929 件 ・ 関連度順
概要と使いどころ
Provides AI and machine learning techniques for CTF challenges. Use when attacking ML models, crafting adversarial examples, performing model extraction, prompt injection, membership inference, training data poisoning, fine-tuning manipulation, neural network analysis, LoRA adapter exploitation, LLM jailbreaking, or solving AI-related puzzles.
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Retrieve protein and nucleotide sequences from NCBI databases using E-utilities. Supports direct accession lookup, CDS translation, gene+organism search, locus lookup, PubMed-linked sequences, patent protein extraction, and organism+length fallback search. Use when you need to fetch biological sequences by accession, gene name, locus tag, PubMed ID, or patent number.
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。
Web scraping with anti-bot bypass, content extraction, undocumented APIs and poison pill detection. Use when extracting content from websites, handling paywalls, implementing scraping cascades or processing social media. Covers requests, trafilatura, Playwright with stealth mode, yt-dlp and instaloader patterns.
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 image analysis applications with Azure AI Vision SDK for Java. Use when implementing image captioning, OCR text extraction, object detection, tagging, or smart cropping.
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 asks to "codify how our founder sounds", "build a founder voice dossier", or "set our content pillars"; runs an 80%-extraction interview over the user's OWN posts, emails, and decks (never competitor scraping, never an invented persona) and produces the versioned voice record — a per-platform register map (incl. 小红书/微信公众号), banned phrases, per-context disclosure lines (the ECHO C2 upstream), a few-shot bank built exclusively from own posts, and 3-5 content pillars with Estimated %-allocations — submitted via memory/events/channels.ndjson for channel-registry to store as voice-dossier.md, the record every Craft-phase skill reads first. Not for the brand-level voice canon — use brand-language-codifier; not for audience/persona research — use audience-mapper. 声音档案/品牌语气/创始人语气/内容支柱/披露声明
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。
Execute this skill enables AI assistant to perform natural language processing and text analysis using the nlp-text-analyzer plugin. it should be used when the user requests analysis of text, including sentiment analysis, keyword extraction, topic modeling, or ... Use when analyzing code or data. Trigger with phrases like 'analyze', 'review', or 'examine'.
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。
Execute an Adobe PDF Services operation through explicit asset custody, asynchronous status, output verification, and cleanup. Use for approved document conversion, extraction, or generation. Use when the task requires pdf services controlled document job. Trigger with "process PDF with Adobe", "PDF Services job", or "Adobe document automation".
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。
Comprehensive document creation, editing, and analysis with support for tracked changes, comments, formatting preservation, and text extraction. When Claude needs to work with professional documents (.docx files) for: (1) Creating new documents, (2) Modifying or editing content, (3) Working with tracked changes, (4) Adding comments, or any other document tasks
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 AI-powered software with TypeSafe: small units of AI intelligence you can use like programming primitives. Its System One models, including Jev, turn natural language and application state into typed judgments and probabilities that code can combine. Use when a feature needs programmable common sense, when brainstorming what AI could make possible in an app, or when an LLM prompt-and-parse step could become a structured decision. Applications include routing, ranking, extraction, verification, and interactive experiences; these are starting points, not the limits. Read live docs and cookbooks to find useful patterns and discover new combinations.
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。
Web search and content extraction via Brave Search API. Use for searching documentation, facts, or any web content. Lightweight, no browser required.
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 heterogeneous documents and selected URIs to Markdown with Microsoft MarkItDown for text analysis, search, and LLM/RAG ingestion. Covers safe local conversion, streams, Office/PDF/data formats, batch workflows, plugins, vision OCR, Azure extraction, and the official MCP server.
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 Parallel CLI for web search, URL extraction, deep research, structured data enrichment, entity discovery, and recurring web monitoring. Best for requests that explicitly need current web evidence, academic-source discovery, repeated entity lookups, exhaustive reports, or ongoing change tracking.
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。
Path traversal and LFI playbook. Use when file paths, download endpoints, include operations, archive extraction, or wrapper behavior may expose filesystem control.
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。
Memory forensics playbook using Volatility 2/3. Use when analyzing memory dumps for malware analysis, credential extraction, process investigation, code injection detection, and incident response timeline reconstruction.
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。
Traffic analysis and PCAP forensics playbook. Use when analyzing network captures including Wireshark filters, protocol analysis (HTTP/DNS/FTP/SMTP/USB/WiFi), data extraction, covert channel detection, PCAP repair, TLS decryption, and tshark command-line 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。
Steganography detection and extraction playbook. Use when analyzing images (LSB, PNG chunks, JPEG DCT, EXIF), audio (spectrogram, DTMF), files (polyglots, appended data, ADS), and text (whitespace, zero-width, homoglyphs) for hidden data.
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。
Android pentesting playbook. Use when testing Android applications for SSL pinning bypass, exported component abuse, WebView vulnerabilities, intent redirection, root detection bypass, tapjacking, and backup extraction during authorized mobile security assessments.
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Entry P1 category router for file access and upload workflows. Use when testing download endpoints, file paths, local file inclusion, upload flows, preview pipelines, archive extraction, or storage and sharing boundaries.
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。
iOS pentesting playbook. Use when testing iOS applications for keychain extraction, URL scheme hijacking, Universal Links exploitation, runtime manipulation, binary protection analysis, data storage issues, and transport security bypass during authorized mobile security assessments.
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。
Universal AI-powered web scraper for any platform. Scrape data from Instagram, Facebook, TikTok, YouTube, LinkedIn, X/Twitter, Google Maps, Google Search, Google Trends, Reddit, Airbnb, Yelp, and 15+ more platforms. Use for lead generation, brand monitoring, competitor analysis, influencer discovery, trend research, content analytics, audience analysis, review analysis, SEO intelligence, recruitment, or any data extraction task.
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。
Extract, suggest, and sync tags and categories for blog posts across all major CMS platforms. Supports WordPress REST API, Shopify GraphQL, Ghost Content API, Strapi REST/GraphQL, and Sanity GROQ. Generates tag suggestions from content analysis (keyword frequency, heading extraction, semantic grouping), enforces minimum post-count thresholds to prevent thin tag archives, and syncs taxonomy via authenticated API calls. Use when user says "tags", "categories", "taxonomy", "tag suggestions", "sync tags", "WordPress tags", "Shopify tags".
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。
Comprehensive document creation, editing, and analysis with support for tracked changes, comments, formatting preservation, and text extraction. When Claude needs to work with professional documents (.docx files) for: (1) Creating new documents, (2) Modifying or editing content, (3) Working with tracked changes, (4) Adding comments, or any other document tasks
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。
Comprehensive document creation, editing, and analysis with support for tracked changes, comments, formatting preservation, and text extraction. When Claude needs to work with professional documents (.docx files) for: (1) Creating new documents, (2) Modifying or editing content, (3) Working with tracked changes, (4) Adding comments, or any other document tasks
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。