation mandatory for blind cases.
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。
Turn almost any file into Markdown plus metadata — PDF, Office, HTML, email, archives, images, audio/video, 1000+ formats — powered by Apache Tika, either via the tika-app CLI (zero setup, one file) or a running tika-server (curl, warm process, many calls). Leads with rmeta (structured, embedded-item-aware output) as the default operation rather than flat concatenated text, since you can't tell whether a file has embedded content from its extension. Covers metadata-only triage, type and language detection, OCR, and output-size discipline for agent context. Use whenever a task involves reading the content of a file whose format you don't want to hand-parse.
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。
Cross-model second opinion from Google Gemini — a different AI reviewing the same changes, with deep Google ecosystem knowledge. Three modes: review (pass/fail gate for Google Ads campaigns, SEO metadata, or code), challenge (adversarial stress-test that tries to break your changes), and consult (open Q&A with Gemini on Google Ads strategy, SEO best practices, or implementation questions). Use when the user says "gemini review", "ask gemini", "gemini challenge", "second opinion from gemini", "consult gemini", "stress test with gemini", "what would gemini say", "cross-model review", or "get another opinion". Voice aliases: "gem", "gemini check". Especially useful for Google Ads changes, SEO metadata updates, campaign structure decisions, keyword strategies, and bid/budget changes — Gemini has native Google ecosystem knowledge that complements Claude's 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。
Runs SQL analytics on SageMaker Catalog asset metadata tables exported as Apache Iceberg in S3 Tables. Covers governance queries, asset growth tracking, ownership audits, time-travel over catalog state, and metadata quality analysis. Applies when querying catalog inventory, finding assets without descriptions, comparing catalog snapshots, or auditing data ownership. Trigger phrases: catalog inventory SQL, how many assets, assets without descriptions, asset growth over time, who owns this data, catalog governance, data quality audit, catalog 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。
Organization-grade identity-fabric mapping: tenant/federation fingerprinting and the pre-auth user-ENUMERATION oracle methodology — enumeration and fingerprint only, never credential submission. Covers domain-to-tenant resolution (Microsoft getuserrealm.srf Managed/Federated namespace check, Entra OIDC metadata tenant-GUID extraction, Autodiscover v2), keyless Microsoft tenant-federation mapping (GetFederationInformation SOAP -> sibling-domain discovery, discover-only ROE, FEDERATED_WITH provenance edge held out of attack-path pivoting), Okta org-slug derivation + OIDC fingerprint + governed custom-domain enumeration, ADFS passive/active fingerprint + version inference, Google Workspace MX-correlated detection, generic OIDC (Auth0/Keycloak/Ping Identity/OneLogin/Duo) discovery, SAML metadata (5 paths), Azure AD Seamless-SSO Negotiate-challenge detection, Microsoft Defender for Identity (MDI) sensor-API presence check, the user-enumeration oracle methodology for Microsoft GetCredentialType (IfExistsResult semantics: exists / doesn't-exist / exists-in-federated-tenant / throttled) and Okta /api/v1/authn (errorCode differential), Medium-detectability discipline with a hard 20-candidate-per-tenant cap and admin/role interest-based ranking, and name x confirmed-email-pattern login-candidate synthesis that FAILS CLOSED with zero output when no org pattern is confirmed. Grounded directly in a production ASM implementation's sso_idp.py, tenant_recon.py, and core/email_patterns.py modules. Deepens — does not duplicate — offensive-osint skill's Identity Fabric endpoint reference with the tenant-federation MAP, the oracle WORKFLOW, and the candidate-SYNTHESIS methodology that reference lacks. Use when fingerprinting an organization's identity provider, mapping its tenant/federation boundary, running an authorized pre-auth user-enumeration pass, or synthesizing login candidates from harvested names to feed that oracle — never for password spray, credential submission, or auth bypass.
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 developing WordPress (Gutenberg) blocks: block.json metadata, register_block_type(_from_metadata), attributes/serialization, supports, dynamic rendering (render.php/render_callback), deprecations/migrations, viewScript vs viewScriptModule, and @wordpress/scripts/@wordpress/create-block build and test workflows.
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。
When the user wants to discover, evaluate, or prioritize App Store keywords. Also use when the user mentions "keyword research", "find keywords", "search volume", "keyword difficulty", "keyword ideas", or "what keywords should I target". For implementing keywords into metadata, see metadata-optimization. For auditing current keyword performance, see aso-audit.
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。
When the user wants to A/B test App Store product page elements to improve conversion rate. Also use when the user mentions "A/B test", "product page optimization", "test my screenshots", "test my icon", "conversion rate optimization", "CPP", or "custom product pages". For screenshot design, see screenshot-optimization. For metadata optimization, see metadata-optimization.
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。
When the user wants to analyze competitors' App Store strategy, find keyword gaps, or understand competitive positioning. Also use when the user mentions "competitor analysis", "competitive research", "keyword gap", "what are my competitors doing", or "compare my app to". For keyword-specific research, see keyword-research. For metadata writing, see metadata-optimization.
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。