Skill UI

A comprehensive guide and template for deploying applications from idea to production. It covers modern DevOps practices, including containerization with Docker, robust CI/CD workflows using GitHub Actions, and infrastructure management with AWS SAM and Terraform. Includes setup for monitoring, health checks, and scalable cloud deployments.

A comprehensive toolkit for accelerating digital product growth. It covers essential strategies including growth hacking, SEO/ASO optimization, building viral loops, email marketing funnels, CRM implementation, and analyzing the entire customer acquisition lifecycle using AARRR metrics. Ideal for product managers and marketers aiming for exponential business expansion.

Comprehensive guide to LLM Operations (LLMOps). Covers advanced topics like Retrieval-Augmented Generation (RAG), vector database management (Pinecone, Chroma), embeddings, fine-tuning, prompt engineering, and cost optimization. Essential for bridging the gap between AI prototypes and reliable, scalable, production-grade AI systems.

This comprehensive framework guides the design of sustainable motivation systems for products and teams, shifting focus from outdated 'carrot-and-stick' rewards to intrinsic drivers: Autonomy, Mastery, and Purpose (AMP). It provides deep insights into how to build engaging, lasting user loops and organizational incentives that foster genuine engagement and high performance, especially when traditional gamification fails.

This framework outlines the 'Hook Model' (Trigger, Action, Variable Reward, Investment) for building deeply engaging, habit-forming product loops. It guides designers in optimizing user retention, understanding behavioral psychology, and structuring product features to move users from deliberate usage to automatic, routine behavior. Use it for onboarding, feature design, and improving long-term user stickiness.

This comprehensive index provides access to 32 production-ready, specialized engineering skills. It covers the entire development lifecycle, including system architecture, frontend, backend, DevOps, security operations, AI/ML, and data engineering. It is designed to guide users in selecting and loading only the specific module needed, ensuring focused expertise for complex tasks.

This powerful orchestrator coordinates the entire product development lifecycle by routing inquiries across 16 specialized sub-skills (e.g., RICE, OKRs, UX research, analytics). It acts as a central hub for both deterministic task routing and running continuous, machine-gated discovery loops. Use it when you need to systematically prioritize features, run A/B tests, or drive a goal from initial assumption to validated repository code.

This skill provides methods for reliably extracting typed, validated, and structured data from Large Language Models (LLMs), moving beyond simple text parsing. It covers best practices across major APIs (OpenAI's response_format, Anthropic's tool_use, Google's responseSchema) and local models. Key workflows include defining schemas (Pydantic/Zod), implementing constrained decoding, and building robust retry loops for production-grade data pipelines.

Autoresearch Agent runs an autonomous optimization loop that edits a target file, invokes a provided evaluation command, keeps metric-improving commits, drops failures, and repeats indefinitely so engineers can optimize speed, bundle size, prompts, or other measurable goals overnight.

Helps founders, RevOps, and GTM engineers turn manual revenue processes into reliable, observable automation by mapping tech stacks, designing workflow architectures, and orchestrating AI agents across platforms like n8n, Make, and Zapier.

Perform comprehensive digital forensic analysis on raw (dd), E01, or AFF disk images using industry-standard tools like Autopsy and The Sleuth Kit. This process allows investigators to recover deleted files, examine file system metadata, search for specific keywords, and build detailed investigation timelines. It is essential for generating structured reports from digital evidence in legal and cybersecurity investigations.

A comprehensive guide detailing how to integrate DevSecOps best practices into a GitLab CI/CD pipeline. It automates multiple security checks, including Static Analysis (SAST), Dynamic Analysis (DAST), container scanning, dependency scanning, and secret detection. This approach enforces 'shift-left' security, finding and fixing vulnerabilities early in the development lifecycle to ensure compliance and robust security.