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Found 114 Skills
Build stateful chatbots with OpenAI Assistants API v2 - Code Interpreter, File Search (10k files), Function Calling. Prevents 10 documented errors including vector store upload bugs, temperature parameter conflicts, memory leaks. Deprecated (sunset August 2026); use openai-responses for new projects. Use when: maintaining legacy chatbots, implementing RAG with vector stores, or troubleshooting thread errors, vector store delays, uploadAndPoll issues.
Use this skill when the user asks to add, embed, integrate, configure, style, or remove an agent, chatbot, chat widget, conversation client, or AI assistant in a UI Bundle project. TRIGGER when: project contains a uiBundles/*/src/ directory and the task involves adding or modifying a chat widget, chatbot, or conversational AI; files under uiBundles/*/src/ import AgentforceConversationClient; user asks to add any chat or agent functionality to a page. DO NOT TRIGGER when: user wants to create a custom agent, chatbot, or chat widget component from scratch; the project has no uiBundles directory.
Design conversational AI chatbots including intent recognition, slot filling, dialogue flow, and response generation. Use this skill when the user needs to build a chatbot, design conversation flows, implement intent classification, or improve chatbot accuracy — even if they say 'build a chatbot', 'our bot doesn't understand users', 'design a FAQ bot', or 'improve our chatbot's responses'.
Use when building an internal admin-facing AI agent in a Medusa project. These agents are operated by merchants and store operators — not customers. Covers data models, module service, agent runtime (tools, system prompt, streamText), streaming API routes (NDJSON), and admin UI chat extensions. Load for any internal agent type: store operations assistant, product audit, cohort analysis, customer service tooling for support staff, etc. Do NOT use for customer-facing agents (storefront chatbots, buyer-side assistants).
Build AI that answers questions about your database. Use when you need text-to-SQL, natural language database queries, a data assistant for non-technical users, AI-powered analytics, plain English database search, or a chatbot that talks to your database. Covers DSPy pipelines for schema understanding, SQL generation, validation, and result interpretation.
Lindy platform help — no-code AI agent builder for email triage, meeting notes, calendar management, custom workflow automation, chatbots, and AI phone agents. Use when setting up Lindy agents for inbox management, meeting recording not working or transcripts missing, credits burning too fast and need to optimize usage, building custom AI workflows with triggers and actions, choosing between Lindy and a dedicated note-taker or automation tool, or debugging agent errors in multi-step workflows. Do NOT use for picking a dedicated AI note-taker across vendors (use /sales-note-taker) or general workflow automation without AI reasoning (use /sales-integration).
Use the data.gouv.fr MCP server to search, explore, and analyze French Open Data datasets through AI chatbots
De-AI-ifies and tightens academic prose without changing any technical claim. Use when a researcher says "this sounds like ChatGPT / AI-written", "humanize my paper", "remove the AI words", "too many delves and leverages", "polish my writing", "tighten the prose", "fix my hedging", "too many Moreover/Furthermore", "make my contributions active", or "keep my terminology consistent". Runs deterministic stdlib linters — prose_lint.py (leftover chatbot artifacts, LLM-tell vocabulary, connective stacking, em-dash density, double hedges, overclaiming boosters, passive contribution statements) and terminology_check.py (dataset/data set/data-set variants, acronym discipline, -ise/-ize mixing, glossary enforcement) — then drives a section-by-section edit pass using rewrite catalogs and the venue family's register norms. Numbers, results, claims, and citations are never altered; the venue's AI-use disclosure policy is surfaced and respected, never dodged.
Rewrite text that sounds AI-generated while keeping the writer's facts, meaning, and voice. Use when editing or reviewing prose for inflated claims, sales language, vague sources, repetitive structure, stock AI words, passive voice, filler, or chatbot artifacts. Based on Wikipedia's "Signs of AI writing."
Expert assistant for BuilderBot (v1.4.0) — a TypeScript/JavaScript framework for building multi-platform chatbots (WhatsApp, Telegram, Instagram, Email, etc.). Use when creating or editing flows (addKeyword, addAnswer, addAction), wiring EVENTS, managing per-user state or globalState, configuring providers (Baileys, Meta, Telegram, Evolution, etc.) or databases (Mongo, Postgres, MySQL, JSON), implementing REST API endpoints (handleCtx, httpServer), debugging flow control (gotoFlow, endFlow, fallBack, idle, capture, flowDynamic), or handling blacklist logic. Architecture: Provider + Database + Flow.
Provides authoritative guidance on Amazon Neptune Database and Neptune Analytics for graph, knowledge-graph, and relationship-heavy workloads — fraud detection / fraud rings, agentic memory / chatbot context across sessions, recommendations, identity resolution, Gremlin / openCypher / SPARQL queries, supernode / slow traversal, Neo4j to Neptune migration / APOC compatibility, Neptune Database vs Analytics engine selection, PageRank / community detection, GraphRAG, and connectivity from Lambda / EC2 / applications. Creates and modifies Neptune Database clusters/instances and Neptune Analytics graphs on explicit user confirmation; blocks destructive operations (delete, reset-graph, failover, major upgrade) and redirects to change-control.
Build AI agents with structured access to Sanity content via Sanity Context. Use when setting up a Sanity-powered chatbot, connecting an AI assistant to Sanity content, or adding client-side tools to an agent. Covers Studio setup, agent implementation, and advanced patterns. Always use this skill when users mention building a chatbot with Sanity, creating an AI assistant for their content, setting up the Sanity Context MCP server, integrating Sanity with Claude/GPT/any LLM, making content searchable by AI, implementing semantic search over Sanity data, or connecting their CMS to an AI agent.