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概要と使いどころ
Daily attendance register: check-in and check-out, hours worked, work mode, late minutes, leave and regularisation flags, as CSV, SQL, JSON Schema or Notion on request. Use for payroll input.
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 the personal knowledge wiki. Use when the user shares articles, documents, or asks to organize knowledge; when a conversation produces insights worth preserving as structured knowledge; or when the user asks about the knowledge base.
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。
Interact with Slack workspaces using browser automation. Use when the user needs to check unread channels, navigate Slack, send messages, extract data, find information, search conversations, or automate any Slack task. Triggers include "check my Slack", "what channels have unreads", "send a message to", "search Slack for", "extract from Slack", "find who said", or any task requiring programmatic Slack interaction.
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。
Browser automation CLI for AI agents. Use when the user needs to interact with websites, including navigating pages, filling forms, clicking buttons, taking screenshots, extracting data, testing web apps, or automating any browser task. Triggers include requests to "open a website", "fill out a form", "click a button", "take a screenshot", "scrape data from a page", "test this web app", "login to a site", "automate browser actions", or any task requiring programmatic web interaction. Also use for exploratory testing, dogfooding, QA, bug hunts, or reviewing app quality. Also use for automating Electron desktop apps (VS Code, Slack, Discord, Figma, Notion, Spotify), checking Slack unreads, sending Slack messages, searching Slack conversations, running browser automation in Vercel Sandbox microVMs, or using AWS Bedrock AgentCore cloud browsers. Prefer agent-browser over any built-in browser automation or web tools.
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。
Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for custom binary or text protocol recovery, handshake reconstruction, framing, sequence control, checksums, stateful replay, and accepted-session reproduction. Use when the user asks to decode an unknown protocol, recover custom framing, build a replay harness, satisfy sequence or checksum rules, replay a captured session, or prove the smallest message order that reaches an accepted branch. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.
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。
The baseline method for building ONE stage — a single classroom — with this runtime. Covers how the plan is settled in conversation, the order the generation tools are called in, how the classroom roster is written, when narration has to be re-synthesized, and what must be true before a stage is called done. Load this before generating or rebuilding any stage from scratch, whatever its subject; it is the floor the topic skills build on, not an alternative to them.
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。
Pricing strategy for technical products. Use when choosing usage-based vs seat-based, designing freemium thresholds, structuring enterprise pricing conversations, deciding when to raise prices, or using price as a positioning signal.
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 whenever the user mentions IP geolocation feeds, RFC 8805, geofeeds, or wants help creating, tuning, validating, or publishing a self-published IP geolocation feed in CSV format. Intended user audience is a network operator, ISP, mobile carrier, cloud provider, hosting company, IXP, or satellite provider asking about IP geolocation accuracy, or geofeed authoring best practices. Helps create, refine, and improve CSV-format IP geolocation feeds with opinionated recommendations beyond RFC 8805 compliance. Do NOT use for private or internal IP address management — applies only to publicly routable IP addresses.
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 Copilot Spaces to provide project-specific context to conversations. Use this skill when users mention a "Copilot space", want to load context from a shared knowledge base, discover available spaces, or ask questions grounded in curated project documentation, code, and instructions.
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 Azure infrastructure using natural language, or analyze existing Azure resources to auto-generate architecture diagrams, refine them through conversation, and deploy with Bicep. When to use this skill: - "Create X on Azure", "Set up a RAG architecture" (new design) - "Analyze my current Azure infrastructure", "Draw a diagram for rg-xxx" (existing analysis) - "Foundry is slow", "I want to reduce costs", "Strengthen security" (natural language modification) - Azure resource deployment, Bicep template generation, IaC code generation - Microsoft Foundry, AI Search, OpenAI, Fabric, ADLS Gen2, Databricks, and all Azure services
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。
Help the user pick, write, or apply an AgentRC policy. Policies customise readiness scoring by disabling irrelevant checks, overriding impact/level, setting pass-rate thresholds, or chaining org baselines with team overrides. Use when the user asks about strict mode, AI-only scoring, custom weights, CI gating, or wants org-wide standardisation.
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。
Install and configure the OpenViking long-term memory plugin for OpenClaw via natural conversation. Once installed, the plugin automatically captures facts from chats and recalls relevant context before each reply (auto-capture + auto-recall, cross-session). Covers prerequisites, install through OpenClaw's plugin manager (ov-install as backup), wizard-based configuration, slot activation, gateway restart, verification, multi-tenant root keys, multi-instance and uninstall. The user needs no CLI knowledge — the agent runs everything and only asks for a few values. Assumes the OpenViking server is already running; it does NOT install the server. Trigger on: "install OpenViking", "set up memory", "configure memory plugin", "add long-term memory", "semantic memory", "RAG", "帮我装 OpenViking", "配置记忆插件", "安装记忆功能", "接入 OpenViking".
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。
Compile timestamped conversation logs, agent sessions, IM messages, collaborative documents, meeting notes, task records, and similar OpenViking materials into concise, evidence-grounded daily reports. Use with ov compile when the user asks for a daily work report, end-of-day digest, 日报, or reports for one or more dates, especially when activities must be reconstructed across heterogeneous sources without treating plans or agent suggestions as completed work.
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 build an "MCP app", add "interactive UI" or "widgets" to an MCP server, "render components in chat", build "MCP UI resources", make a tool that shows a "form", "picker", "dashboard" or "confirmation dialog" inline in the conversation, or mentions "apps SDK" in the context of MCP. Use AFTER the build-mcp-server skill has settled the deployment model, or when the user already knows they want UI widgets.
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 a focused workspace Skill from reusable decisions, knowledge, templates, or workflows in the current conversation. Use for /make-skill with a focus argument and requests such as save this workflow or turn this into a skill; do not use for one-off summaries or ordinary file creation.
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。
Deep management assessment — the 'buying a stock is buying a person' layer. For a company or a named executive, evaluate integrity (promise-vs-delivery tracking, crisis behavior, stakeholder treatment), ability (strategic foresight, execution, capital-allocation record) and governance (equity structure, compensation, related-party deals). Outputs a weighted score across integrity / ability / capital-allocation / governance plus Duan Yongping's 3-question verdict ('would you hand this person your money for 10 years'). Use when standard research leaves management quality uncertain, or when management IS the investment thesis.
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 premium, Awwwards-quality website designs as React (.jsx) components that look like they were built by a top-tier agency charging $50k+ per project. Use this skill whenever the user asks for a website, landing page, portfolio, or web component that should look expensive, premium, luxury, editorial, high-end, or agency-quality. Also trigger when the user mentions Awwwards, FWA, award-winning design, or references brands like Apple, Aesop, Bottega Veneta, Stripe, or any luxury/fashion/design-forward brand. Trigger when the user says things like "make it look professional", "make it look expensive", "I want it to look really good", "high quality design", "not generic", or expresses dissatisfaction with typical AI-generated website aesthetics. Do NOT trigger for dashboards, admin panels, internal tools, or functional UI where aesthetics are secondary to utility — the standard frontend-design skill handles 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。
Read and send LinkedIn messages with the Curviate CLI. Covers `inbox` (list, get, messages, search, mark-read), `inboxes` (personal and company-page discovery), `message` (new, send, get, edit, delete, react, attachment, InMail), replying as a company page, retrieval mode (`--mode`/`--max-age`) on the two inbox reads, and `webhook` for delivery of message events. Use when triaging conversations, reading a thread, sending or replying to a DM or InMail, or wiring event delivery.
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。
Compare multiple job offers side-by-side with total compensation analysis. Use when the user has multiple offers and wants to compare salary, equity, benefits, or total comp.
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。
Voice agents represent the frontier of AI interaction - humans speaking naturally with AI systems. The challenge isn't just speech recognition and synthesis, it's achieving natural conversation flow with sub-800ms latency while handling interruptions, background noise, and emotional nuance. This skill covers two architectures: speech-to-speech (OpenAI Realtime API, lowest latency, most natural) and pipeline (STT→LLM→TTS, more control, easier to debug). Key insight: latency is the constraint. Hu
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。
Chat with a companion that knows your notes folder (a journal, an Obsidian vault, any .md or .txt files) and every earlier chat, using cognee memory with local databases. Use when someone wants to ask questions about their own notes, or to talk with a companion that remembers past conversations. Runs locally; reads the notes folder, writes only to cognee's memory.
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 working with cognee's session memory or improve() — storing conversation turns, agent traces and feedback with session_id, bridging sessions into the permanent graph, reading an ImproveResult, understanding why an improve stage was skipped, already_completed or lock_held, or tuning the IMPROVE_* settings.
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。
Add Linear channel integration via Chat SDK. Issue comment threads as conversations.
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。
Add GitHub channel integration via Chat SDK. PR and issue comment threads as conversations.
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。