Skill UI

This skill helps transform a product initiative into a comprehensive, structured influence plan. It maps key stakeholders, identifies their primary concerns and decision authority, and recommends the optimal conversation sequence. It generates tailored talking points and objection handling strategies to ensure alignment and secure buy-in from complex organizational groups.

A comprehensive guide detailing the aesthetic and technical implementation of AI-native user interfaces for web and mobile applications. It emphasizes conversational flow, adaptive layouts, and generative state visual cues, providing CSS, SwiftUI, and Flutter code examples.

PyRIT (Python Risk Identification Tool) is an open-source automation framework designed for advanced, multi-turn adversarial red-teaming of conversational LLMs. It simulates real-world attack scenarios by orchestrating an attacker model and a scorer model in a feedback loop. Features include Crescendo (gradual escalation) and Tree-of-Attacks with Pruning (TAP) techniques to detect complex vulnerabilities like prompt injection and jailbreaks in stateful dialogues.

This skill guides developers on implementing Conversational Language Understanding (CLU) using the official azure-ai-language-conversations Python SDK. It demonstrates how to analyze conversation intent and entities, build advanced NLP features, and integrate language understanding into applications using ConversationAnalysisClient. Best practices emphasize using DefaultAzureCredential and context managers for robust, production-ready code.

A comprehensive skill for systematically monitoring developer conversations across technical platforms like GitHub, Reddit, and Stack Overflow. It helps track brand mentions, competitor positioning, and unsolved technical problems to gather unfiltered product feedback and high-intent leads.

This skill guides developers on building real-time, bidirectional, and multimodal streaming applications using the Gemini Live API. It covers low-latency voice, video, and text streaming over WebSockets, incorporating advanced features like Voice Activity Detection (VAD), function calling, and secure session management. Ideal for conversational AI, interactive video conferencing, and complex real-time user experiences.

A powerful plugin enabling Claude Code to delegate complex tasks to external AI CLIs (such as OpenAI Codex or Google Antigravity/Gemini) without exiting the conversation. It supports multi-model workflows, allowing users to seek a second opinion, cross-check deep analysis, or delegate side tasks (like research or code review). The core value is that Claude synthesizes the raw outputs, providing a critical review and reconciliation of findings across all consulted models.

Execute discrete, bounded coding, review, or verification tasks using the Codex CLI as an isolated subagent. This tool is ideal for tasks requiring explicit file ownership, such as isolated refactoring, feature implementation, or independent code verification, without relying on continuous conversational context. It runs in a safe sandbox environment.

This guide details how to effectively delegate complex, multi-step tasks to various specialized AI agents (Codex, Pi, Claude, Hermes). It covers best practices for maintaining conversation context, handling TUI prompts (e.g., avoiding newlines, using quotes), techniques for reliable polling, and choosing the optimal agent for coding, design, or general purpose work. Essential for advanced AI workflow automation.

A powerful tool for conversational time tracking. Users simply report time spent in plain language (e.g., "read papers 2h, gym 1h"), and the skill parses this into structured rows (Activity, Minutes, Date) within your Notion database. It ensures data integrity by flagging any uncertain details as 'To-confirm' rows and batch-asking questions, preventing data fabrication. Use it for logging daily activities or performing time reviews.

This skill provides a granular, session-by-session view of total accumulated costs. It lists metrics such as start time, message count, top model, and total cost for every recorded conversation. This is ideal for identifying which specific sessions consumed the most resources, which is crucial for project retrospectives and evaluating the sustainability of current session patterns.

This tool provides a granular, per-message cost breakdown within a single conversation session. It serves as a deep-dive utility following an anomaly detection, allowing operators to pinpoint exactly which messages incurred the highest costs. It reveals critical cost components like output tokens, cache writes, and model escalations, helping users understand the true resource consumption beyond simple token counts.