Trauma-informed AI moderator for addiction recovery communities. Applies harm reduction principles, honors 12-step traditions, distinguishes healthy conflict from abuse, detects crisis posts. Activate on 'community moderation', 'moderate forum', 'review post', 'check content', 'crisis detection'. NOT for legal documents (use recovery-app-legal-terms), app development (use domain skills), or therapy (use jungian-psychologist).
Manages conversation threads to group messages into linear histories. Use this when organizing multi-turn conversations, managing per-user history, and handling thread metadata.
Detect crisis signals in user content using NLP, mental health sentiment analysis, and safe intervention protocols. Implements suicide ideation detection, automated escalation, and crisis resource integration. Use for mental health apps, recovery platforms, support communities. Activate on "crisis detection", "suicide prevention", "mental health NLP", "intervention protocol". NOT for general sentiment analysis, medical diagnosis, or replacing professional help.
Use Gemini CLI for research with Google Search grounding and 1M token context
Expert prompt optimization for LLMs and AI systems. Use PROACTIVELY when building AI features, improving agent performance, or crafting system prompts. Masters prompt patterns and techniques.
Expert skill for implementing text-to-speech with Kokoro TTS. Covers voice synthesis, audio generation, performance optimization, and secure handling of generated audio for JARVIS voice assistant.
Router skill for DiPeO runtime Python code (execution handlers, service architecture, domain models, LLM infrastructure). Use when task mentions node handlers, EventBus, ServiceRegistry, Envelope pattern, or domain logic. For simple tasks, handle directly; for complex work, escalate to dipeo-package-maintainer agent.
Delegate code implementation/review/debugging/alternatives to Claude Code via a JSON bridge script. Multi-turn via SESSION_ID.
Proactive context window management via token monitoring, intelligent extraction, and selective rehydration. Features predictive budget monitoring, context health indicators, and priority-based retention. Use when approaching token limits or needing to preserve essential context. Complements /transcripts and PreCompact hook with proactive optimization.
Sets up a web UI for testing, debugging, and developing agents without code. Use this to manually test agents, browse conversation history, and verify behavior in real-time.
Expert skill for integrating local Large Language Models using llama.cpp and Ollama. Covers secure model loading, inference optimization, prompt handling, and protection against LLM-specific vulnerabilities including prompt injection, model theft, and denial of service attacks.
Expert in drone systems, computer vision, and autonomous navigation. Specializes in flight control, SLAM, object detection, sensor fusion, and path planning. Activate on "drone", "UAV", "SLAM", "visual odometry", "PID control", "MAVLink", "Pixhawk", "path planning", "A*", "RRT", "EKF", "sensor fusion", "optical flow", "ByteTrack". NOT for domain-specific inspection tasks like fire detection, roof damage assessment, or thermal analysis (use drone-inspection-specialist), GPU shader optimization (use metal-shader-expert), or general image classification without drone context (use clip-aware-embeddings).
Advanced AgentDB Vector Search Implementation operates on 3 fundamental principles:
Customizes what information the LLM receives for each generation. Use this to control message history, implement RAG context injection, search across threads, and provide custom context.
Extracts key learnings from conversations, debugging sessions, and failed attempts. Use at session end or after solving complex problems to capture insights. Stores discoveries in memory (via amplihack.memory.discoveries), suggests PATTERNS.md updates, and recommends new agent creation. Ensures knowledge persists across sessions via Kuzu memory backend.
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.
Expert in voice synthesis, TTS, voice cloning, podcast production, speech processing, and voice UI design via ElevenLabs integration. Specializes in vocal clarity, loudness standards (LUFS), de-essing, dialogue mixing, and voice transformation. Activate on 'TTS', 'text-to-speech', 'voice clone', 'voice synthesis', 'ElevenLabs', 'podcast', 'voice recording', 'speech-to-speech', 'voice UI', 'audiobook', 'dialogue'. NOT for spatial audio (use sound-engineer), music production (use DAW tools), game audio middleware (use sound-engineer), sound effects generation (use sound-engineer with ElevenLabs SFX), or live concert audio.
Automatically intercepts and optimizes prompts using the prompt-learning MCP server. Learns from performance over time via embedding-indexed history. Uses APE, OPRO, DSPy patterns. Activate on "optimize prompt", "improve this prompt", "prompt engineering", or ANY complex task request. Requires prompt-learning MCP server. NOT for simple questions (just answer them), NOT for direct commands (just execute them), NOT for conversational responses (no optimization needed).
Sets up and configures Convex agents for chat-based AI interactions. Use this when initializing agent instances, creating conversation threads, and generating basic text or structured responses from LLMs. Essential foundation for any Convex agent implementation.
Expert speech-language pathologist specializing in AI-powered speech therapy, phoneme analysis, articulation visualization, voice disorders, fluency intervention, and assistive communication technology. Activate on 'speech therapy', 'articulation', 'phoneme analysis', 'voice disorder', 'fluency', 'stuttering', 'AAC', 'pronunciation', 'speech recognition', 'mellifluo.us'. NOT for general audio processing, music production, or voice acting coaching without clinical context.
Implements Retrieval-Augmented Generation (RAG) patterns to enhance agents with custom knowledge bases. Use this when agents need to search through documents, retrieve context from a knowledge base, or ground responses in specific data.
Guides architects on when and how to use goal-seeking agents as a design pattern. This skill helps evaluate whether autonomous agents are appropriate for a given problem, how to structure their objectives, integrate with goal_agent_generator, and reference real amplihack examples like AKS SRE automation, CI diagnostics, pre-commit workflows, and fix-agent pattern matching.
