Machine learning

train a Cluster Expansion (CE) for lattice-based Monte Carlo simulation of disordered materials.

Fine-tune Fairchem machine learning interatomic potentials (UMA, ESEN) on custom datasets.

Generate inorganic material structures using MatterGen, a diffusion-based generative model.

Fine-tune MACE machine learning interatomic potentials on custom datasets.

Fine-tune MatGL machine learning interatomic potentials on custom datasets.

Benchmark of inference speed of Machine Learning Interatomic Potentials (MLIPs).

GPT-5.6 全栈逆向+渗透融合 Skill（已并入 Code Sentinel）。 覆盖：通用/深度/脱壳/黑盒/JS/APK/Binary Diff 逆向；渗透/API/攻击链； 游戏/卡密/Pwn/IoT/恶意样本/EDR/LLM/供应链/Patch Diff/CTF 全速解题； Kali…

Detects prompt injection attacks targeting LLM-based applications using a multi-layered defense combining regex pattern matching for known attack signatures…

Build, maintain, and query a personal LLM-managed wiki — a markdown knowledge base where the LLM owns all writing, cross-referencing, and bookkeeping while…

Deep-crawl any website from start URLs, return per-page LLM-ready text/markdown/HTML plus metadata (title, description, author, language, canonical URL, OG)…

Shrink a HuggingFace safetensors model to 1 hidden layer for fast debugging without loading the full model into memory.

Wire Promptfoo and DeepTeam into CI/CD for automated regression red-teaming of LLM apps against OWASP LLM Top 10 and OWASP Agentic presets, failing the build…

Test vector stores for embedding inversion, cross-tenant leakage, and poisoning.

Deploy Llama Guard, NeMo Guardrails, and LLM Guard input/output scanners as runtime defenses.

Identify poisoned training data and backdoored models across the ML pipeline.

Evaluate Anvia agents and retrieval — deterministic metrics, semantic similarity, LLM judges, RAG quality, and CLI eval runs.

Perform finance web searches and local context searches.

Analyze finance text sentiment using FinBERT or LLM.

Build retrieval and RAG with Anvia — chunking, embeddings, vector stores, knowledge graphs, filters, and search tools.

Scaffold, structure, and manage AI/ML projects that run on AzureML.

Generates code that transforms datasets between ML schemas for model training or evaluation.

Transform audio recordings into professional Markdown documentation with intelligent summaries using LLM integration

Build RAG / unstructured-document evaluation datasets and demo documents (e.g.

Master context engineering for AI agent systems.