Machine learning

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…

Generate AI videos using ByteDance Seedance models via Volcengine Ark API.

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.

Use when the user wants to migrate code that calls OpenAI, Gemini/Google AI, or the Anthropic API to Amazon Bedrock — a pure model/SDK rewrite.

Trains and fine-tunes vision models for object detection (D-FINE, RT-DETR v2, DETR, YOLOS), image classification (timm models — MobileNetV3, MobileViT…

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…

Trustpilot customer reviews scraper for any company listed on trustpilot.com — given a company domain (e.g.

Publish and manage research papers on Hugging Face Hub.

Use for debugging DSPy programs, inspect_history, tracing LLM calls, custom callbacks, observability, monitoring, and cost tracking.

Use for DSPy retrieval with dspy.Embedder, dspy.Embeddings, FAISS indexes, semantic search, and local or hosted embedding models.

Use for GEPA reflective optimization, ReAct agent optimization, feedback metrics, LLM reflection, and execution trajectories.

Use for RAG pipelines, retrieval augmented generation, ColBERTv2, context retrieval, multi-hop RAG, and grounded DSPy answers.

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.

Expert guidance for GRPO/RL fine-tuning with TRL for reasoning and task-specific model training