Skill UI

Varlock provides secure, default environment variable management, ensuring that sensitive secrets (like API keys and passwords) are never exposed in logs, terminal output, or the AI context. It validates environments, reads schemas, and executes commands in a secure manner, promoting best practices for handling secrets in modern development workflows.

This skill integrates multiple digital health sources (such as Apple Health, Fitbit, Oura, and CSV/JSON files) into a unified system. It processes, standardizes, and validates diverse health data formats, and connects this data to a dedicated knowledge base (WellAlly.tech). It provides intelligent recommendations and article retrieval based on the user's current health context and needs.

Auri is an advanced, next-generation voice assistant built upon Amazon Alexa and the powerful Claude AI model. Unlike traditional assistants, Auri conducts real, contextual conversations, enabling deep reasoning, remembering user context via DynamoDB, and providing natural, empathetic responses in Brazilian Portuguese. It elevates interactions from simple commands to complex, multi-turn dialogues requiring sophisticated natural language understanding.

A deep expert system for Anthropic's Claude Code CLI. This skill maximizes productivity by managing advanced features like hooks, persistent context via CLAUDE.md, memory management, sub-agent orchestration, and integrating with local development ecosystems. Ideal for complex coding tasks, debugging, and advanced workflow automation.

This agent ensures seamless continuity across multiple work sessions. It automatically captures, compresses, and stores critical project information—including summarized discussions, architectural decisions, pending tasks, modified files, and technical discoveries. When a new session starts, it loads a comprehensive briefing, allowing users to quickly resume work without losing track of the project's current state.

Context Guardian preserves critical context before Claude Code’s automatic compression, collecting verified snapshots, detecting integrity issues, and briefing transitions so heavy-technical sessions keep every essential decision and dependency intact.

A framework for building production-ready, low-latency, multimodal AI agents. It allows developers to orchestrate complex pipelines that seamlessly handle real-time audio, video, and text data. Key capabilities include integrating multiple AI services (LLMs, STT, TTS), managing conversation context, and enabling tool use and function calling for advanced, interactive AI applications.

This skill implements a structured product positioning framework based on identifying competitive alternatives, unique product attributes, and ideal target customers. It guides the user through defining the context in which their product is evaluated, ensuring that messaging, sales narratives, and marketing strategies are deeply aligned with the market's truth. Ideal for new product launches, repositioning, and defining go-to-market strategies.

Automates project-aware diary maintenance by recording status from conversations, refreshing context, extracting global and project progress, and syncing to Notion/Obsidian while enforcing strict workflow steps.

This utility converts a single, high-level objective into a comprehensive, step-by-step build plan suitable for complex, multi-agent, or multi-session engineering projects. It automatically handles dependency mapping, detects parallel steps, and structures each step with self-contained context briefs, ensuring any new agent can execute it without prior knowledge. Ideal for large-scale refactoring or feature development spanning multiple PRs.

A comprehensive guide to building robust, maintainable, and idiomatic Perl applications using modern features (Perl 5.36+). This resource covers best practices for object-oriented programming (Moo/Moose), advanced error handling (Try::Tiny), context sensitivity, complex regex usage, and efficient module design.

Creates concise Architecture Decision Records (ADRs) that capture context, decision, and consequences whenever a significant technical choice has been finalized so future engineers understand why the architecture looks the way it does.