「agentic ai」の検索結果

Use when building durable AI agents or agentic workflows with Inngest and AgentKit, including model calls, tool calls, multi-agent networks, human approval, realtime progress, provider rate limits, crash-safe execution, and Agent Evals handoff. Covers AgentKit, `step.ai`, `step.run`, `step.waitForEvent`, native realtime, and when to use lower-level Inngest primitives instead of an in-memory agent loop. Use `inngest-agent-evals` with this skill when the user wants scoring, sessions, experiments, deferred scorers, or outcome-based evaluation for the agent.

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Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then guards against regression. Use when asked to make a route's navigation instant (its static shell commits immediately), fix a route whose static shell isn't prerendered/served/prefetched, grow a route's static shell or fix its slow first paint, diagnose which Suspense boundary keeps a route out of its static shell, or write the instant() e2e guard for one. Requires Next.js 16.3+ with cacheComponents; directs an upgrade if older.

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AWS Bedrock AgentCore comprehensive expert for deploying and managing AI agents at scale. Use when working with any AgentCore service including Gateway, Runtime, Memory, Identity, Code Interpreter, Browser, Observability, Agent Registry, or Evaluations. Covers agent deployment, MCP tool integration, credential management, agent discovery, governance workflows, and automated quality assessment. Essential when user mentions AgentCore, agent runtime, agent registry, agent evaluation, MCP gateway, deploy agent, register MCP server, discover agents, evaluate agent quality, agent credentials, or wants to build, deploy, catalog, or monitor AI agents on AWS.

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End-to-end AI video production skill for agentic frameworks. Use when the user asks to make, edit, repurpose, caption, soundtrack, or export videos using HeyGen avatars, Seedance or ByteDance b-roll, OpenAI image generation and editing, AI UGC ads, Remotion, HyperFrames, screen recordings, FFmpeg, captions, ElevenLabs music, Suno, or social-video variants.

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Plan, implement, or review agentic eval work in an existing codebase with compatibility, security, and verification controls. Use when the user explicitly requests agentic eval work.

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Build AI agents that automate scientific research — hypothesis generation, experiment design, data analysis, and paper writing using agentic tree search. Use when: automating research workflows, generating and testing hypotheses, building AI-powered research assistants.

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AI Manipulation Defense System implementation with Midstream, AgentDB, and lean-agentic

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Behavioral guardrails to reduce common LLM coding mistakes with a caution-first approach.

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Sub-skill: Implement ARD (Agentic Resource Discovery). Publish /.well-known/ai-catalog.json so agents can discover MCP servers, A2A agents, skills and API tools (agenticresourcediscovery.org / ai-catalog).

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Research a topic to support a decision, three ways: draft a research prompt for the user to run in their own tool (ChatGPT, Gemini, Grok, Perplexity, …), turn a finished research report into a short summary with cited sources that other skills can use directly, or run the research here with parallel web searches. Built-in research types: market, domain, technical, competitive, user-voice, academic-lit; also supports choosing between candidates, and custom types via overrides. Use when the user says "deep recon", "research this", "draft a research prompt", "process this research report", "market research", "domain research", "technical research", "competitor research", "literature review", or "help me choose between"

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Runs the evals of a BMad skill through the agent harness the user works in and reports what they show: baseline against the bare model, variant against a stripped or prior version, quality against a rubric, and trigger accuracy of the description with an optional optimization loop. Use when the user asks to evaluate, benchmark, test the triggers of, grade, or compare versions of a skill, or when the bmad-toolsmith skill asks for an eval of a skill it built.

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Facilitate a brainstorming session using diverse creative techniques. Use when the user says 'help me brainstorm' or 'help me ideate'

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AI Agent Architect: Design agent systems: single vs multi-agent, tools and MCP, memory, guardrails, evals.

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Create high-quality, model-aware system prompts for any LLM (Claude, GPT, Gemini, open-source, etc.). Use this skill whenever the user wants to create, write, build, design, draft, or improve a system prompt, system instructions, or custom instructions for any AI model. Also trigger when the user asks about prompt engineering, prompt design, prompt optimization, or wants to define behavior for an AI assistant, chatbot, agent, or any LLM-powered application — even if they don't explicitly say "system prompt". Covers all use cases including chatbots, agentic systems, tool-use workflows, content generation, data extraction, code assistants, and multi-turn conversations.

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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.

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Expert guide for DOKU Model Context Protocol (MCP) Server integration. Enables AI Agentic Commerce with tools for DOKU Checkout, payment links, Virtual Accounts, QRIS, and order status checks / Panduan ahli DOKU MCP Server untuk AI Agentic Commerce.

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Design and implement agentic AI workflows and patterns. Covers ReAct, planning agents, tool use, memory systems, and multi-agent orchestration. Use when building autonomous AI agents, implementing complex task automation, or designing intelligent workflow systems.

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Batteries-included agent component for React/Next.js from ui.inference.sh. One component with runtime, tools, streaming, approvals, and widgets built in. Capabilities: drop-in agent, human-in-the-loop, client-side tools, form filling. Use for: building AI chat interfaces, agentic UIs, SaaS copilots, assistants. Triggers: agent component, agent ui, chat agent, shadcn agent, react agent," agentic ui, ai assistant ui, copilot ui, inference ui, human in the loop

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Build a plug-in-ready SDR operator package for sales development and outbound pipeline work: ICP definition, compliant lead-source plan, enrichment/research workflow, account scoring, outbound assets, CRM handoff, reply triage, meeting prep, approval gates, and loop memory. Use when "SDR", "sales development", "outbound SDR", "operator room", "lead gen pipeline", "prospecting pipeline", "ICP targeting", "Apify", "RapidAPI", "Clay", "Apollo", "agentic SDR workflow", "sales operating loop", "sales dashboard handoff", or any request to package outbound sales workflows that can later connect to data tools, CRMs, or operator surfaces.

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Security architecture and threat modeling. OWASP Top 10 2025 analysis, OWASP Agentic AI Top 10 (ASI01-ASI10), AI/LLM security patterns, supply chain security, modern API authentication (OAuth 2.1, DPoP, Passkeys/WebAuthn), vulnerability assessment, and security review for code and infrastructure.

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Use when building durable AI agents or agentic workflows with Inngest and AgentKit, including model calls, tool calls, multi-agent networks, human approval, realtime progress, provider rate limits, crash-safe execution, and Agent Evals handoff. Covers AgentKit, `step.ai`, `step.run`, `step.waitForEvent`, native realtime, and when to use lower-level Inngest primitives instead of an in-memory agent loop. Use `inngest-agent-evals` with this skill when the user wants scoring, sessions, experiments, deferred scorers, or outcome-based evaluation for the agent.

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 building self-correcting retrieval systems for AI agents. Keywords: RAG, retrieval, Corrective RAG, Self-RAG, query decomposition, reranking, hallucination, grounding.

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Get productive with agentic coding assistants — task framing, iterative prompting, file-scoped workflows, review loops, and multi-file refactors. Use when pairing with an AI coding agent on real codebases.

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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.

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。