Machine learning

Use when building self-correcting retrieval systems for AI agents.

AIChat is a comprehensive LLM command-line tool written in Rust that combines chat-REPL, shell command generation, RAG, AI tools, and multi-provider support…

Use when optimizing token usage, KV cache efficiency, or context window management for LLM agents.

Use when needing global or relational understanding of codebases or knowledge corpora.

Use when validating, benchmarking, or monitoring LLM application performance.

Use when the user wants to optimize configurable system parameters against a measurable scalar objective, especially for model training, inference…

Implements the NOWAIT technique for efficient reasoning in R1-style LLMs.

Bundle the canonical memory set into one structured Markdown document for external-LLM review, audit, or dashboard download.

Inspect and manage Hugging Face Hub models, datasets, Spaces, repositories, cache, authentication, and downloads with the current hf CLI.

Explore, validate, filter, paginate, export, and prepare Hugging Face datasets through the Dataset Viewer API and Hub tooling.

Plan, launch, inspect, monitor, and cancel authenticated Hugging Face Jobs for CPU, GPU, TPU, batch, inference, and data workloads.

Design and run reproducible LLM fine-tuning with TRL, PEFT, Hugging Face Jobs, validated datasets, experiment tracking, and durable Hub outputs.

Instrument, monitor, compare, and diagnose machine-learning experiments with Trackio metrics, alerts, dashboards, and structured exports.

Prepare, train, evaluate, and publish computer-vision and vision-language models with Hugging Face datasets, Transformers, PEFT, and Jobs.

Build and validate local browser or Bun inference with Hugging Face Transformers.js, WebGPU or WASM backends, quantization, caching, and cleanup.

Writing an ML research paper as an argument: finding the one-sentence contribution, shaping the narrative, structuring sections, and keeping every citation…

Select and optimize embedding models for semantic search and RAG applications.

Build end-to-end MLOps pipelines from data preparation through model training, validation, and production deployment.

Use when turning a transcribed source — a thesis, paper, standard, manual, or a folder of Markdown chapters under docs/references — into a Karpathy-style LLM…

Scan source files (Python, C/C++, TypeScript, Rust) and generate semantic logic index via LLM.

Use this skill when the user wants an AI governance, legal-risk, privacy, compliance, procurement, or vendor-risk review of an internal AI use case, an AI…

Use for "interrogate", "adversarial review", "multi-model review", "challenge this", "stress test this code", "find blind spots", or "tear this apart".

Brev instance operating guidance for NeMo-RL agents working in /home/ubuntu/RL with limited workspace disk, a larger /ephemeral volume, and optional…

Build and integrate Stream Chat, Video, and Feeds in Android apps.