Boost efficiency with task automation and organizers.
Search app data flow - query flow diagram (user input → debounce → alias check → provider dispatch → aggregate → rank → UI), state management patterns (mutableStateOf, mutableStateListOf, StateFlow, SharedFlow), settings JSON persistence in SharedPreferences.
Develop and troubleshoot Bitbucket Pipelines. Use when creating pipelines, debugging build failures, understanding Bitbucket-specific features like pipes and deployments, or optimizing pipeline performance.
Access external LLM providers through Domino AI Gateway - a secure proxy with centralized API key management, usage monitoring, and compliance. Supports OpenAI, AWS Bedrock, Azure OpenAI, Anthropic, and more. Use when calling LLMs from Domino, configuring AI Gateway endpoints, or monitoring LLM usage and costs.
Metathink - Optimize Claude Code usage through metacognitive strategies. Use extended thinking, context engineering, task decomposition, and chain-of-thought prompting. Use when planning complex tasks, debugging difficult issues, making architectural decisions, or improving AI collaboration effectiveness.
CLI power tools for AI-assisted development. Use when (1) needing recommendations for CLI tools to install, (2) processing JSON/YAML data with jq/yq, (3) searching code with ripgrep or ast-grep, (4) documenting a CLI tool or multi-tool recipe you've discovered, (5) wanting to learn CLI patterns for data pipelines, or (6) setting up a new project and want CLI recommendations. Supports three modes - init (project scan), document (capture new recipes), and recommend (codebase analysis).
Build and deploy a Next.js web interface for the Voice Assistant with real-time voice/chat capabilities. Use this skill when: (1) Creating a web-based voice assistant UI, (2) Setting up FastAPI WebSocket backend, (3) Implementing browser-based voice recording without Picovoice, (4) Deploying with Docker for global access, (5) Integrating push-to-talk or browser wake word detection, (6) Building scalable agentic web systems.
Guide for creating Claude Code slash commands. Use when the user wants to create a new slash command, update an existing slash command, or asks about syntax and options.
Guide for creating effective skills. Use when users want to create a new skill, update an existing skill, or learn about skill structure, frontmatter fields, hooks, context forking, or distribution patterns.
Create distinctive, production-grade frontend interfaces with high design quality. Use when building UI components, pages, or applications to avoid generic AI aesthetics.
Create new Claude Code skills with proper structure and conventions. Use when the user wants to create a new skill, scaffold skill templates, or understand skill anatomy.
Guide for creating custom Claude Code subagents (Task tool agents). Use when building specialized autonomous agents for specific domains or workflows. Covers agent definition, frontmatter options, tool restrictions, and spawning patterns.
Create optimized prompts for Claude-to-Claude pipelines with research, planning, and execution stages. Use when building prompts that produce outputs for other prompts to consume, or when running multi-stage workflows (research -> plan -> implement).
Search and analyze AI coding assistant session history using Terraphim. Find past conversations, discover patterns, and learn from previous work. Supports Claude Code, Cursor, Aider, and other AI coding assistants.
Execute the Joseph Cognitive Baseline v2.1 (JC B-v2.1) 5-Scenario Test Suite to quantify AI alignment, friction maintenance, and protocol adherence.
Create, validate, install, convert, port, and manage Claude Code skills. Use when users want to create a new skill, validate existing skills, install from GitHub, list skills, convert MCP servers to skills, or port skills between Claude and Gemini platforms. Covers the full skill lifecycle from creation to cross-platform distribution.
Build production-ready Tavily integrations with best practices baked in. Reference documentation for developers using coding assistants (Claude Code, Cursor, etc.) to implement web search, content extraction, crawling, and research in agentic workflows, RAG systems, or autonomous agents.
AI agent requests effective code reviews through proper preparation, clear descriptions, and appropriate reviewer selection. Use when creating PRs, requesting feedback, or submitting for review.
Build persistent project knowledge using checkpoint/recall. Activates when fixing bugs, making decisions, or investigating past work. Creates automatic knowledge base through systematic checkpointing and semantic recall.
Set up effective automated discovery - from vague idea to working system in one streamlined conversation.
A production-ready pattern for integrating AI models (specifically Google Gemini) with automatic fallback, retry logic, structured output via Zod schemas, and comprehensive error handling. Use when integrating AI/LLM APIs, need automatic fallback when models are overloaded, want type-safe structured responses, or building features requiring reliable AI generation.
Kailash Kaizen - production-ready AI agent framework with signature-based programming, multi-agent coordination, and enterprise features. Use when asking about 'AI agents', 'agent framework', 'BaseAgent', 'multi-agent systems', 'agent coordination', 'signatures', 'agent signatures', 'RAG agents', 'vision agents', 'audio agents', 'multimodal agents', 'agent prompts', 'prompt optimization', 'chain of thought', 'ReAct pattern', 'Planning agent', 'PEV agent', 'Tree-of-Thoughts', 'pipeline patterns', 'supervisor-worker', 'router pattern', 'ensemble pattern', 'blackboard pattern', 'parallel execution', 'agent-to-agent communication', 'A2A protocol', 'streaming agents', 'agent testing', 'agent memory', 'agentic workflows', 'AgentRegistry', 'OrchestrationRuntime', 'distributed agents', 'agent registry', '100+ agents', 'capability discovery', 'fault tolerance', 'health monitoring', 'trust protocol', 'EATP', 'TrustedAgent', 'trust chains', 'secure messaging', 'enterprise trust', 'credential rotation', 'trust verificati
Guided workflow for reverse engineering black-box systems. Use when a user wants to decode a defunct API, replicate a competitor's feature, understand unfamiliar code, or crack a data format.
Coordinates Serena MCP knowledge graph operations for Shannon Framework. Enforces standardized entity naming (shannon/* namespace), relation creation patterns, search protocols, and observation management. Prevents orphaned entities, naming chaos, and broken context lineage. Use when: storing specs/waves/goals/checkpoints, querying Shannon history, managing knowledge graph structure, ensuring cross-wave context preservation.
