Results for “chatbot”

Provides patterns to build declarative AI Services with LangChain4j for LLM integration, chatbot development, AI agent implementation, and conversational AI in Java. Generates type-safe AI services using interface-based patterns, annotations, memory management, and tools integration. Use when creating AI-powered Java applications with minimal boilerplate, implementing conversational AI with memory, or building AI agents with function calling.

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Operate ChatterBot conversational dialog engine workflows, including chatbot setup, training, logic adapters, storage backends, Django integration, and optional LLM adapters.

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Remove signs of AI-generated writing from prose, and keep it out of a repo. Use when drafting, editing, or reviewing text to make it sound natural and human, or when auditing a whole docs folder for AI slop. Detects 41 patterns across content, language, style, communication, filler, and rhetoric, including: significance inflation, promotional language, -ing tails, vague attributions, AI vocabulary, copula avoidance, negative parallelisms, false agency, em dash overuse, chatbot artifacts, hedging stacks, staccato drama, and aphorism formulas. Includes voice calibration, a no-fabrication rule, statistical tells, and a draft -> audit -> final rewrite loop.

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Detects 55 AI writing patterns and rewrites text in five voice profiles so it reads like a specific human wrote it, with an optional 0-100 AI-tell score. Use when text sounds AI-generated or like a chatbot, when preparing a blog post, README, or LinkedIn post for publication, when auditing prose for AI tells, or when editing a Markdown file in place. Triggers on phrases like "humanize this", "make this sound less AI", "make this sound human", "remove AI tells", "does this read like ChatGPT", and "rewrite so it does not sound AI-generated". Pure Markdown, zero dependencies, no network calls.

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Build production-ready LLM applications, RAG systems, and intelligent agents. Use when implementing AI features, chatbots, vector search, or agent orchestration.

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Test AI/LLM features that ship in your product. Covers prompt regression testing, response quality evaluation, tool-call validation, hallucination and RAG grounding checks, nondeterministic-output strategies, red-team/safety scans, eval frameworks, and agent-as-target injection (indirect injection via tool output / RAG / scan reports, self-propagating payloads, data exfiltration via an agent) plus a bundled detector for untrusted content. Use when: "test our LLM feature," "prompt regression test," "eval framework," "hallucination test," "RAG grounding," "nondeterministic output," "AI feature testing," "red-team our chatbot," "indirect prompt injection," "agent reading untrusted tool output," "production AI quality." Not for: using AI to generate your own test code — use ai-test-generation. Not for: classifying CI failures with AI — use ai-bug-triage. Not for: EU AI Act / GDPR conformity of an AI feature — use compliance-testing. Not for: canary/flag rollout of an AI feature — use testing-in-production. Related: ai-test-generation, ai-qa-review, api-testing, compliance-testing, security-testing, risk-based-testing, test-data-management.

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Dify is an open-source platform for building LLM apps such as chatbots, agents, workflows and RAG knowledge bases, and it exposes every published app as a REST API. Use when a user asks to self-host Dify with Docker Compose, configure Dify environment variables, call a Dify app or workflow API, upload documents to a Dify knowledge base, export or import a Dify DSL file, run a Dify app from the terminal with difyctl, or upgrade and back up a Dify instance.

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Add live chat and customer support with Crisp. Use when a user asks to add a chat widget, implement live customer support, set up a help desk, create a knowledge base, or add a chatbot to a website.

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Integrate Claude AI into applications with the Anthropic SDK. Use when a user asks to add Claude to an app, use Claude for text generation, build a chatbot with Claude, use Claude's long context window, implement tool use with Claude, stream Claude responses, use Claude for code generation, document analysis, or reasoning tasks. Covers Messages API, streaming, tool use, vision, system prompts, extended thinking, and batch processing.

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Answer questions about the AI SDK and help build AI-powered features. Use when developers: (1) Ask about AI SDK functions like generateText, streamText, ToolLoopAgent, embed, or tools, (2) Want to build AI agents, chatbots, RAG systems, or text generation features, (3) Have questions about AI providers (OpenAI, Anthropic, Google, etc.), streaming, tool calling, structured output, or embeddings, (4) Use React hooks like useChat or useCompletion. Triggers on: "AI SDK", "Vercel AI SDK", "generateText", "streamText", "add AI to my app", "build an agent", "tool calling", "structured output", "useChat".

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Script a multi-turn tutoring dialogue with branching responses for anticipated student difficulties. Use when designing AI tutors, chatbot interactions, or structured one-to-one support scripts.

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Design a multi-round questioning sequence for interrogating AI chatbot answers, tracking how responses shift and distinguishing genuine updates from sycophantic capitulation. Use when teaching students to probe AI critically.

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Add Qdrant embedding support to v3 WordPress components for RAG chatbot. Implements component-level content chunking for searchable, structured embeddings. Use when adding embedding to new or existing v3 components.

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AI Engine is a WordPress plugin by Meow Apps that connects sites to OpenAI, Claude, Gemini, and other models while exposing WordPress actions through MCP and REST interfaces. This skill helps agents configure providers, enable the plugin's MCP capabilities, and automate content, chatbots, media, and site-management workflows from WordPress.

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Hunt vulnerabilities in LLM-powered applications — direct/indirect prompt injection, system prompt extraction, ASCII smuggling, RCE via code tools, agentic AI exploits (ASI01-ASI10), data exfiltration via response channels, IDOR in chat history, jailbreak chains, RAG poisoning. Use when target has a chatbot, AI assistant, copilot, or agentic AI features.

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Si attiva quando l'utente chiede di scrivere, redigere, tradurre o revisionare testi italiani per uso pubblico, editoriale, blog, social, newsletter, marketing, narrativa, saggistica o divulgazione. Si attiva anche quando l'utente chiede di umanizzare testi italiani, eliminare pattern AI, correggere lessico da chatbot, catturare o calibrare la voce di un autore su un corpus di suoi testi, rispondere a un rilevatore automatico che ha segnalato come generato un testo scritto da una persona, o quando rivede testi italiani generati da modelli linguistici.

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DNS C2 operator skill for the AgentCore sandbox breakout demo. This skill should be used when the user asks to run C2 verification, attack a victim chatbot, send commands to a compromised session, retrieve exfiltrated data, or interact with the DNS C2 system.

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Test Retrieval-Augmented Generation (RAG) systems for data poisoning, prompt injection via retrieved documents, and data exfiltration through manipulated context windows. Use this skill when assessing RAG-based chatbots, knowledge bases, enterprise AI assistants, or any system that augments LLM responses with external document retrieval. Covers document injection, embedding manipulation, knowledge base poisoning, and cross-document inference attacks.

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สร้าง chatbot flow, FAQ bank, escalation rule, reply template สำหรับ Line OA/Facebook Inbox/IG DM

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Designs human-in-the-loop review points for DAG workflows. Determines what to present to the human, how to collect feedback, and how to route approve/reject/modify decisions back into the DAG. Use when adding approval gates, designing review UX, or handling human feedback in agent workflows. Activate on "human review", "approval gate", "human-in-the-loop", "human gate", "approval workflow", "user review step". NOT for executing human gates at runtime (use dag-runtime with Temporal signals), general UX design, or chatbot conversation design.

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Expert bot developer specializing in Discord, Telegram, Slack automation with deep knowledge of rate limiting, state machines, event sourcing, moderation systems, and conversational AI integration. Activate on 'Discord bot', 'Telegram bot', 'Slack bot', 'chat automation', 'moderation system'. NOT for web APIs (use backend-architect), general automation scripts (use python-pro), or frontend chat widgets (use frontend-developer).

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Build production-ready LLM applications, advanced RAG systems, and intelligent agents. Implements vector search, multimodal AI, agent orchestration, and enterprise AI integrations. Use PROACTIVELY for LLM features, chatbots, AI agents, or AI-powered applications.

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Authorized testing of LLM applications, agents, chatbots, RAG systems, guardrails, and tool-using model workflows. Use for LLM mode selection, jailbreak and prompt-injection assessment, guardrail bypass review, tool misuse, tool or system prompt disclosure, data exfiltration paths, model refusal/stop detection, bypass hypotheses, and hardening guidance.

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Black-box evaluation of a shipped LLM feature — adversarial probes for hallucination, prompt-injection, and cost-runaway vs stated expectations. Not RAG/embedding. Triggers 'review my chatbot'.

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