PyTorch Deep Learning
PyTorch patterns: training pipelines, model architectures, data loading, mixed precision, distributed training, and reproducibility best practices.
Governance Receipt
- Signer
- sovereign-claw-ed25519
- Signed At
- 6/4/2026
- Risk Tier
- T1
- Receipt Hash
- 3d96df71
- Manifest Hash
- 5ac761f6b250a393e5a14228da1d4a26109b89ca35c6874f41d89c6f32c4f726
- Signature
- YZArmGr8
- Root Public Key
- 349b0348
Skill Details
- Gate Verdict
- Attested
- Publication State
- published
- Risk Tier
- T1
- Manifest Hash
- 5ac761f6
Download Attested Bundle
More Skills
MCP Builder
Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
Theme Factory
Toolkit for styling artifacts with a theme. These artifacts can be slides, docs, reportings, HTML landing pages, etc. There are 10 pre-set themes with colors/fonts that you can apply to any artifact that has been creating, or can generate a new theme on-the-fly.
Claude API
Reference for the Claude API / Anthropic SDK — model ids, pricing, params, streaming, tool use, MCP, agents, caching, token counting, model migration. TRIGGER — read BEFORE opening the target file; don't skip because it "looks like a one-liner" — whenever: the prompt names Claude/Anthropic in any form (Claude, Anthropic, Opus, Sonnet, Haiku, `anthropic`, `@anthropic-ai`, `claude-*`, `us.anthropic.*`, `[1m]`); the user asks about an LLM (pricing/model choice/limits/caching) — never answer from memory; OR the task is LLM-shaped with provider unstated (agent/MCP/tool-definition/multi-agent/RAG…