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Build Python features that identify a speaker’s intent and extract details such as names from conversation text using Azure. Get guidance on request structure, authentication, client cleanup, and exception handling.
Turn exported AI chats and agent session logs into dated, searchable pages in gbrain. Check for missing conversations, extract facts, and trace when a topic first appeared or how an idea developed, with quotes and dated references.
Connect a ChatGPT or Claude account to import conversation history into knowledge pages. Sync new and changed conversations incrementally, revisit recent history to catch updates, and optionally enable scheduled syncing.
Collects every tweet in an X (Twitter) conversation thread given a conversation id (root tweet id) — the focal tweet plus all replies, sub-replies, and quote chains — and returns normalized per-tweet data with text, author, engagement counts, media, hashtags, mentions, in_reply_to mapping, and cursor for pagination. Use when user mentions Twitter conversation, X conversation thread, conversation_id, thread scraper, scrape Twitter replies, all replies to a tweet, replies under a tweet, sub-replies, nested replies, thread harvester, get replies of a tweet, scrape comments on Twitter, scrape comments on X, full thread extraction, conversation export, conversation tree, reply chain, thread dump, X tweet thread, twitter thread scrape, comment scraping twitter, comment scraping x, focal tweet plus context, root tweet plus replies. Also applies to sentiment analysis on a single viral tweet, controversy mapping, harvesting community Q&A threads, capturing AMA threads, recovering long-running discussions, and any paginated bulk reply collection driven by a conversation id.
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Review AI agent spending by conversation, with each session’s start time, message count, main model, and total cost. Use the results to identify expensive sessions and understand typical costs before planning longer work.
Draft, confirm, and post a single conversational reply to GitHub PR conversation comments (issue comments). The reply addresses all tracked items in one natural-prose message. Use when the user asks to "reply to PR conversation", "post PR conversation replies", or "draft PR conversation messages".
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Conversation patterns and interaction protocols for multi-agent systems. Covers request/response, pub/sub, blackboard, delegation chains, debate, critique, consensus, fan-out/fan-in, supervisor-worker, and peer negotiation. Deep analysis of AutoGen conversation patterns, CrewAI delegation, LangGraph state passing, and FIPA-ACL performatives. Teaches how to design what agents say to each other and in what order. Activate on: "agent conversation", "agent protocol", "multi-agent debate", "agent delegation", "supervisor worker pattern", "agent voting", "consensus protocol", "fan-out fan-in", "agent negotiation", "blackboard pattern", "agent dialogue", "conversation topology", "agent handoff". NOT for: wire format or serialization (use agent-interchange-formats), orchestration infrastructure (use agentic-infrastructure-2026), single agent behavior (use agentic-patterns).
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Design memory systems that help AI assistants retain conversation information and retrieve it when relevant. Explore short-term, long-term, and entity memory, along with memory consolidation, prompt integration, storage lifecycle management, and separation between users.
Create evaluation drafts for existing skills using actual conversations and user corrections. Produce criteria and repeatable test cases, distinguish evidence from specification assumptions, and prepare an anonymized draft for human review.
Request an owner’s permission to use a listed keychain credential needed by a command. An approval card appears in the conversation, and owner approval automatically reruns the blocked command with access shared across that conversation.
Build image conversations with LLaVA, a model that connects image recognition with language. This skill guides setup, image descriptions, visual questions, and follow-up conversations, with examples for Python, command-line use, and a Gradio interface.
Create a new Claude Code session containing only the last quarter of the current conversation. Preserve the most recent work, reduce conversation tokens, and get a command to resume the copied session directly.
Create a new Claude Code session from the later half of the current conversation. Keep recent work while reducing conversation tokens, then receive a command to resume the copied session directly.
Configure a new OpenRig agent seat, a managed place for an agent to run, using a prior Claude or Codex conversation. Covers fork settings, new session identities, and checks that distinguish forking from restoring.
Implement Conversational Language Understanding (CLU) using the azure-ai-language-conversations Python SDK. Use when working with ConversationAnalysisClient to analyze conversation intent and entities, building NLP features, or integrating language understanding into applications.
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Designing conversational flows for website chatbots and AI agents. Intent recognition architecture, branching logic, fallback handling, escalation to human, conversation analytics. Honest about scripted-bot (rigid trees, fail edge cases), hallucinating-bot (LLM without structure, makes things up), and structured-guided-conversation (LLM-powered with intent architecture and fallback discipline) patterns. Distinguishes chatbot DESIGN (this skill) from chatbot IMPLEMENTATION (engineering and platform work). Triggers on chatbot, conversational AI, AI agent, chat widget, intent design, conversational flow, bot escalation, LLM grounding. Also triggers when a chatbot is hallucinating, when a scripted bot is failing edge cases, or when a chatbot is being scoped for the first time.
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Implement AI chatbot analytics and conversation monitoring. Use when adding conversation metrics, tracking AI usage, measuring user engagement with chat, or building conversation dashboards. Activates for AI analytics, token tracking, conversation categorization, and chat performance.
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Implement Conversational Language Understanding (CLU) using the azure-ai-language-conversations Python SDK. Use when working with ConversationAnalysisClient to analyze conversation intent and entities, building NLP features, or integrating language understanding into applications.
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Implement AI chatbot analytics and conversation monitoring. Use when adding conversation metrics, tracking AI usage, measuring user engagement with chat, or building conversation dashboards. Activates for AI analytics, token tracking, conversation categorization, and chat performance.
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Collect OpenClaw pull requests, proposed code changes, and issues shared in a specified conversation and time window. Recheck them on GitHub, assess impact and readiness, and return a prioritized queue with evidence and coverage gaps.
Read recent chats and conversation history, watch for new messages, and send texts or attachments through macOS Messages.app using the imsg command-line tool. The workflow requires confirmation of the recipient and message before sending.
Send research and analysis tasks to a running DeerFlow instance through its HTTP API, an interface for exchanging requests. Retrieve responses, continue conversations, upload documents, and inspect available models, skills, agents, and service health.
Read Slack conversations and thread replies, decide when a response is needed, and send messages, files, or reactions. Keep updates and results in the original conversation, with support for direct messages sent on the user's behalf.
Search the agent’s conversation history by topic or date to retrieve relevant past entries. Inspect neighboring records when needed to understand the surrounding discussion, using the history log specified for the agent’s workspace.