Master coordinator that delegates to specialist skills, synthesizes outputs, AND creates new skills on-the-fly when needed. Expert in problem decomposition, skill orchestration, quality assurance, and skill creation for capability gaps. Use for multi-skill coordination, complex task decomposition, workflow design. Activates on 'orchestrate', 'coordinate', 'multi-skill', 'complex task'. NOT for single-domain tasks or simple linear workflows.
Extracts key learnings from conversations, debugging sessions, and failed attempts. Use at session end or after solving complex problems to capture insights. Automatically suggests updates to: DISCOVERIES.md (learnings), PATTERNS.md (reusable solutions), new agent creation (repeated workflows). Ensures knowledge persists across sessions.
Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.
Build production LLM streaming UIs with Server-Sent Events, real-time token display, cancellation, error recovery. Handles OpenAI/Anthropic/Claude streaming APIs. Use for chatbots, AI assistants, real-time text generation. Activate on "LLM streaming", "SSE", "token stream", "chat UI", "real-time AI". NOT for batch processing, non-streaming APIs, or WebSocket bidirectional chat.
Semantic image-text matching with CLIP and alternatives. Use for image search, zero-shot classification, similarity matching. NOT for counting objects, fine-grained classification (celebrities, car models), spatial reasoning, or compositional queries. Activate on "CLIP", "embeddings", "image similarity", "semantic search", "zero-shot classification", "image-text matching".
Create AI learning plugins using AgentDBs 9 reinforcement learning algorithms. Train Decision Transformer, Q-Learning, SARSA, and Actor-Critic models. Deploy these plugins to build self-learning agents, implement RL workflows, and optimize agent behavior through experience. Apply offline RL for safe learning from logged data.
Integrates human agents into automated workflows for human-in-the-loop interactions. Use this when humans need to respond alongside AI agents, handle escalations, or provide context that AI cannot determine.
Expert in asynchronous programming patterns across languages (Python asyncio, JavaScript/TypeScript promises, C# async/await, Rust futures). Use for concurrent programming, event loops, async patterns, error handling, backpressure, cancellation, and performance optimization in async systems.
Assigns confidence scores to agent outputs based on multiple factors including source quality, consistency, and reasoning depth. Produces calibrated confidence estimates. Activate on 'confidence score', 'how confident', 'certainty level', 'output confidence', 'reliability score'. NOT for validation (use dag-output-validator) or hallucination detection (use dag-hallucination-detector).
Guides creation of high-quality Agent Skills with domain expertise, anti-pattern detection, and progressive disclosure best practices. Use when creating skills, reviewing existing skills, or when users mention improving skill quality, encoding expertise, or avoiding common AI tooling mistakes. Activate on keywords: create skill, review skill, skill quality, skill best practices, skill anti-patterns. NOT for general coding advice or non-skill Claude Code features.
Multi-dimensional web search orchestrator. Use when the user needs comprehensive web research that would benefit from parallel searches across different angles. Dispatches multiple fast subagents (haiku) to search in parallel, synthesizes results, and returns only relevant information with source URLs. Preserves main agent context by handling search complexity in subagents. Triggers on "research this", "find information about", "search the web for", "comprehensive search", or when a single search wouldn't capture all relevant aspects.
Run Microsoft's eval-recipes benchmarks to validate amplihack improvements against baseline agents. Auto-activates when testing improvements, running evals, or benchmarking changes.
Reset the development MySQL database by copying the test scenario SQL dump to the Docker init directory, replacing the database name from memoWikisTest to memoWikis_dev, and reinitializing the MySQL container. Use this skill when the user wants to reset, recreate, or reinitialize the development database.
Graceful degradation through cascading fallback strategies - ensures system always completes while maintaining acceptable functionality
Performs root cause analysis on DAG execution failures. Traces failure propagation, identifies systemic issues, and generates actionable remediation guidance. Activate on 'failure analysis', 'root cause', 'why did it fail', 'debug failure', 'error investigation'. NOT for execution tracing (use dag-execution-tracer) or performance issues (use dag-performance-profiler).
