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Analyze incoming user requests to detect intent, request type (design/validate/debug/document), complexity level, and identify vague requirements or buzzwords that need clarification. Use when cto-orchestrator receives new requests that need classification before routing to specialist agents.
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 an "enable observability" card asks for metrics, traces or logs - what the developer task must add per language (Go, Node/TS, Python, Java/Kotlin, .NET, Ruby, PHP), the zero-code agents, the OTEL_* environment variables and resource attributes, and how to reach GCP, AWS, Vercel or a plain OTLP endpoint
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 diff calls a model, exposes a tool to one, feeds it fetched or user content, renders or executes its output, stores agent memory or conversation history, asks a user to approve an agent action, routes MCP tools, or changes agent settings, MCP configs, tool policies or prompt files - including this platform's own agents
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。
Analyze production Agentforce agent behavior using session traces and Data Cloud, and manage Agent Health Monitoring (AHM) alerts. TRIGGER when: user queries STDM session data or Data Cloud trace records; investigates production agent failures, regressions, or performance issues; asks about session traces, conversation logs, or agent metrics; wants to reproduce a reported production issue in preview; runs findSessions or trace analysis queries; creates, lists, updates, or deletes an AHM data alert on an agent metric (escalation rate, deflection, etc.); asks why an alert is not firing or wants to check whether alerts have fired (notification counts, or the per-alert Incidents view). DO NOT TRIGGER when: user creates, modifies, or debugs .agent files during development (use agentforce-generate); writes or runs test specs (use agentforce-test); uses sf agent preview for local development iteration; deploys or publishes agents.
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 for Codex models/pricing, scheduled tasks, skills, settings, setup, troubleshooting, customization, automations, and self-knowledge—including 'you,' 'your,' 'this app,' or 'this coding agent' when they refer to Codex—and for OpenAI APIs/products and ChatGPT Work. Also use for model choice/migration, prompting, SDKs, Responses, Realtime, agents, evals, and Chat/Work/Codex comparisons. Do not use for generic app/software tasks that merely mention Codex.
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。
Run Prettier for the is-kit repository. Use when asked to format or check formatting in this project. Always consult AGENTS.md in the repo root before executing commands.
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 skill for building, migrating, and maintaining Astro v7 projects. Covers best practices from the official AGENTS.md, the v6→v7 migration path, validation of breaking/deprecated patterns, AI-enhanced dev server usage (background mode, JSON logging), advanced routing with src/fetch.ts, route caching, Sätteri Markdown, and the Rust compiler. Use when the user mentions 'Astro', '.astro files', 'astro dev', 'astro build', 'islands architecture', 'content collections', 'SSG', 'SSR adapter', 'upgrade to Astro 7', 'migrate Astro', 'Astro v7', 'Astro v6', 'Sätteri', 'route caching', 'Astro.cache', 'astro dev --background', 'src/fetch.ts', 'advanced routing', 'Hono + Astro', 'Rolldown', 'Vite 8', 'queued rendering', 'CDN cache provider', 'Astro AI', 'related content', 'related posts', 'vector embeddings Astro', 'astro-related-content', or asks about static site generation with Astro.
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。
Sub-skill: Implement MPP Payment Discovery. Publish OpenAPI with x-payment-info extensions so AI agents discover payable endpoints via Machine Payment Protocol (mpp.dev).
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, validate, and enrich Open Knowledge Format (OKF) bundles — the open spec for representing organizational knowledge as markdown files with YAML frontmatter. Use when the user mentions 'OKF', 'Open Knowledge Format', 'knowledge bundle', 'OKF bundle', 'create a knowledge base for agents', 'validate OKF', 'convert to OKF', 'enrich knowledge docs', 'agent-readable knowledge', 'LLM wiki', 'knowledge catalog', 'kcmd', or wants to structure knowledge as markdown files for AI agent consumption. Also use when the user has a directory of markdown files and wants to make them interoperable or conformant with the OKF standard. Even for simple requests like 'make this folder OKF conformant' — the skill has critical structural rules the agent needs.
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。
Orchestrate a multi-model code review: spawn 3 review subagents, merge findings. In PR mode, posts GitHub PR comments with reactions. In local mode, outputs findings directly. CRITICAL: this skill is costly, don't use it unless user explicitly requested to use it.
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 code review using parallel specialized subagents. If a PR URL is provided, fetches PR details and can post comments. If no PR is provided, reviews the diff between the current branch and its base branch plus any uncommitted changes. CRITICAL: this skill is costly, don't use it unless user explicitly requested to use it.
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。
Use when the user asks to initialize a repo, create AGENTS.md, generate contributor guidelines, or set up agent-oriented documentation for a codebase.
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 diagnosing Superloopy doctor, install, wrapper, plugin cache, hook bootstrap, bundled agents, marketplace, Codex, Claude Code, stale-version, evidence-floor, or host-wiring health problems.
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。
Stage-aware prompt coaching, prompt improvement, lookback analysis, prompting habit feedback, and local reports about prompt quality for AI coding agents such as Claude Code or Codex.
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。
Maintain the harness memory files (USER.md, MEMORY.md, AGENTS.md) so that long-term knowledge persists across 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。
Browser automation CLI for AI agents. Use when needs to interact with websites, including navigating pages, filling forms, clicking buttons, taking screenshots, extracting data, testing web apps, or automating any browser task. TRIGGER when user requests to "open a website", "fill out a form", "click a button", "take a screenshot", "debug this in browser", "scrape data from a page", "test this web app", "login to a site", "frontend UI/UX aesthetics", "automate browser actions", or any task requiring programmatic web 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。
Choose bounded implementation scope and existing owning modules before Guardrails runtime, configuration, client, resource, streaming, Agents, or SDK compatibility changes and feedback fixes.
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 writing Japanese novels with AI coding agents (Hermes, Claude Code, opencode, goose). File-based, agent-agnostic workflow: settings in TOML, deterministic context packs, validation scripts.
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。
Expert guide for Ultra-Low Latency Conversational Voice AI (<300ms), WebRTC bidirectional streaming, OpenAI Realtime API, Gemini Multimodal Live Audio, LiveKit Agents, and Semantic VAD / Panduan ahli AI suara percakapan real-time berlatensi ultra-rendah.
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。
Expert guide for designing and orchestrating multi-agent systems, agent swarms, 2026 Anthropic agentic design patterns, graph-based workflows (LangGraph, OpenAI Agents SDK, Google ADK, Mastra.ai), shared state memory, and human-in-the-loop guardrails in English and Indonesian.
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。
Expert guide for integrating Large Language Models (LLMs), Model Context Protocol (MCP v1.x), hybrid reasoning models, RAG architecture, vector databases, and AI agents / Panduan ahli untuk integrasi LLM, Model Context Protocol (MCP), model hybrid reasoning, arsitektur RAG, vector database, dan agen AI.
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。
Expert guide for autonomous web agents (Browser-Use, Stagehand v0.4+), hardcore anti-bot evasion (Playwright Stealth, WebGL masking), and Vision LLM visual QA / Panduan ahli agen web otonom, penghindaran deteksi bot, dan QA visual berbasis Vision LLM.
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。
Expert guide for long-term episodic memory integration (Mem0 v2, Letta/MemGPT, Zep v2), memory tier architecture, pgvector HNSW storage, and unified context management for autonomous AI agents / Panduan ahli untuk integrasi memori episodik jangka panjang (Mem0 v2, Letta/MemGPT, Zep v2), arsitektur tier memori, penyimpanan pgvector HNSW, dan manajemen konteks terpadu untuk agen AI otonom.
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。