Machine learning

Benchmarks LLM inference and drives GPU kernel optimization with Magpie.

Diagnoses why ROCm, the HIP SDK, PyTorch, or llama.cpp is broken on an AMD GPU on Linux, Windows, or WSL2, then applies a low-risk fix with consent or hands…

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.

Turn a refined research proposal or method idea into a detailed, claim-driven experiment roadmap.

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, Antigravity, Cursor, and AGENTS.md-compatible tools.

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

Writes rigorous mathematical proofs for ML/AI theory.

Persistent research knowledge base that accumulates papers, ideas, experiments, claims, and their relationships across the entire research lifecycle.

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.

Deploy and run ML experiments on local, remote, Vast.ai, or Modal serverless GPU.

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.

Autonomous research review loop using any OpenAI-compatible LLM API.

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