PyTorch Deep Learning

Attested

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

Claude Code SkillMCP ToolsRaw Bundle
Attested Source RepositoryOriginal Source

Install PyTorch Deep Learning

Each bundle is served from the attested copy of PyTorch Deep Learning, so the file you download is the one covered by the governance receipt on this page rather than whatever currently sits at the upstream source.

Claude Code skill

curl -L "https://atestiv.com/api/download/ecc-pytorch-patterns?format=claude-code" -o ecc-pytorch-patterns.skill.md

MCP tool definitions

curl -L "https://atestiv.com/api/download/ecc-pytorch-patterns?format=mcp" -o ecc-pytorch-patterns.mcp-tools.json

Raw attested bundle

curl -L "https://atestiv.com/api/download/ecc-pytorch-patterns?format=raw" -o ecc-pytorch-patterns.bundle.json

Verify the bundle against its manifest hash

shasum -a 256 ecc-pytorch-patterns.bundle.json

Provenance

  • Catalog identifier: ecc-pytorch-patterns
  • Harvested from the upstream repository.
  • The manifest hash below is what the signing key committed to. A bundle whose digest does not match it is not the attested PyTorch Deep Learning.
  • Anything published here has been through the same gate, so a missing receipt is itself a signal rather than an omission.

More Skills

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Attested

Quality management system patterns: nonconformance reporting, CAPA workflows, root cause analysis, disposition tracking, supplier quality management, and audit trail generation for ISO 9001 and regulated manufacturing.

RFC-Driven DAG Pipeline

Elevated · Review

RFC-driven multi-agent DAG execution: quality gates, merge queues, work unit orchestration, and structured parallel development.

Regex vs LLM Text Parsing

Elevated · Review

Decision framework for choosing between regex and LLM when parsing structured text. Start with regex for deterministic patterns, add LLM only for low-confidence edge cases.