Skill UI

This module provides production-grade patterns for integrating the Juicebox AI-powered people search API. It includes a singleton client for centralized rate-limit handling, a robust request builder for complex criteria (location, company, experience years), and structured error wrappers for reliable API calls (retries, error context). Ideal for building scalable recruitment, market research, or data enrichment tools.

Identifies high-quality leads by analyzing your product/service, defining ideal customers, and surfacing prioritized target companies with contact strategies, making it ideal for sales, business development, and marketing outreach.

Loki Mode is a multi-agent autonomous startup system that progresses PRD to production with zero human intervention, orchestrating research-enhanced components such as OpenAI SDK, DeepMind, Anthropic, AWS Bedrock, and agent tooling while enforcing RARV reasoning and efficiency tracking.

This comprehensive guide covers the entire MLOps lifecycle, specializing in building robust, scalable, and production-grade Machine Learning systems. Expertise includes orchestrating complex ML pipelines using Kubeflow, Airflow, Prefect, and cloud-native services (AWS SageMaker, Azure ML, GCP Vertex AI). It covers experiment tracking (MLflow, W&B), model versioning, model registries, infrastructure as code (Terraform), and container deployment using Kubernetes. Ideal for engineers tasked with moving ML models from research to reliable, managed production environments.

This guide outlines the comprehensive reference architecture for building advanced, AI-powered search and research applications, modeled after Perplexity. It covers three core tiers: a searchable service with model routing and caching, a citation pipeline for validation and rendering, and an orchestrator for multi-query research synthesis. Use this when designing complex, production-grade, search-augmented LLM integrations or reviewing advanced system patterns.

A comprehensive toolkit for modern product management. It guides users through essential workflows, including feature prioritization using RICE, deep customer discovery analysis from interviews, and structured PRD creation. Ideal for defining product strategy, synthesizing user research, and documenting requirements from concept to launch.

Guides post-training of GLM-class LLMs with Megatron-LM and SGLang, focusing on GRPO rollout generation, custom data buffers, and tight RL scaling workflows for research and production use.

An expert-level skill set covering the entire spectrum of user-centered design. Mastery includes building scalable design systems using atomic principles and design tokens, conducting deep user research (usability testing, persona creation), ensuring WCAG accessibility compliance, and managing cross-platform implementation (web, mobile, desktop). Ideal for developing complex, inclusive, and highly structured digital products.

This comprehensive toolkit guides senior UX designers and researchers through the entire product development lifecycle. It covers generating data-backed personas, mapping user journeys, planning structured usability tests, and synthesizing raw research data into actionable design recommendations. Use it when conducting deep user research or validating product designs.

Provides Python access to openFDA endpoints for drugs, devices, foods, veterinary products, and substances, enabling regulatory analysis, safety monitoring, recalls tracking, and pharmacovigilance research.

PyTorch Lightning Toolkit streamlines PyTorch model training by organizing LightningModules, LightningDataModules, Trainer configs, callbacks, logging, and distributed strategies for scalable multi-GPU/TPU runs in research or production workflows.

This skill is designed to streamline the process of market research. It helps users gather comprehensive data on target markets, analyze industry trends, and identify key consumer insights. Use it to inform product development, refine business strategies, and make data-driven decisions.