[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6AFHZ628J6HT0WDJW7TMPQ8":3,"skill-same:01M6AFHZ628J6HT0WDJW7TMPQ8":106},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":18,"publishedAt":47,"linkOnly":48,"markdownContent":49,"markdownBody":50,"frontMatter":51,"refName":52,"commitSha":53,"skillDirectory":54,"forksCount":55,"repositoryHtmlUrl":56,"repositoryHomepageUrl":57,"ownerHtmlUrl":58,"ownerBlogUrl":59,"ownerDescription":60,"updatedAt":61,"install":62,"resources":75},"01M6AFHZ628J6HT0WDJW7TMPQ8","pennylane",null,"Cross-platform Python library for quantum computing, quantum machine learning, and quantum chemistry. Enables building and training quantum circuits with automatic differentiation, seamless integration with PyTorch\u002FJAX\u002FTensorFlow, and device-independent execution across simulators and quantum hardware (IBM, Amazon Braket, Google, Rigetti, IonQ, etc.). Use when working with quantum circuits, variational quantum algorithms (VQE, QAOA), quantum neural networks, hybrid quantum-classical models, molecular simulations, quantum chemistry calculations, or any quantum computing tasks requiring gradient-based optimization, hardware-agnostic programming, or quantum machine learning workflows.","davila7\u002Fclaude-code-templates","davila7","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F6216945?v=4","01M6ADEWWFPS191NFM0Y1EX1JH","Daniel Avila","01M6ADJHGM78PHQRD3XZTFDVKK",33332,"MIT","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fpennylane\u002FSKILL.md",[],[5,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46],"ibm","Simulations","when","hybrid","automatic","gradient","quantum algorithms","differentiation","amazon braket","molecular","across","algorithms","networks","execution","google","Optimization","integration","library","quantum computing","quantum chemistry","tasks","training","pytorch","neural networks","workflows","machine learning","tensorflow","python","2026-11-10 10:12:30",false,"---\nname: pennylane\ndescription: Cross-platform Python library for quantum computing, quantum machine learning, and quantum chemistry. Enables building and training quantum circuits with automatic differentiation, seamless integration with PyTorch\u002FJAX\u002FTensorFlow, and device-independent execution across simulators and quantum hardware (IBM, Amazon Braket, Google, Rigetti, IonQ, etc.). Use when working with quantum circuits, variational quantum algorithms (VQE, QAOA), quantum neural networks, hybrid quantum-classical models, molecular simulations, quantum chemistry calculations, or any quantum computing tasks requiring gradient-based optimization, hardware-agnostic programming, or quantum machine learning workflows.\n---\n\n# PennyLane\n\n## Overview\n\nPennyLane is a quantum computing library that enables training quantum computers like neural networks. It provides automatic differentiation of quantum circuits, device-independent programming, and seamless integration with classical machine learning frameworks.\n\n## Installation\n\nInstall using uv:\n\n```bash\nuv pip install pennylane\n```\n\nFor quantum hardware access, install device plugins:\n\n```bash\n# IBM Quantum\nuv pip install pennylane-qiskit\n\n# Amazon Braket\nuv pip install amazon-braket-pennylane-plugin\n\n# Google Cirq\nuv pip install pennylane-cirq\n\n# Rigetti Forest\nuv pip install pennylane-rigetti\n\n# IonQ\nuv pip install pennylane-ionq\n```\n\n## Quick Start\n\nBuild a quantum circuit and optimize its parameters:\n\n```python\nimport pennylane as qml\nfrom pennylane import numpy as np\n\n# Create device\ndev = qml.device('default.qubit', wires=2)\n\n# Define quantum circuit\n@qml.qnode(dev)\ndef circuit(params):\n    qml.RX(params[0], wires=0)\n    qml.RY(params[1], wires=1)\n    qml.CNOT(wires=[0, 1])\n    return qml.expval(qml.PauliZ(0))\n\n# Optimize parameters\nopt = qml.GradientDescentOptimizer(stepsize=0.1)\nparams = np.array([0.1, 0.2], requires_grad=True)\n\nfor i in range(100):\n    params = opt.step(circuit, params)\n```\n\n## Core Capabilities\n\n### 1. Quantum Circuit Construction\n\nBuild circuits with gates, measurements, and state preparation. See `references\u002Fquantum_circuits.md` for:\n- Single and multi-qubit gates\n- Controlled operations and conditional logic\n- Mid-circuit measurements and adaptive circuits\n- Various measurement types (expectation, probability, samples)\n- Circuit inspection and debugging\n\n### 2. Quantum Machine Learning\n\nCreate hybrid quantum-classical models. See `references\u002Fquantum_ml.md` for:\n- Integration with PyTorch, JAX, TensorFlow\n- Quantum neural networks and variational classifiers\n- Data encoding strategies (angle, amplitude, basis, IQP)\n- Training hybrid models with backpropagation\n- Transfer learning with quantum circuits\n\n### 3. Quantum Chemistry\n\nSimulate molecules and compute ground state energies. See `references\u002Fquantum_chemistry.md` for:\n- Molecular Hamiltonian generation\n- Variational Quantum Eigensolver (VQE)\n- UCCSD ansatz for chemistry\n- Geometry optimization and dissociation curves\n- Molecular property calculations\n\n### 4. Device Management\n\nExecute on simulators or quantum hardware. See `references\u002Fdevices_backends.md` for:\n- Built-in simulators (default.qubit, lightning.qubit, default.mixed)\n- Hardware plugins (IBM, Amazon Braket, Google, Rigetti, IonQ)\n- Device selection and configuration\n- Performance optimization and caching\n- GPU acceleration and JIT compilation\n\n### 5. Optimization\n\nTrain quantum circuits with various optimizers. See `references\u002Foptimization.md` for:\n- Built-in optimizers (Adam, gradient descent, momentum, RMSProp)\n- Gradient computation methods (backprop, parameter-shift, adjoint)\n- Variational algorithms (VQE, QAOA)\n- Training strategies (learning rate schedules, mini-batches)\n- Handling barren plateaus and local minima\n\n### 6. Advanced Features\n\nLeverage templates, transforms, and compilation. See `references\u002Fadvanced_features.md` for:\n- Circuit templates and layers\n- Transforms and circuit optimization\n- Pulse-level programming\n- Catalyst JIT compilation\n- Noise models and error mitigation\n- Resource estimation\n\n## Common Workflows\n\n### Train a Variational Classifier\n\n```python\n# 1. Define ansatz\n@qml.qnode(dev)\ndef classifier(x, weights):\n    # Encode data\n    qml.AngleEmbedding(x, wires=range(4))\n\n    # Variational layers\n    qml.StronglyEntanglingLayers(weights, wires=range(4))\n\n    return qml.expval(qml.PauliZ(0))\n\n# 2. Train\nopt = qml.AdamOptimizer(stepsize=0.01)\nweights = np.random.random((3, 4, 3))  # 3 layers, 4 wires\n\nfor epoch in range(100):\n    for x, y in zip(X_train, y_train):\n        weights = opt.step(lambda w: (classifier(x, w) - y)**2, weights)\n```\n\n### Run VQE for Molecular Ground State\n\n```python\nfrom pennylane import qchem\n\n# 1. Build Hamiltonian\nsymbols = ['H', 'H']\ncoords = np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.74])\nH, n_qubits = qchem.molecular_hamiltonian(symbols, coords)\n\n# 2. Define ansatz\n@qml.qnode(dev)\ndef vqe_circuit(params):\n    qml.BasisState(qchem.hf_state(2, n_qubits), wires=range(n_qubits))\n    qml.UCCSD(params, wires=range(n_qubits))\n    return qml.expval(H)\n\n# 3. Optimize\nopt = qml.AdamOptimizer(stepsize=0.1)\nparams = np.zeros(10, requires_grad=True)\n\nfor i in range(100):\n    params, energy = opt.step_and_cost(vqe_circuit, params)\n    print(f\"Step {i}: Energy = {energy:.6f} Ha\")\n```\n\n### Switch Between Devices\n\n```python\n# Same circuit, different backends\ncircuit_def = lambda dev: qml.qnode(dev)(circuit_function)\n\n# Test on simulator\ndev_sim = qml.device('default.qubit', wires=4)\nresult_sim = circuit_def(dev_sim)(params)\n\n# Run on quantum hardware\ndev_hw = qml.device('qiskit.ibmq', wires=4, backend='ibmq_manila')\nresult_hw = circuit_def(dev_hw)(params)\n```\n\n## Detailed Documentation\n\nFor comprehensive coverage of specific topics, consult the reference files:\n\n- **Getting started**: `references\u002Fgetting_started.md` - Installation, basic concepts, first steps\n- **Quantum circuits**: `references\u002Fquantum_circuits.md` - Gates, measurements, circuit patterns\n- **Quantum ML**: `references\u002Fquantum_ml.md` - Hybrid models, framework integration, QNNs\n- **Quantum chemistry**: `references\u002Fquantum_chemistry.md` - VQE, molecular Hamiltonians, chemistry workflows\n- **Devices**: `references\u002Fdevices_backends.md` - Simulators, hardware plugins, device configuration\n- **Optimization**: `references\u002Foptimization.md` - Optimizers, gradients, variational algorithms\n- **Advanced**: `references\u002Fadvanced_features.md` - Templates, transforms, JIT compilation, noise\n\n## Best Practices\n\n1. **Start with simulators** - Test on `default.qubit` before deploying to hardware\n2. **Use parameter-shift for hardware** - Backpropagation only works on simulators\n3. **Choose appropriate encodings** - Match data encoding to problem structure\n4. **Initialize carefully** - Use small random values to avoid barren plateaus\n5. **Monitor gradients** - Check for vanishing gradients in deep circuits\n6. **Cache devices** - Reuse device objects to reduce initialization overhead\n7. **Profile circuits** - Use `qml.specs()` to analyze circuit complexity\n8. **Test locally** - Validate on simulators before submitting to hardware\n9. **Use templates** - Leverage built-in templates for common circuit patterns\n10. **Compile when possible** - Use Catalyst JIT for performance-critical code\n\n## Resources\n\n- Official documentation: https:\u002F\u002Fdocs.pennylane.ai\n- Codebook (tutorials): https:\u002F\u002Fpennylane.ai\u002Fcodebook\n- QML demonstrations: https:\u002F\u002Fpennylane.ai\u002Fqml\u002Fdemonstrations\n- Community forum: https:\u002F\u002Fdiscuss.pennylane.ai\n- GitHub: https:\u002F\u002Fgithub.com\u002FPennyLaneAI\u002Fpennylane\n","# PennyLane\n\n## Overview\n\nPennyLane is a quantum computing library that enables training quantum computers like neural networks. It provides automatic differentiation of quantum circuits, device-independent programming, and seamless integration with classical machine learning frameworks.\n\n## Installation\n\nInstall using uv:\n\n```bash\nuv pip install pennylane\n```\n\nFor quantum hardware access, install device plugins:\n\n```bash\n# IBM Quantum\nuv pip install pennylane-qiskit\n\n# Amazon Braket\nuv pip install amazon-braket-pennylane-plugin\n\n# Google Cirq\nuv pip install pennylane-cirq\n\n# Rigetti Forest\nuv pip install pennylane-rigetti\n\n# IonQ\nuv pip install pennylane-ionq\n```\n\n## Quick Start\n\nBuild a quantum circuit and optimize its parameters:\n\n```python\nimport pennylane as qml\nfrom pennylane import numpy as np\n\n# Create device\ndev = qml.device('default.qubit', wires=2)\n\n# Define quantum circuit\n@qml.qnode(dev)\ndef circuit(params):\n    qml.RX(params[0], wires=0)\n    qml.RY(params[1], wires=1)\n    qml.CNOT(wires=[0, 1])\n    return qml.expval(qml.PauliZ(0))\n\n# Optimize parameters\nopt = qml.GradientDescentOptimizer(stepsize=0.1)\nparams = np.array([0.1, 0.2], requires_grad=True)\n\nfor i in range(100):\n    params = opt.step(circuit, params)\n```\n\n## Core Capabilities\n\n### 1. Quantum Circuit Construction\n\nBuild circuits with gates, measurements, and state preparation. See `references\u002Fquantum_circuits.md` for:\n- Single and multi-qubit gates\n- Controlled operations and conditional logic\n- Mid-circuit measurements and adaptive circuits\n- Various measurement types (expectation, probability, samples)\n- Circuit inspection and debugging\n\n### 2. Quantum Machine Learning\n\nCreate hybrid quantum-classical models. See `references\u002Fquantum_ml.md` for:\n- Integration with PyTorch, JAX, TensorFlow\n- Quantum neural networks and variational classifiers\n- Data encoding strategies (angle, amplitude, basis, IQP)\n- Training hybrid models with backpropagation\n- Transfer learning with quantum circuits\n\n### 3. Quantum Chemistry\n\nSimulate molecules and compute ground state energies. See `references\u002Fquantum_chemistry.md` for:\n- Molecular Hamiltonian generation\n- Variational Quantum Eigensolver (VQE)\n- UCCSD ansatz for chemistry\n- Geometry optimization and dissociation curves\n- Molecular property calculations\n\n### 4. Device Management\n\nExecute on simulators or quantum hardware. See `references\u002Fdevices_backends.md` for:\n- Built-in simulators (default.qubit, lightning.qubit, default.mixed)\n- Hardware plugins (IBM, Amazon Braket, Google, Rigetti, IonQ)\n- Device selection and configuration\n- Performance optimization and caching\n- GPU acceleration and JIT compilation\n\n### 5. Optimization\n\nTrain quantum circuits with various optimizers. See `references\u002Foptimization.md` for:\n- Built-in optimizers (Adam, gradient descent, momentum, RMSProp)\n- Gradient computation methods (backprop, parameter-shift, adjoint)\n- Variational algorithms (VQE, QAOA)\n- Training strategies (learning rate schedules, mini-batches)\n- Handling barren plateaus and local minima\n\n### 6. Advanced Features\n\nLeverage templates, transforms, and compilation. See `references\u002Fadvanced_features.md` for:\n- Circuit templates and layers\n- Transforms and circuit optimization\n- Pulse-level programming\n- Catalyst JIT compilation\n- Noise models and error mitigation\n- Resource estimation\n\n## Common Workflows\n\n### Train a Variational Classifier\n\n```python\n# 1. Define ansatz\n@qml.qnode(dev)\ndef classifier(x, weights):\n    # Encode data\n    qml.AngleEmbedding(x, wires=range(4))\n\n    # Variational layers\n    qml.StronglyEntanglingLayers(weights, wires=range(4))\n\n    return qml.expval(qml.PauliZ(0))\n\n# 2. Train\nopt = qml.AdamOptimizer(stepsize=0.01)\nweights = np.random.random((3, 4, 3))  # 3 layers, 4 wires\n\nfor epoch in range(100):\n    for x, y in zip(X_train, y_train):\n        weights = opt.step(lambda w: (classifier(x, w) - y)**2, weights)\n```\n\n### Run VQE for Molecular Ground State\n\n```python\nfrom pennylane import qchem\n\n# 1. Build Hamiltonian\nsymbols = ['H', 'H']\ncoords = np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.74])\nH, n_qubits = qchem.molecular_hamiltonian(symbols, coords)\n\n# 2. Define ansatz\n@qml.qnode(dev)\ndef vqe_circuit(params):\n    qml.BasisState(qchem.hf_state(2, n_qubits), wires=range(n_qubits))\n    qml.UCCSD(params, wires=range(n_qubits))\n    return qml.expval(H)\n\n# 3. Optimize\nopt = qml.AdamOptimizer(stepsize=0.1)\nparams = np.zeros(10, requires_grad=True)\n\nfor i in range(100):\n    params, energy = opt.step_and_cost(vqe_circuit, params)\n    print(f\"Step {i}: Energy = {energy:.6f} Ha\")\n```\n\n### Switch Between Devices\n\n```python\n# Same circuit, different backends\ncircuit_def = lambda dev: qml.qnode(dev)(circuit_function)\n\n# Test on simulator\ndev_sim = qml.device('default.qubit', wires=4)\nresult_sim = circuit_def(dev_sim)(params)\n\n# Run on quantum hardware\ndev_hw = qml.device('qiskit.ibmq', wires=4, backend='ibmq_manila')\nresult_hw = circuit_def(dev_hw)(params)\n```\n\n## Detailed Documentation\n\nFor comprehensive coverage of specific topics, consult the reference files:\n\n- **Getting started**: `references\u002Fgetting_started.md` - Installation, basic concepts, first steps\n- **Quantum circuits**: `references\u002Fquantum_circuits.md` - Gates, measurements, circuit patterns\n- **Quantum ML**: `references\u002Fquantum_ml.md` - Hybrid models, framework integration, QNNs\n- **Quantum chemistry**: `references\u002Fquantum_chemistry.md` - VQE, molecular Hamiltonians, chemistry workflows\n- **Devices**: `references\u002Fdevices_backends.md` - Simulators, hardware plugins, device configuration\n- **Optimization**: `references\u002Foptimization.md` - Optimizers, gradients, variational algorithms\n- **Advanced**: `references\u002Fadvanced_features.md` - Templates, transforms, JIT compilation, noise\n\n## Best Practices\n\n1. **Start with simulators** - Test on `default.qubit` before deploying to hardware\n2. **Use parameter-shift for hardware** - Backpropagation only works on simulators\n3. **Choose appropriate encodings** - Match data encoding to problem structure\n4. **Initialize carefully** - Use small random values to avoid barren plateaus\n5. **Monitor gradients** - Check for vanishing gradients in deep circuits\n6. **Cache devices** - Reuse device objects to reduce initialization overhead\n7. **Profile circuits** - Use `qml.specs()` to analyze circuit complexity\n8. **Test locally** - Validate on simulators before submitting to hardware\n9. **Use templates** - Leverage built-in templates for common circuit patterns\n10. **Compile when possible** - Use Catalyst JIT for performance-critical code\n\n## Resources\n\n- Official documentation: https:\u002F\u002Fdocs.pennylane.ai\n- Codebook (tutorials): https:\u002F\u002Fpennylane.ai\u002Fcodebook\n- QML demonstrations: https:\u002F\u002Fpennylane.ai\u002Fqml\u002Fdemonstrations\n- Community forum: https:\u002F\u002Fdiscuss.pennylane.ai\n- GitHub: https:\u002F\u002Fgithub.com\u002FPennyLaneAI\u002Fpennylane\n",{"name":5,"description":7},"main","2b749c82b5d11fac39d9a6e021ce3f5b8a541bd1","cli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fpennylane",3911,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates","https:\u002F\u002Faitmpl.com","https:\u002F\u002Fgithub.com\u002Fdavila7","https:\u002F\u002Fdanielavila.me","Building AI dev tools with LLMs","2026-11-11 21:38:31",{"source":8,"skillName":5,"commands":63,"prompt":73,"zipAvailable":74},[64,67,70],{"tool":65,"command":66},"npx","npx skills add davila7\u002Fclaude-code-templates --skill pennylane",{"tool":68,"command":69},"bunx","bunx skills add davila7\u002Fclaude-code-templates --skill pennylane",{"tool":71,"command":72},"pnpm","pnpm dlx skills add davila7\u002Fclaude-code-templates --skill pennylane","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fpennylane\u002FSKILL.md",true,[76,82,86,90,94,98,102],{"path":77,"resourceType":78,"resourceTypeDesc":79,"sizeBytes":80,"sourceHtmlUrl":81},"references\u002Fadvanced_features.md","REFERENCE","参考文档",14984,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fpennylane\u002Freferences\u002Fadvanced_features.md",{"path":83,"resourceType":78,"resourceTypeDesc":79,"sizeBytes":84,"sourceHtmlUrl":85},"references\u002Fdevices_backends.md",13079,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fpennylane\u002Freferences\u002Fdevices_backends.md",{"path":87,"resourceType":78,"resourceTypeDesc":79,"sizeBytes":88,"sourceHtmlUrl":89},"references\u002Fgetting_started.md",5097,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fpennylane\u002Freferences\u002Fgetting_started.md",{"path":91,"resourceType":78,"resourceTypeDesc":79,"sizeBytes":92,"sourceHtmlUrl":93},"references\u002Foptimization.md",16696,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fpennylane\u002Freferences\u002Foptimization.md",{"path":95,"resourceType":78,"resourceTypeDesc":79,"sizeBytes":96,"sourceHtmlUrl":97},"references\u002Fquantum_chemistry.md",14438,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fpennylane\u002Freferences\u002Fquantum_chemistry.md",{"path":99,"resourceType":78,"resourceTypeDesc":79,"sizeBytes":100,"sourceHtmlUrl":101},"references\u002Fquantum_circuits.md",9395,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fpennylane\u002Freferences\u002Fquantum_circuits.md",{"path":103,"resourceType":78,"resourceTypeDesc":79,"sizeBytes":104,"sourceHtmlUrl":105},"references\u002Fquantum_ml.md",14532,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fpennylane\u002Freferences\u002Fquantum_ml.md",[107,138,165,198,233,257],{"skillNo":108,"name":109,"nameZh":6,"description":110,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":111,"categories":112,"keywords":118,"publishedAt":47},"01M70NHBTQ8BV0A2SCAZXEPHCX","wealthville-pool-scoring","Score DeFi liquidity pools before providing liquidity. 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Trigger even if the user doesn't say \"technical debt\" explicitly — any request to systematically assess code quality and identify improvement priorities belongs here.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdevelopment\u002Ftechnical-debt-auditor\u002FSKILL.md",[144],{"categoryNo":114,"categoryName":115,"taxonomyType":116,"taxonomyTypeDesc":117},[146,147,148,149,150,151,152,153,154,155,156,21,157,158,159,160,161,162,163,164],"technical debt","debt","technical","technical debt auditor","auditor","coverage","complexity","markers","project","cleanup","unused","code","todo","work","plan","focus","signals","audit","dependency",{"skillNo":166,"name":167,"nameZh":6,"description":168,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":169,"categories":170,"keywords":180,"publishedAt":47},"01M70KPYH9QNHCNME5GJQGE22K","readonly-sql-guard","Make an agent's database access provably read-only. Use when an agent runs caller-supplied SQL against a production\u002FERP\u002Fanalytics database (a \"query\" MCP tool, text-to-SQL, an admin console), or when reviewing DB-access code for write\u002FDDL\u002Fside-effect\u002Ftransaction-escape risks. Checks what a statement CALLS, not just its shape.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdatabase\u002Freadonly-sql-guard\u002FSKILL.md",[171,174,175],{"categoryNo":172,"categoryName":173,"taxonomyType":116,"taxonomyTypeDesc":117},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":114,"categoryName":115,"taxonomyType":116,"taxonomyTypeDesc":117},{"categoryNo":176,"categoryName":177,"taxonomyType":178,"taxonomyTypeDesc":179},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[181,182,183,184,185,186,187,124,188,189,190,191,192,193,194,127,195,196,21,197],"readonly","guard","sql guard","sql","readonly sql guard","erp","effect","read-only","db","just","tool","calls","only","MCP","access","database","query",{"skillNo":199,"name":200,"nameZh":6,"description":201,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":202,"licenseFileUrl":203,"sourceHtmlUrl":204,"categories":205,"keywords":209,"publishedAt":47},"01M6AFY6F7CC2BBZP1QD6VTNVC","web-artifacts-builder","Suite of tools for creating elaborate, multi-component claude.ai HTML artifacts using modern frontend web technologies (React, Tailwind CSS, shadcn\u002Fui). Use for complex artifacts requiring state management, routing, or shadcn\u002Fui components - not for simple single-file HTML\u002FJSX artifacts.","Apache-2.0","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FSKILL.md",[206],{"categoryNo":207,"categoryName":208,"taxonomyType":116,"taxonomyTypeDesc":117},"01JBSEEDCAT000000000000009","前端开发",[210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,124,232],"artifacts builder","web","web artifacts builder","artifacts","builder","state","routing","state management","component","css","frontend","ui","Tools","file","components","shadcn","react","Tailwind","suite","jsx","html","single-file","ui components",{"skillNo":234,"name":235,"nameZh":6,"description":236,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":202,"licenseFileUrl":237,"sourceHtmlUrl":238,"categories":239,"keywords":244,"publishedAt":47},"01M6AFWF33SFV9Z3SF4PEEE4BF","webapp-testing","Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FSKILL.md",[240,243],{"categoryNo":241,"categoryName":242,"taxonomyType":116,"taxonomyTypeDesc":117},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":207,"categoryName":208,"taxonomyType":116,"taxonomyTypeDesc":117},[245,246,247,248,249,211,250,220,221,251,252,253,254,124,255,129,256],"webapp testing","testing","webapp","browser","playwright","applications","debugging","toolkit","logs","screenshots","local","behavior",{"skillNo":258,"name":259,"nameZh":6,"description":260,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":202,"licenseFileUrl":261,"sourceHtmlUrl":262,"categories":263,"keywords":271,"publishedAt":47},"01M6AF67Q67JJJ5V51JR2P52KA","discernment-nudge","After you give a substantive answer or draft that the user may act on — advice or recommendations, drafted artifacts such as goals, plans, pitches, proposals, or emails, estimates or projections, analysis or interpretation of data, factual claims they may rely on, or a multi-step argument — invoke this skill BEFORE finalizing your reply and then, if it applies, append 2-3 short follow-up questions, each tied to something specific in what you just produced, that help the user check key facts, probe the reasoning or assumptions, and notice missing context. Do this at most once per conversation. Skip it when the user asked a trivial how-to or simple lookup, wants a purely educational explanation, asked you only to format, convert, or assemble a file from content they provided, is writing code they will run, is doing creative writing or casual chat, or already asked you to double-check, cite, or review — the skill file explains these boundaries and the exact output format.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fdiscernment-nudge\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fdiscernment-nudge\u002FSKILL.md",[264,267,268],{"categoryNo":265,"categoryName":266,"taxonomyType":116,"taxonomyTypeDesc":117},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":114,"categoryName":115,"taxonomyType":116,"taxonomyTypeDesc":117},{"categoryNo":269,"categoryName":270,"taxonomyType":178,"taxonomyTypeDesc":179},"01JBSEEDCAT000000000000036","内容创作者",[272,273,274,275,276,277,278,279,213,280,223,157,281,282,283,284,285,193,286,21,287,288,289,290,291,135,292],"discernment nudge","nudge","interpretation","help","run","reasoning","data","writing","lookup","content","review","conversation","short","up","invoke","you","analysis","claims","argument","assemble","recommendations"]