「agentic ai」の検索結果

Auto-assembles a domain-specific expert panel (7–10 experts), scores any content or strategy artifact against a typed rubric, and iterates until the aggregate hits 90+ (max 3 rounds). Use as a quality gate on copy, email sequences, landing-page drafts, strategy docs, charts, titles, or recruiting evaluations — or when another skill needs a final review gate on its output. Triggers on "expert panel this", "score this", "rate these variants", "quality check this", "panel review", "expert score", "evaluate this copy/strategy/page". For variant generation and multi-round conversion optimization see autoresearch; for live-URL CRO auditing see conversion-ops; for the scripted content-production pipeline see content-pipeline.

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Use to structure a vague marketing request into a well-scoped brief before any real work begins. Triggers on "shape this campaign", "plan this launch", "scope this content", "marketing plan", "growth plan", "content strategy", "outbound plan", or when invoked as the /mshape slash command. Produces a filled brief (campaign, content, optimization, research, or pipeline) that downstream marketing skills can act on. Do not use for work already well-defined — go straight to execution. For engineering task shaping see prompt-shaper; for game-design intake see game-design-shaper.

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Coordinates release preparation for your monorepo — maintaining CHANGELOG and the release assessment document, resolving merge conflicts on release branches, and communicating status, risk, and asks to the broader team. Use when the user mentions release manager, release train, cut a release, release branch, CHANGELOG, release assessment, monorepo release, merge conflicts during release, versioning, or coordinating a version bump with engineering and stakeholders.

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Generate llms.txt files from project documentation following the llmstxt.org spec. Use when asked to create AI-friendly documentation indexes, generate llms.txt, or make a project discoverable by AI assistants.

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Data structures and serialization formats for agent-to-agent communication. Covers message envelopes, structured output schemas, capability declarations, task handoff payloads, error/retry signaling, and context windows as data structures. Deep comparison of A2A protocol, MCP, OpenAI function calling, and LangChain message types. Teaches when to use rigid schemas vs free-form with validation, typed vs untyped, streaming vs batch. Activate on: "agent message format", "agent communication schema", "agent-to-agent protocol", "A2A protocol", "MCP message format", "structured output for agents", "agent interop", "interchange format", "agent serialization", "task handoff format", "capability declaration". NOT for: what agents say to each other (use agent-conversation-protocols), orchestration topology (use multi-agent-coordination), building agent infrastructure (use agentic-infrastructure-2026).

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Expert Antigravity Code (Gemini IDE, développement agentic, IA-assisté, workflows)

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Browser automation powers web testing, scraping, and AI agent interactions. The difference between a flaky script and a reliable system comes down to understanding selectors, waiting strategies, and anti-detection patterns. This skill covers Playwright (recommended) and Puppeteer, with patterns for testing, scraping, and agentic browser control. Key insight: Playwright won the framework war. Unless you need Puppeteer's stealth ecosystem or are Chrome-only, Playwright is the better choice in 202

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Focused Signals scout for PostHog projects running Replay Vision scanners — the standing LLM probes that watch session recordings and write `$recording_observed` events. Watches two promises: that enabled scanners are actually observing (throughput / success-rate cliffs, exhausted quota — a silent watch gap), and that what the scanners see in aggregate gets surfaced (a monitor's `yes`-rate or a scorer's score stepping away from its own baseline, a classifier tag or a recurring summarizer theme concentrating across many sessions). It is the agentic pull complement to the per-session push path: scanners with `emits_signals` already emit one signal per session into this same inbox, so this scout never repeats them — it adds the cross-session shape the per-session probe can't see. Emits findings only when they clear the confidence bar; otherwise writes durable memory and closes out empty. Self-contained peer in the signals-scout-* fleet.

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Spawns pedestrians that wander autonomously via WalkerAIController, and destroys them in the correct order. Pairs each walker.pedestrian.* with a controller.ai.walker, places them on the navigation mesh, and sends them to random navmesh points at walking speed. Use when the user asks to "spawn pedestrians/walkers", "add people/crowds", "make pedestrians walk around", or "remove the walkers". Reads the world settings first: if the world is already synchronous it never ticks it, and if it is asynchronous it makes it synchronous and drives the clock itself.

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Azure Container Registry SDK for Python. Use for managing container images, artifacts, and repositories.

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Build AI applications on Microsoft Foundry using the azure-ai-projects SDK.

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Azure AI Voice Live SDK for JavaScript/TypeScript. Build real-time voice AI applications with bidirectional WebSocket communication.

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Build anomaly detection applications with Azure AI Anomaly Detector SDK for Java. Use when implementing univariate/multivariate anomaly detection, time-series analysis, or AI-powered monitoring.

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Azure AI Projects SDK for .NET. High-level client for Azure AI Foundry projects including agents, connections, datasets, deployments, evaluations, and indexes.

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Azure AI VoiceLive SDK for Java. Real-time bidirectional voice conversations with AI assistants using WebSocket.

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Azure AI Projects SDK for Java. High-level SDK for Azure AI Foundry project management including connections, datasets, indexes, and evaluations.

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。

Azure AI Voice Live SDK for .NET. Build real-time voice AI applications with bidirectional WebSocket communication.

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Azure AI Agents Persistent SDK for Java. Low-level SDK for creating and managing AI agents with threads, messages, runs, and tools.

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Azure AI Agents Persistent SDK for .NET. Low-level SDK for creating and managing AI agents with threads, messages, runs, and tools.

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Master proven backend architecture patterns including Clean Architecture, Hexagonal Architecture, and Domain-Driven Design to build maintainable, testable, and scalable systems.

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Geracao de imagens humanizadas via Google AI Studio (Gemini). Fotos realistas estilo influencer ou educacional com iluminacao natural e imperfeicoes sutis.

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Design spec with 98 rules for building CLI tools that AI agents can safely use. Covers structured JSON output, error handling, input contracts, safety guardrails, exit codes, and agent self-description.

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AI agent development workflow for building autonomous agents, multi-agent systems, and agent orchestration with CrewAI, LangGraph, and custom agents.

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AI-powered presentation generation via the 2slides API — create slides from text, match a reference image style, summarize documents into decks, add AI voice narration, and export pages/audio. Use for any "make slides", "create a deck", or "slides from this document" request.

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。