Skill UI

This system creates and registers high-quality, reusable synthetic voices (voice clones) from local audio samples. It operates under a strict, consent-first workflow, requiring explicit speaker consent, detailed use permissions, and source verification before cloning. It is designed for professional Text-to-Speech (TTS) applications, IVR systems, or training content development where a consistent, branded voice is essential.

Produces a comprehensive and structured change management plan for any organizational transformation or system rollout. This skill guides users through critical phases, including stakeholder analysis, impact assessment, communication strategy, training plan development, and resistance mitigation, ensuring a high probability of successful adoption.

This skill generates comprehensive, step-by-step process guides suitable for training new employees or standardizing complex operations. It captures not only the main workflow steps, roles, and tools, but also essential elements like scope definition, prerequisites, edge cases, and escalation paths, ensuring clarity and completeness in business documentation.

This skill details the process of building and maintaining a comprehensive anti-phishing training program. It covers establishing baseline susceptibility assessments, deploying regular, progressive simulations, and providing interactive, role-based learning modules (using platforms like KnowBe4 or Proofpoint). The focus is on measuring and improving the human layer of defense against social engineering and sophisticated phishing attempts, thereby fostering a continuous security culture.

This skill manages the entire lifecycle of long-running deep learning training and inference jobs deployed on remote, ephemeral computing environments (e.g., AutoDL, RunPod, bare SSH, Slurm, K8s). It provides critical capabilities like resilient checkpointing, managing spot instance preemption, debugging resource failures (OOM, NaN), and ensuring safe billing teardown, making it essential for professional ML Ops on rented resources.

A comprehensive guide for training or fine-tuning advanced sentence-transformer models. This skill assists in selecting the correct architecture—including Bi-encoders (SentenceTransformer), CrossEncoders (for reranking), and SparseEncoders—for various Natural Language Processing (NLP) tasks. Use cases include semantic retrieval, similarity matching, document clustering, and pair-wise classification.

This skill provides expert capabilities for training and fine-tuning transformer language models using the TRL (Transformers Reinforcement Learning) library. It supports state-of-the-art post-training methods, including Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), Group Relative Policy Optimization (GRPO), and Reward Model training. Use this skill to align and customize foundation models for advanced tasks via CLI commands.

A comprehensive presentation template designed for educational workshops and training sessions. It features a warm paper aesthetic, persistent learning objectives on the left sidebar, core content slides, interactive Multiple Choice Question (MCQ) self-assessment pages, and module wrap-ups, ensuring a structured and engaging learning experience.

This skill fetches the latest, live documentation and code examples for specific libraries and frameworks (e.g., Next.js, React, Prisma). Instead of relying on static or outdated training data, it utilizes the Context7 MCP to provide accurate, up-to-date answers for setup questions, API references, and code generation based on current best practices.

This skill runs an automated evolution process designed to systematically optimize the seven core policy surfaces of an AI agent harness (e.g., planner, contextBuilder, reviewer). By mutating these policies and scoring the variants in a sandbox, it identifies crucial performance improvements without requiring expensive foundation model retraining. It is ideal for empirical configuration tuning and establishing optimized baselines.

Implements the DISTILL and CONSOLIDATE phases of the intelligence pipeline. This tool enables the training and consolidation of learned neural patterns. It supports various adaptation methods, including single-domain SONA for real-time learning and multi-domain MicroLoRA for robust, long-term knowledge storage, preventing catastrophic forgetting.

Dispatches computationally intensive quantitative financial jobs (such as multi-year walk-forward backtesting, large Monte-Carlo simulations, parameter sweeps, and deep model training using neural-trader) to a managed cloud agent. This is necessary for workloads that exceed local computational limits, providing robust, scalable infrastructure for professional quantitative research.