GitHub
generating-ralph-charts
Generates RALPH charts (factor-level analysis diagrams) for HAYST method test design. Use when designing combination tests, identifying test coverage gaps, or analyzing system behavior factors.
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retrieving-knowledge
Retrieves project knowledge resources by browsing categories, searching tags, or filtering by keywords. Use when exploring existing knowledge or finding specific documentation entries.
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GitHub
reviewing-flaky-patterns
Detects test flakiness patterns including timing issues, environmental dependencies, state pollution, and non-deterministic behaviors. Use as part of parallel test review execution.
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GitHub
reviewing-ui-coverage
Reviews UI test scenarios for RALPH chart and AC coverage. Detects uncovered constraints and acceptance criteria. Use as part of parallel UI test scenario review.
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GitHub
reviewing-ui-scenario-integration
Reviews UI test scenarios for consolidation opportunities. Identifies scenarios that can be merged to reduce operation counts. Use as part of parallel UI test scenario review.
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GitHub
reviewing-documents
Systematic documentation review following document-writer principles. Use when user requests documentation review, quality check, or principle compliance verification.
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GitHub
reviewing-comments
Comment quality review. Detects LLM generation traces, implementation history, conversational tone, HOW vs WHY patterns, and redundant comments.
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GitHub
reviewing-code
Generic code review for production code. Use as part of parallel code review execution.
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GitHub
implementing-tdd
TDD workflow executor using Canon TDD (Red-Green-Refactor). Supports test list creation, failing test writing, minimal implementation, and refactoring with test verification. Use when implementing features with...
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GitHub
managing-tickets
Manages project tickets - list, show, create, update, and move. Use when listing tickets, showing ticket details, creating new tickets, updating ticket content, or moving tickets between statuses.
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GitHub
reviewing-ui-wording
Reviews UI test scenarios for wording accuracy. Detects variables and dynamic values that cannot be verified from actual UI. Use as part of parallel UI test scenario review.
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GitHub
reviewing-assertion-quality
Reviews test code for assertion quality including precision levels, property coverage, and validation patterns. Use as part of parallel test review execution.
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GitHub
reviewing-ui-behavior
Reviews UI test scenarios for behavioral appropriateness. Detects design tests (colors, fonts, layout) that should not be in scenario tests. Use as part of parallel UI test scenario review.
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GitHub
reviewing-test
Generic code review for test code. Use as part of parallel test review execution.
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GitHub
template
A template skill that demonstrates the SKILL.md format. Use this as a starting point for creating new skills.
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GitHub
c2pa-spec-compliance
Verifies software compliance with the C2PA specification. Invoke during planning or code review to check implementations against the spec. Provide code/design context and optionally a target version.
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GitHub
burn-onnx-surgeon
Specializes in ONNX model import failures, unsupported operators, opset version mismatches, and dynamic shape issues. Use when burn-import fails or produces incorrect results.
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GitHub
burn-onnx
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...
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GitHub
burn-app-dev
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...
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GitHub
burn-debugger
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...
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GitHub
burn-router
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...
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GitHub
burn-training
This skill should be used when the user asks about "training loop", "Learner", "metrics", "dataset", "dataloader", "checkpointing", "optimizer", "learning rate scheduler", "custom training", or Burn training wo...
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GitHub
burn-backends
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...
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GitHub
burn-contrib
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 developme...
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