Skill UI

Expert guidance for fine-tuning LLMs with Axolotl, covering YAML configs, LoRA/QLoRA/DPO variants, multimodal support, and practical troubleshooting patterns for development and training workflows.

Expert guidance for fine-tuning LLMs with Axolotl, covering YAML configs, LoRA/QLoRA/DPO variants, multimodal support, and practical troubleshooting patterns for development and training workflows.

A comprehensive guide to configuring and maximizing the potential of Claude Code, an advanced agentic coding tool. This resource details best practices, setup templates (CLAUDE.md), advanced prompting techniques (e.g., thinking keywords), and debugging strategies for building reliable, complex software autonomously.

Guides engineers through advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability across few-shot learning, reasoning patterns, templates, and system prompt design.

Provides production-ready Megatron-Core workflows for training 2B–462B parameter LLMs with tensor/pipeline/expert/context parallelism, maximizing GPU efficiency on H100 clusters.

Hardware-agnostic quantum ML framework enabling gradient-based training of quantum circuits, hybrid quantum-classical models, and seamless integration with PyTorch/JAX/TensorFlow alongside device portability across IBM/Google/Rigetti/IonQ.

This methodology converts verbose, natural language system instructions into an atomic, structured-label format (e.g., trigger:, action:). By structuring rules, it bypasses the limitations of natural language processing, ensuring LLMs allocate full attention to every constraint, thereby maximizing compliance, improving reliability, and reducing token usage.

RecallMax supercharges AI agents by injecting 500K–1M clean tokens, adaptively summarizing each turn, compressing history, and verifying facts so long conversations stay coherent without hallucinations.

Autonomously orchestrates multi-round research reviews via Codex MCP: review, implement fixes, and re-review until the reviewer declares the work ready or the max rounds expire. Logs progress, maintains compact/recoverable state, and optionally adds human checkpoints for iterative improvement requests.

This skill executes an autonomous, iterative research review loop, simulating a rigorous peer-review process. It repeatedly uses an external OpenAI-compatible LLM via API calls to score, critique, and identify weaknesses in a given research topic or document. The process continues to refine the content by implementing suggested fixes until the LLM provides a positive assessment or the maximum round limit is reached. Ideal for academic writing, technical documentation, and refining complex proposals.

Autonomous loop that submits research context to MiniMax-M2.7, parses reviewer feedback, and iterates through fixes until a positive assessment or max rounds is reached, using compact state persistence for long runs.

Connect to the MAXIA AI-to-AI marketplace on Solana to discover, buy, and sell agent services, earn USDC, and query sentiment, DeFi yields, rug detection, GPU rental tiers, and wallet analysis through its public APIs.