johnzfitch

This skill should be used when the user asks about "Burn tensors", "tensor operations", "Module derive", "burn config", "autodiff", "backward pass", "gradient", "record serialization", "model weights", or core Burn application development patterns.

This skill should be used when the user asks about "Burn backend", "WGPU", "NdArray", "Candle", "LibTorch", "custom kernel", "CubeCL", "quantization", "WebAssembly", "no_std", or backend selection and extension.

This skill should be used when the user asks about "contributing to Burn", "adding an operation", "burn internals", "burn-tensor", "burn-autodiff", "ONNX converter", "NodeProcessor", or Burn framework development.

Diagnoses Burn deep learning errors including tensor shape mismatches, backend panics, autodiff issues, and training failures. Use when encountering runtime errors, compilation failures, or unexpected behavior in Burn code.

This skill should be used when the user asks about "ONNX import", "PyTorch to Burn", "safetensors", "model conversion", "burn-import", "unsupported operator", "opset version", or importing external models into Burn.

Specializes in ONNX model import failures, unsupported operators, opset version mismatches, and dynamic shape issues. Use when burn-import fails or produces incorrect results.

Reviews Burn deep learning code for idiomatic patterns, performance, backend portability, and best practices. Use when asked for code review, refactoring suggestions, or "is this idiomatic".

This skill should be used when the user mentions "burn", "tensor", "module", "backend", "training", "ONNX", "autodiff", "deep learning in Rust", or any Burn framework related query. Routes to appropriate domain skills and enforces evidence-seeking behavior.

GitHub Markdown expert that produces distinctive output using the full GFM capability surface -- semantic HTML tags, cross-layer combos, and features most people don't know exist. Use when creating or improving any GitHub Markdown: READMEs, GitHub Pages, wiki pages, issue/PR templates, release notes, CONTRIBUTING, SECURITY, CHANGELOG, CODEOWNERS, or any .md file that should use semantic HTML, alerts, details/summary, mermaid, math, or cross-layer formatting. Also use when asked to "make a README", "improve this markdown", "format for GitHub", or "use proper GFM."

Research sub-agent for github-grandpa. Reads the complete GFM knowledge base and returns a compressed, task-specific briefing. Do not invoke directly -- invoked by the github-grandpa bootstrap skill.

GitHub Markdown expert that produces distinctive output using the full GFM capability surface -- semantic HTML tags, cross-layer combos, and features most people don't know exist. Use when creating or improving any GitHub Markdown: READMEs, GitHub Pages, wiki pages, issue/PR templates, release notes, CONTRIBUTING, SECURITY, CHANGELOG, CODEOWNERS, or any .md file that should use semantic HTML, alerts, details/summary, mermaid, math, or cross-layer formatting. Also use when asked to "make a README", "improve this markdown", "format for GitHub", or "use proper GFM."

This skill provides an advanced financial modeling suite with DCF analysis, sensitivity testing, Monte Carlo simulations, and scenario planning for investment decisions

Use llmx for token-efficient code intelligence on a codebase — semantic/keyword search, symbol lookup, and call-graph traversal via its local MCP tools (llmx_status, llmx_search, llmx_lookup, llmx_refs, llmx_explore, llmx_symbols, llmx_get_chunk, llmx_index, llmx_manage). Prefer it over raw grep/Read when exploring an unfamiliar or large repo, answering "where is X defined", "who calls Y", "what does this module do", or when you need semantic ("find the code that handles auth retries") rather than exact-string matches. Indexing and on-device embeddings run locally; no code leaves the machine.