All Skills

Fine-tune and align language models using reinforcement learning with TRL, including SFT, DPO, PPO, GRPO, and reward model training.

Detect prompt injections and jailbreak attempts in LLM applications using Meta's 86M parameter classifier. Filter user inputs, third-party data, and RAG documents with low latency and multilingual support.

Extract structured data from LLM responses with Pydantic validation, automatic retries, and streaming support across multiple providers.

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

Optimizes LLM inference with NVIDIA TensorRT for maximum throughput and lowest latency on NVIDIA GPUs (A100/H100).

Add distributed training support to any PyTorch script with minimal code changes using a unified API for DDP, DeepSpeed, FSDP, and mixed precision.

Add programmable safety guardrails to LLM applications at runtime, including jailbreak detection, input/output validation, fact-checking, hallucination detection, PII filtering, and toxicity detection.

Extend context windows of transformer models using RoPE, YaRN, ALiBi, and position interpolation techniques for processing long documents and implementing efficient positional encodings.

Train Mixture of Experts (MoE) models using DeepSpeed or HuggingFace, covering architectures, routing, load balancing, and expert parallelism.

Merge multiple fine-tuned models using mergekit to combine capabilities without retraining, covering SLERP, TIES-Merging, DARE, Task Arithmetic, linear merging, and production deployment strategies.

Train AI models to be harmless through self-critique and AI feedback using a set of constitutional principles, without requiring human labels for harmful outputs.

Trains large language models (2B-462B parameters) using NVIDIA Megatron-Core with advanced parallelism strategies for maximum GPU efficiency.

Adds PyTorch FSDP2 (fully_shard) to training scripts with correct init, sharding, mixed precision/offload config, and distributed checkpointing. Use when models exceed single-GPU memory or when you need DTensor-based sharding with DeviceMesh.

Perform causal interventions on PyTorch models using pyvene's declarative framework for causal tracing, activation patching, and interchange intervention training.

Run interpretability experiments on neural network internals using nnsight, with optional NDIF remote execution for massive models.

Train and analyze Sparse Autoencoders (SAEs) using SAELens to decompose neural network activations into interpretable features for mechanistic interpretability research.

Provides paragraph-level structural blueprints for writing systems papers targeting OSDI, SOSP, ASPLOS, NSDI, and EuroSys, including page allocation, writing patterns, and venue-specific checklists.

Compiles research inputs—PDFs, code, logs, notes—into structured Agent-Native Research Artifacts with cognitive and physical layers.

Accelerate LLM inference using speculative decoding, Medusa multiple heads, and lookahead decoding techniques for 1.5-3.6× speedup without quality loss.

Inspect and manipulate transformer internals via HookPoints and activation caching for mechanistic interpretability research.

Performs a semantic epistemic review of Agent-Native Research Artifacts, scoring six dimensions and producing a constructive report with a recommendation.

Guides researchers through structured ideation frameworks to discover high-impact research directions.

Train a personalized identity model on a person's face for use in Higgsfield's image and video generation.

Generate images, videos, 3D assets, and audio via the Higgsfield AI CLI, including Marketing Studio ads and Virality Predictor analysis.

Build, edit, and deploy full-stack websites or web apps using the Higgsfield CLI, with two distinct product types: standalone websites or Higgsfield-integrated apps.

Install and configure Acontext, a memory layer for AI agents that provides persistent sessions, file storage, and skill management.

Records daily activities, progress, decisions, and learnings in structured chronological markdown files.

Capture and organize general facts about the user by topic, such as preferences, background, and goals, to personalize interactions.

Write Python code in n8n Code nodes, covering data access patterns, return formats, and standard library limitations.

Deploy a production self-hosted n8n instance on a fresh Linux VM over SSH, using Docker Compose behind a Caddy reverse proxy with automatic HTTPS.

Build reusable, composable n8n sub-workflows with typed inputs, stateless or stateful design, and discoverable naming conventions.

Handle files and binary data in n8n workflows correctly, covering the $binary vs $json split, reading/writing binary, preserving binary across transforms, and the agent-tool binary boundary.

Write JavaScript code in n8n Code nodes for data transformation, API calls, and workflow logic.

Provides expert guidance for using n8n-mcp MCP server tools to build, validate, and manage n8n workflows efficiently.

Interpret n8n validation errors and warnings, guide iterative fixes, and explain validation profiles, error types, and auto-sanitization.

Provides proven architectural patterns for building n8n workflows, covering webhook processing, HTTP API integration, database operations, AI agent workflows, batch processing, and scheduled tasks.