Machine learning

Orchestrates modular PyTorch profiler trace analysis with TraceLens: generates perf reports, prepares category data, runs system-level and compute-kernel…

Agent Platform Model Registry Management.

Manage and query Agent Platform RAG Engine Corpora and retrieve grounded contexts using the Google GenAI SDK.

Quick single-paper lookup via AlphaXiv LLM-optimized summaries with tiered source fallback.

Analyze ML experiment results, compute statistics, generate comparison tables and insights.

Generate a long-form Chinese interview-prep cheat sheet on a specific ML/LLM topic — formulas with derivations, from-scratch PyTorch code, comparison tables…

Use when designing how one canonical agent core runs across Codex, Claude Code, Gemini CLI, Cursor, and AGENTS.md-compatible tools.

Use when designing how one canonical agent core runs across Codex, Claude Code, Antigravity, Cursor, and AGENTS.md-compatible tools.

Run GPU workloads on Modal — training, fine-tuning, inference, batch processing.

Diagnose, troubleshoot, and advise on any Qdrant deployment by loading the latest official Qdrant skills live from skills.qdrant.tech.

Measures and improves the quality of AI models and agents on Google Cloud using the Eval Quality Flywheel methodology.

Agent Platform Model Tuning.

Deploy and run ML experiments on local or remote GPU servers.

科研分析能力入口：统计、因果推断、数据清洗与可视化。Use for statistics, econometrics, wrangling, causal inference, charts, and publication figures.

This skill should be used for project-level decisions about LLM-powered systems: whether an LLM is the right primitive for the task at hand, the shape of a…

Use when building or maintaining a self-contained personal knowledge base (an LLM wiki) in plain markdown.

Generates publication-quality figures for ML papers from research context.

Open-source embedding database for AI applications.

Facebook's library for efficient similarity search and clustering of dense vectors.

Post-training 4-bit quantization for LLMs with minimal accuracy loss.

Compress large language models using knowledge distillation from teacher to student models.

Framework for building LLM-powered applications with agents, chains, and RAG.

Battle-tested PyTorch training recipes for all domains — LLMs, vision, diffusion, medical imaging, protein/drug discovery, spatial omics, genomics.

Reduce LLM size and accelerate inference using pruning techniques like Wanda and SparseGPT.