142 件 ・ 関連度順
概要と使いどころ
Define the smallest viable experiment and MVP for a selected one-person company opportunity. Use when Codex needs to explain what MVP means when needed, verify prerequisites, ask one question at a time, present multiple MVP options, and write user-confirmed outputs into `opc-doc/`.
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 the operating health of a one-person company using lightweight metrics, bottleneck analysis, and stop-loss logic. Use when Codex needs to explain review concepts when needed, verify available outputs, ask one question at a time, present multiple bottleneck hypotheses, and write user-confirmed outputs into `opc-doc/`.
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。
Design a viable business model for a one-person company using Lean Canvas and a simplified Business Model Canvas. Use when Codex needs to explain the business-model concepts when needed, verify niche and value-proposition prerequisites, ask one question at a time, present multiple model choices, and write user-confirmed outputs into `opc-doc/`.
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。
Design a conversion loop for a one-person company from reach to lead capture to purchase. Use when Codex needs to explain conversion concepts when needed, verify MVP prerequisites, ask one question at a time, present multiple conversion-path options, and write user-confirmed outputs into `opc-doc/`.
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。
Windows アプリ開発 (Win32 / WPF / WinForms 相互運用) の相互運用・近代化リファレンス。 Win32 API, CreateWindowExW, WNDPROC, メッセージループ, COM (IUnknown, CoCreateInstance, ComPtr), CsWin32, windows-rs, C++/WinRT (winrt::com_ptr, IAsyncOperation, IDL, ABI 相互運用), C#/WinRT (cswinrt, ComWrappers, AOT/trimming, WindowNative), XAML Islands (DesktopWindowXamlSource, DesktopChildSiteBridge, WindowsXamlManager), WPF/WinForms 相互運用 (HwndHost, HwndSource, WindowsFormsHost, ElementHost), UWP から Windows App SDK / WinUI 3 への 移行 (namespace mapping, AppWindow, DispatcherQueue, .NET Upgrade Assistant)。
Microsoft 365 / Entra ID red-team attack chain — current 2026 reality. AADSTS code reference, user enumeration vectors (with hardening status), Smart Lockout math, Conditional Access bypass options, ROPC + SAML SSO browser flow, Burp/Playwright templates. Built from authorized red-team work where ROPC spray surfaced pre-existing lockouts and CA-blocked credentials, plus real-time external attacker activity correlation. Use for any M365/Entra credential attack, password spray, user enumeration, CA-bypass exploration, or active-attacker-detection scenario.
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。
Reverse JavaScript-based custom DSL/VM interpreters, non-standard WASM-like runtimes, and risk-control engines. Use when analyzing IIFE or switch-based opcode dispatchers, extracting instruction tables, recovering bytecode semantics, capturing VM state at runtime, or reconstructing execution flow.
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。
Deploys anomaly detection for OT/ICS environments using machine learning on OT network baselines, physics-based process models, and Modbus/DNP3/OPC UA traffic analysis to flag deviations, rogue devices, and mismatches against historian data. Use for continuous OT monitoring, baselining deterministic SCADA polling, or investigating alerts from Nozomi Guardian/Dragos needing deeper protocol 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。
Planificación persistente basada en archivos para tareas multipaso de agentes de IA. Mantiene task_plan.md, findings.md y progress.md en disco; los hooks del ciclo de vida inyectan contexto seleccionado de planificación del proyecto. La recuperación automática solo lee los archivos de planificación del proyecto. session-catchup.py --metadata, solicitado de forma explícita, puede inspeccionar metadatos locales de sesiones del mismo proyecto; --replay puede emitir extractos limitados y enmarcados con nonce. El modo con gate opcional solo puede solicitar que el host continúe si este lo admite y nunca ejecuta comandos declarados en Markdown. El skill no tiene ninguna ruta de carga por red. Úsalo para investigación o trabajo que requiera 5 o más llamadas a herramientas.
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。
IoT and embedded device security testing methodology. Covers hardware reconnaissance (UART, JTAG, SWD, SPI flash, I2C EEPROM, eMMC chip-off), firmware acquisition (vendor portals, OTA capture, flash dump, binwalk extraction), firmware analysis (filesystem mounting, binary triage, hardcoded secrets, default credential discovery), bootloader attacks (U-Boot console, secure-boot bypass, fault injection), runtime attacks on embedded Linux/RTOS (busybox CVEs, MTD writes, /dev/mem), wireless protocol attacks (Zigbee, BLE, Z-Wave, LoRaWAN, Thread/Matter, sub-GHz), MQTT/CoAP/Modbus/BACnet/OPC-UA exploitation, mobile companion app analysis, cloud-IoT API abuse, and side-channel/glitching basics. Use for IoT pentest, smart-home assessment, ICS/OT testing, or embedded vulnerability research.
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。
Approve a GitHub contributor to open PRs in get-bb/bb, then land the allowlist change with SlopCop and CI skipped. Use for requests such as "add @username to be able to open PRs" or an explicit add-contributor invocation.
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 this skill when the user is dropping below the higher-level DOCA libraries (doca-rdma / doca-eth / doca-rmax) into the raw-verbs escape hatch — managing QP / CQ / PD / MR / SRQ / AH / CC-group / Ethernet-SQ-RQ primitives inside DOCA Core, porting libibverbs code into the DOCA Core model, capability-querying a specific verb / opcode / WR flag / QP attribute via doca_verbs_query_device, or debugging DOCA_ERROR_* from doca_verbs_* calls. Trigger even when the user does not say "doca-verbs" — implicit phrasings include "raw QP attribute the task API doesn't expose", "keep my ibv_* code next to doca_* on the same QP", "IO_FAILED on WR submit", "QP state transition rejected", "attach a congestion- control group", or "porting my libibverbs code". The skill's first job is to route MOST users back UP to the higher-level library. Refuse and route elsewhere for general doca-rdma / doca-eth / doca-rmax workloads, DOCA install, Core internals, and general libibverbs theory — those belong to other skills.
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 domains, compare prices, find promo codes, get purchase recommendations. Use when user wants to buy a domain, check domain prices, find domain deals, compare registrars, or search for .ai/.com domains.
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。
Create logos using AI image generation. Discuss style/ratio, generate variations, iterate with user feedback, crop, remove background, and export as SVG. Use when user wants to create a logo, icon, favicon, brand mark, mascot, emblem, or design a logo.
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。
Archive session learnings, debugging solutions, and deployment logs to .archive/yyyy-mm-dd/ as indexed markdown with searchable tags. Use when completing a significant task, resolving a tricky bug, deploying, or when the user says "archive this". Maintains .archive/MEMORY.md index for cross-session knowledge reuse.
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 and edit images using Google Gemini 3 Pro Image (Nano Banana Pro). Supports text-to-image, image editing, various aspect ratios, and high-resolution output (2K/4K). Use when user wants to generate images, create images, use Gemini image generation, or do AI 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。
SEO & GEO (Generative Engine Optimization) for websites. Analyze keywords, generate schema markup, optimize for AI search engines (ChatGPT, Perplexity, Gemini, Copilot, Claude) and traditional search (Google, Bing). Use when user wants to improve search visibility.
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 and retrieve content from Product Hunt. Get posts, topics, users, and collections via the GraphQL API. Use when user mentions Product Hunt, PH, or product launches.
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 and retrieve content from Reddit. Get posts, comments, subreddit info, and user profiles via the public JSON API. Use when user mentions Reddit, a subreddit, or r/ links.
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 user demand research reports from real user feedback. Scrape and analyze feature requests, complaints, and questions from Reddit, X, GitHub, YouTube, LinkedIn, and Amazon. Use when user wants to do demand research, find feature requests, analyze user demand, or run RequestHunt queries.
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。
SEO & GEO (Generative Engine Optimization) for websites. Analyze keywords, generate schema markup, optimize for AI search engines (ChatGPT, Perplexity, Gemini, Copilot, Claude) and traditional search (Google, Bing). Use when user wants to improve search visibility, search optimization, search ranking, AI visibility, ChatGPT ranking, Google AI Overview, indexing, JSON-LD, meta tags, or keyword research.
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。
Create banners using AI image generation. Discuss format/style, generate variations, iterate with user feedback, crop to target ratio. Use when user wants to create a banner, header, hero image, cover image, GitHub banner, Twitter header, or readme banner.
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。
Clear description of what this skill does and when to use it. Include trigger keywords and contexts inline, e.g. "Use when user wants to X, Y, or Z."
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 and retrieve content from Twitter/X. Get user info, tweets, replies, followers, communities, spaces, and trends via twitterapi.io. Use when user mentions Twitter, X, or tweets.
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。