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Detects AI-generated deepfake audio used in voice phishing (vishing) attacks by extracting spectral features (MFCC, spectral centroid, spectral contrast, zero-crossing rate) and classifying samples with machine learning models. Supports batch analysis of audio files, generates confidence scores, and produces forensic reports. Activates for requests involving deepfake voice detection, vishing investigation, AI-generated speech analysis, voice cloning detection, or audio authenticity verification.

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。

Analyzes RAM memory dumps from compromised systems using the Volatility framework to identify malicious processes, injected code, network connections, loaded modules, and extracted credentials. Supports Windows, Linux, and macOS memory forensics. Activates for requests involving memory forensics, RAM analysis, volatile data examination, process injection detection, or memory-resident malware investigation.

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。

Traces ransomware cryptocurrency payment flows using blockchain analysis tools such as Chainalysis Reactor, WalletExplorer, and blockchain.com APIs. Identifies wallet clusters, tracks fund movement through mixers and exchanges, and supports law enforcement attribution. Activates for requests involving ransomware payment tracing, bitcoin wallet analysis, cryptocurrency forensics, or blockchain intelligence gathering.

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。

Control Claude Code via MCP protocol. Trigger with "plan" to write a precise execution plan then feed it to Claude Code. Also supports direct commands, persistent sessions, agent teams, and advanced tool 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。

Computer Use Agent — autonomous browser control via Steel, Browser Use, and Browserbase cloud providers. Supports headful CDP sessions, CAPTCHA solving, proxy rotation, live view, session recording/replay, Playwright/Puppeteer integration, and LLM-driven visual browsing. Use when asked about computer use, browser automation, web scraping, Steel sessions, cloud browsers, headful browsing, CAPTCHA solving, or autonomous web navigation.

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。

E2B Desktop Sandbox with autonomous computer use — cloud Linux environments for browser automation, visual UI grounding, screenshot analysis, and shell execution. Supports Browser Use cloud sessions, Playwright integration, and LLM-driven desktop control. Use when asked about sandboxes, computer use, browser automation, E2B, desktop control, visual testing, or cloud Linux environments.

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。

CLI to manage emails via IMAP/SMTP. Use `himalaya` to list, read, write, reply, forward, search, and organize emails from the terminal. Supports multiple accounts and message composition with MML (MIME Meta Language).

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 PDF documents to clean structured Markdown for LLM context. Supports two modes: fast (PyMuPDF) and accurate (IBM Docling TableFormer AI). Features aggressive persistent caching, image extraction with metadata, table detection, and batch processing. Use when asked to convert PDFs, extract PDF content, parse documents, or prepare PDF data for AI/LLM consumption.

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。

Manage Apple Reminders via remindctl CLI (list, add, edit, complete, delete). Supports lists, date filters, and JSON/plain output.

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 creating RFQ (Request for Quotation) documents for software development projects to send to vendors. Use this skill when you have received vague requirements from clients and need to structure them into clear, comprehensive RFQs that enable vendors to provide accurate estimates. Supports Japanese (default) and English, with systematic requirements elicitation, clarification, and markdown-formatted output.

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 creating cost estimates and quotations for software development projects. Use this skill when you have an RFQ (Request for Quotation), project requirements, or a project description and need to create a comprehensive estimate with WBS, effort calculations, cost breakdowns, and ROI analysis. Supports Japanese (default) and English, with systematic work breakdown, effort estimation, and markdown-formatted estimate documents.

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 QR code images from text, URLs, or contact information using Python's qrcode library. Supports customization (size, margin, color, error correction) and batch 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。

Correlate events across multiple log files from different sources, systems, or time periods. Build unified timelines, identify causal relationships between events, and highlight anomalies that span multiple data sources. Supports timezone normalization and gap detection. Use when analyzing logs from multiple systems (frontend, backend, database, infrastructure), investigating distributed system failures, or building event timelines across services. Triggers: "correlate these logs", "build timeline from multiple logs", "cross-system analysis", "find related events across logs", "multi-source log analysis", "distributed trace analysis"

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This skill should be used when creating bug/defect reports during system testing. Use this skill when you discover a bug, need to document test failures, or want to create comprehensive bug tickets with proper reproduction steps, severity assessment, and environment details. Guides users through interactive questioning to gather all necessary information and generates professional bug ticket documents in Markdown format. Supports Japanese (default) and English.

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。

Review audit-related documents (control design documents, bottleneck analyses, requirements definitions, etc.) for quality, scoring them 0-100 with a severity-rated findings list. Use when reviewing audit documents, checking control design quality, or verifying cross-document consistency. Supports documents governed by US GAAP, IFRS, or J-GAAP.

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。

Generic helpdesk first-response skill for creating KB-based response drafts. Use when handling support tickets, creating response templates, or building a structured helpdesk workflow. Supports error code detection, keyword matching, confidence scoring, multi-language templates, and escalation workflows. Customize by providing your own KB articles and configuration.

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 data science workflow skill for analyzing tabular and time series data. Use when conducting exploratory data analysis, building predictive models, performing feature engineering, or generating data-driven insights. Supports end-to-end analysis from data understanding through model deployment, following industry best practices for regression, classification, time series forecasting, and statistical 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。

Ultra-compressed communication mode. Cuts output tokens 65% (measured) by speaking like caveman while keeping full technical accuracy. Supports intensity levels: lite, full (default), ultra, wenyan-lite, wenyan-full, wenyan-ultra. Use when user says "caveman mode", "talk like caveman", "use caveman", "less tokens", "be brief", or invokes /caveman. Also auto-triggers when token efficiency is requested.

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。

Export Codex rollout/session JSONL as a self-contained HTML report that can be opened offline. Use when a user provides a complete session file path or Codex session ID, or asks to export a Codex session to HTML, generate a session report, or convert a rollout to HTML. Supports exact full-ID lookup in the CODEX_HOME sessions and archived_sessions directories.

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。

TanStack Router bundler plugin for route generation and automatic code splitting. Supports Vite, Webpack, Rspack, and esbuild. Configures autoCodeSplitting, routesDirectory, target framework, and code split groupings.

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。

Route planning from A to B via OSRM (Open Source Routing Machine). Supports car, bicycle and pedestrian. No API key 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。

Use when locating TBQ3 documentation, function references, examples, or source docs under docs without loading the full corpus; supports rg-based search and curated topic/function indexes.

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。

Quality gate immediately after building: prove that what was just built actually works, while the context is still hot. Six steps: scope what changed, run live on real data, break it on purpose, show a counter or log proving it ran, root-cause any failure and re-run, then a verdict with evidence. Only a green verdict earns the word done. Supports an external multi-vendor review panel as a mid-step. Triggers: "/tt", "/test", "prove it works", "test what we built".

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。

Get a second opinion from an external LLM at three checkpoints: T1 is the plan valid, T2 which path at a fork, T3 finish plus a QA breaker that tries to break the result. One structured move per exchange (PROPOSE, COUNTER, VERIFY, ACCEPT, BLOCK) with memory across turns, and every exchange mirrored into a human-visible channel. Supports a multi-vendor panel instead of a single reviewer. Triggers: "/secondop", "get a second opinion", "run the review panel".

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。