Results for “conversation-intelligence”

Search and retrieve Gong call recordings, transcripts, user lists, and activity statistics via the Gong API.

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.

Build and query AI-powered knowledge bases from claude-mem observations, enabling focused conversational sessions on specific topics.

Integrate Honcho memory and social cognition into existing Python or TypeScript codebases. Use when adding Honcho SDK, setting up peers, configuring sessions, implementing the dialectic chat endpoint for AI agents, or wiring Honcho into bot frameworks (nanobot, openclaw, picoclaw, etc).

Voice agents represent the frontier of AI interaction - humans speaking naturally with AI systems. The challenge isn't just speech recognition and synthesis, it's achieving natural conversation flow with sub-800ms latency while handling interruptions, background noise, and emotional nuance. This skill covers two architectures: speech-to-speech (OpenAI Realtime API, lowest latency, most natural) and pipeline (STT→LLM→TTS, more control, easier to debug). Key insight: latency is the constraint. Hu

Conversation Analyst

Generate novel research ideas through structured, conversational brainstorming that explores interdisciplinary connections, challenges assumptions, and identifies research gaps.

Voice agents represent the frontier of AI interaction - humans speaking naturally with AI systems. The challenge isn't just speech recognition and synthesis, it's achieving natural conversation flow with sub-800ms latency while handling interruptions, background noise, and emotional nuance. This skill covers two architectures: speech-to-speech (OpenAI Realtime API, lowest latency, most natural) and pipeline (STT→LLM→TTS, more control, easier to debug). Key insight: latency is the constraint. Hu

Makes AI responses feel more human by adding an inner dialogue loop that reflects on and revises the initial instinctive reply.

Builds, edits or analyzes Agent Skills through conversational discovery. Use when the user requests to "Create an Agent", "Analyze an Agent" or "Edit an Agent".

Conversational Pattern Restoration — Fix flat, robotic AI responses across any model and any personality. Restore YOUR natural conversational texture without triggering hype drift. Universal framework tested on 8+ models (Claude, GPT-4o, Grok, Gemini).

Pauses mid-conversation to reassess direction, assumptions, and bias, then recommends whether to continue, pivot, or pause.

Generates speech, transcribes audio, clones voices, and builds real-time voice agents using ElevenLabs, OpenAI TTS, Whisper, and Vapi.

Elite brainstorming specialist for facilitated ideation sessions. Use when the user asks to talk to Carson or requests the Brainstorming Specialist.

Use the discussion-management skill to start a structured discussion

Multi-source recency research skill that takes the pulse of any topic across Reddit, Hacker News, the open web, and optionally X/Twitter within a configurable recent window (default 30 days). Forcing intake clarifies topic specificity, angle (trend/sentiment/problems/opportunities/comparison), time window, and platform scope before searching. Returns a synthesized briefing with citations, engagement metrics, and cross-platform pattern analysis. Use when the user requests multi-source recency intelligence on a topic (e.g., 'pulse on [topic]', 'what's happening with [topic]', 'what are people saying about [topic]', 'current conversation about [topic]', 'take the pulse of [topic]', 'trending: [topic]', 'find me info on [topic]'), and for competitor research, trend discovery, tool comparisons, and audience sentiment analysis.

Analyzes meeting transcripts to uncover behavioral patterns, communication insights, and actionable feedback for improving communication and leadership skills.

Text-to-speech and speech-to-text using fal.ai audio models

Use when a manager, dispatcher, or safety director needs to have a coaching conversation with a driver — about a safety event (harsh brake, hard turn, distraction), an HOS violation, a customer complaint, a missed delivery, repeated minor incidents, or to deliver positive recognition. Covers conversation structure, language, documentation, and escalation.

Use when the user wants more human-like AI responses — less robotic, less listy, more authentic. Triggers: 'behuman', 'be real', 'like a human', 'more human', 'less AI', 'talk like a person', 'mirror mode', 'stop being so AI', or when conversations are emotionally charged (grief, job loss, relationship advice, fear). NOT for technical questions, code generation, or factual lookups.

Use to check whether a conversation export, dataset or AI pipeline is leaking personal data that should have been redacted, and to measure how well the redaction actually works. Trigger for "is this export safe to share", "check our redaction", "can we use support transcripts for training", sending transcripts to a vendor or model, "is there PII in this dataset", or before opening support data to a wider audience.

Loop feature-interviews over a brainstorm idea set and consolidate survivors into the roadmap

Use when starting any conversation - establishes how to find and use skills, requiring skill invocation before ANY response including clarifying questions

Plan customer discovery conversations with hypotheses, prompts, probes, and capture fields.

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.

Evaluates speech language models on beyond-semantic speech attributes such as dialect comprehension, multi-turn context memory, emotion perception, age-aware response generation, and non-verbal cue handling, reporting accuracy and judge-based scores.

Analyzes why a piece of content resonates with audiences using five classic communication theories, identifies the emotional core and effective stance, and outputs discussion directions for a chatroom.

Embody the voice and methodology of AI researcher Fei-Fei Li to enhance technical content with data-centric and human-centered perspectives.

Embody the voice and methodology of Confucius to analyze situations through virtue, relationships, and self-cultivation, offering sage-like guidance.

Builds, edits or analyzes Agent Skills through conversational discovery. Use when the user requests to "Create an Agent", "Analyze an Agent" or "Edit an Agent".

Strategic planning with optional interview workflow

Voice agents represent the frontier of AI interaction - humans speaking naturally with AI systems. The challenge isn't just speech recognition and synthesis, it's achieving natural conversation flow with sub-800ms latency while handling interruptions, background noise, and emotional nuance. This skill covers two architectures: speech-to-speech (OpenAI Realtime API, lowest latency, most natural) and pipeline (STT→LLM→TTS, more control, easier to debug). Key insight: latency is the constraint. Hu