Agent Skills Hub

alphaear-deepear-lite

Fetch real-time financial signals, transmission-chain reasoning, and market confidence metrics directly from the DeepEar Lite platform.

prompt-lookup

Search, discover, and refine AI prompts using the prompts.chat library. Access thousands of community-curated prompts for ChatGPT, Claude, and other AI models.

vibe-prd

A structured PRD generator for vibe-coding MVPs. It guides you through defining product requirements, target audiences, and success metrics, ensuring a clear foundation for your development workflow.

context-cascade

A nested plugin architecture for Claude Code that optimizes context by dynamically loading playbooks, skills, and agents to save over 90% in token usage.

startup-business-analyst-business-case

Generate comprehensive, investor-ready business cases for startups, including market analysis, financial modeling, competitive positioning, and funding strategies.

financial-analyzing

Analyze financial data, calculate key performance metrics like margins and ROI, and generate structured analytical reports.

multi-llm-advisor

Fetches expert perspectives from OpenAI Codex and Google Gemini for architecture, code reviews, and debugging, with transparent LLM synthesis.

orchestrator

Autonomous multi-agent orchestration framework for Claude Code with memory-driven workflows, parallel-first task execution, Aristotle-based deconstruction, and multi-stage quality gates.

competitor-analysis

Conduct strategic competitive analysis to map market landscapes, identify direct competitors, synthesize strengths and weaknesses, and uncover differentiation opportunities.

deep-thinking

A deep reasoning protocol that ensures systematic analysis, multi-hypothesis generation, and rigorous verification for complex architectural, debugging, and high-stakes tasks.

ctx-planning

Generate daily and weekly planning reports from backlog and carryover state, applying WIP limits and priority rules from BaseContext.yaml with automatic git commit/push.

llmfit-advisor

Analyze local system hardware (RAM, CPU, GPU/VRAM) to receive expert recommendations for optimized local LLM models, quantization settings, and performance estimates.