Skill UI

idea-os is a comprehensive, five-phase pipeline designed to transform a raw, abstract idea into a fully structured, build-ready plan. It guides users through Triage, Clarification, Deep Research, detailed PRD creation (including non-goals and metrics), and finally, a phased execution plan. The system adapts its complexity and vocabulary to the user's sophistication level, providing everything from foundational market analysis to actionable technical stack recommendations.

End-to-end iOS security assessment pipeline covering app inventory, IPA acquisition, static analysis, and secret extraction. Requires explicit authorization for offensive testing and bug bounty scopes.

Master Jenkins skills to build, test, and deploy applications using declarative or scripted pipelines. Configure agents, manage plugins, handle credentials, and implement shared libraries for efficient CI/CD workflows in DevOps environments.

`jq` is the standard and powerful CLI tool for querying, filtering, and reshaping JSON data. This skill provides expert-level practical patterns for working with deeply nested structures, handling complex transformations (like flattening, renaming, and grouping), and integrating JSON logic into shell pipelines. Essential for parsing API outputs (AWS, GitHub), log files, and building robust Bash scripts.

Master modern Julia 1.10+ development with performance optimization, multiple dispatch, and production-ready practices. Covers scientific computing, data science, machine learning, web development, and package management. Focus on type stability, modern tooling, testing, and CI/CD pipelines for high-performance applications.

This skill enables comprehensive load testing for APIs, browsers, and scalability using k6. It allows writing realistic load scenarios, analyzing performance results, and integrating tests into CI/CD pipelines. Ideal for validating SLA requirements and comparing system behavior under various load conditions.

Master the LangChain framework to build sophisticated LLM applications. Learn to implement agents, chains, memory management, and tool integration. This skill covers core concepts like ReAct agents, retrieval chains, and document processing pipelines. Ideal for creating autonomous AI agents and production-grade applications using Python.

Instrument LLM applications with Langfuse tracing, prompt versioning and evaluation. Covers SDK setup, masking sensitive data, span verification, dataset comparison and debugging production LLM pipelines.

Automate LinkedIn tasks via CLI: fetch profiles, search people and companies, send messages, manage connections, create posts, react, and comment. Ideal for integrating LinkedIn workflows into automated pipelines.

Expert skill for building production-grade AI systems using LangChain 0.1+ and LangGraph. Covers agent architecture, RAG pipelines, memory systems, and deployment strategies. Ideal for developing sophisticated LLM applications with observability and scalability.

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.

This skill performs an autonomous repository-wide audit and fix pipeline. It audits code health, reviews logic, locates issues, explains findings, applies fixes, verifies diffs, and iterates until clean. It requires initial consent but runs hands-free afterwards, suitable for comprehensive code quality improvement and bug resolution.