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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.","transaurus\u002Fstaging-davila7-claude-code-templates","transaurus","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F210882017?v=4","01M6JH1R6ZCRXGJCTDP5ZKZBE6","Page Turner Agent","01M6JH1RBBR2P6Y0XWDGM4B0G7",0,"MIT","https:\u002F\u002Fgithub.com\u002Ftransaurus\u002Fstaging-davila7-claude-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],"gradient","google","for","quantum algorithms","differentiation","amazon braket","platform","molecular","across","algorithms","ibm","library","tasks","training","pytorch","neural networks","workflows","machine learning","tensorflow","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** - 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Key insight: Zapier optimizes for simplicity and integrations (7000+ apps), Make optimizes for power","https:\u002F\u002Fgithub.com\u002Ftransaurus\u002Fstaging-davila7-claude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fworkflow-automation\u002Fzapier-make-patterns\u002FSKILL.md",[102],{"categoryNo":103,"categoryName":104,"taxonomyType":105,"taxonomyTypeDesc":106},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[108,109,110,111,112,113,114,115,116,117,118,119,120,25,121,122,123,124,21,125],"zapier make patterns","patterns","make","zapier","writing","workflow","automation","code","integrations","business","use","power","complexity","apps","how to","No-Code","which","spawner",{"skillNo":127,"name":128,"nameZh":6,"description":129,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":130,"categories":131,"keywords":132,"publishedAt":38},"01M6JMR8YK6SWPCTG1MCQTMN9S","yeet","Use only when the user explicitly asks to stage, commit, push, and open a GitHub pull request in one flow using the GitHub CLI (`gh`).","https:\u002F\u002Fgithub.com\u002Ftransaurus\u002Fstaging-davila7-claude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fworkflow-automation\u002Fyeet\u002FSKILL.md",[],[128,133,134,135,136,137,138,139,140,141,142,143,118,144,145,146],"cli","github","commit","pull request","openai","pull","push","flow","open","gh","user","only","stage","github cli",{"skillNo":148,"name":149,"nameZh":6,"description":150,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":151,"categories":152,"keywords":154,"publishedAt":38},"01M6JMQNFE64ZASQK90HARRXW8","workflow-automation","Workflow automation is the infrastructure that makes AI agents reliable. Without durable execution, a network hiccup during a 10-step payment flow means lost money and angry customers. With it, workflows resume exactly where they left off.  This skill covers the platforms (n8n, Temporal, Inngest) and patterns (sequential, parallel, orchestrator-worker) that turn brittle scripts into production-grade automation.  Key insight: The platforms make different tradeoffs. n8n optimizes for accessibility","https:\u002F\u002Fgithub.com\u002Ftransaurus\u002Fstaging-davila7-claude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fworkflow-automation\u002Fworkflow-automation\u002FSKILL.md",[153],{"categoryNo":103,"categoryName":104,"taxonomyType":105,"taxonomyTypeDesc":106},[113,114,155,156,157,35,158,159,160,161,162,163,164,165,166,167,168,169,21,125],"Workflow Automation","ai agents","accessibility","apache","execution","inngest","parallel","orchestrator","resume","n8n","payment","worker","scripts","temporal","with",{"skillNo":171,"name":172,"nameZh":6,"description":173,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":174,"categories":175,"keywords":176,"publishedAt":38},"01M6JMQDYJ0XETERPHGDR3EVGN","trigger-dev","Trigger.dev expert for background jobs, AI workflows, and reliable async execution with excellent developer experience and TypeScript-first design. Use when: trigger.dev, trigger dev, background task, ai background job, long running task.","https:\u002F\u002Fgithub.com\u002Ftransaurus\u002Fstaging-davila7-claude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fworkflow-automation\u002Ftrigger-dev\u002FSKILL.md",[],[177,178,179,180,181,35,182,158,159,183,118,184,185,186,187,188,189,169,21,125],"dev","trigger dev","trigger","design","typescript","async execution","first","async","developer experience","expert","task","background jobs","long",{"skillNo":191,"name":192,"nameZh":6,"description":193,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":194,"categories":195,"keywords":199,"publishedAt":38},"01M6JMQ5J6KK3GSX9FJCWWK6G6","planning-with-files","Transforms workflow to use Manus-style persistent markdown files for planning, progress tracking, and knowledge storage. Use when starting complex tasks, multi-step projects, research tasks, or when the user mentions planning, organizing work, tracking progress, or wants structured output.","https:\u002F\u002Fgithub.com\u002Ftransaurus\u002Fstaging-davila7-claude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fworkflow-automation\u002Fplanning-with-files\u002FSKILL.md",[196],{"categoryNo":197,"categoryName":198,"taxonomyType":105,"taxonomyTypeDesc":106},"01JBSEEDCAT000000000000021","资料检索与研究",[200,201,202,169,203,113,204,31,205,206,207,208,118,209,210,211,212,213,214,21],"planning with files","planning","files","with files","storage","markdown","knowledge","projects","work","manus","structured output","progress","transforms","multi","output",{"skillNo":216,"name":217,"nameZh":6,"description":218,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":219,"categories":220,"keywords":225,"publishedAt":38},"01M6JMPWAT60RXXNEC4V9TRBRS","n8n-workflow-patterns","Proven workflow architectural patterns from real n8n workflows. Use when building new workflows, designing workflow structure, choosing workflow patterns, planning workflow architecture, or asking about webhook processing, HTTP API integration, database operations, AI agent workflows, or scheduled tasks.","https:\u002F\u002Fgithub.com\u002Ftransaurus\u002Fstaging-davila7-claude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fworkflow-automation\u002Fn8n\u002Fn8n-workflow-patterns\u002FSKILL.md",[221,224],{"categoryNo":222,"categoryName":223,"taxonomyType":105,"taxonomyTypeDesc":106},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":103,"categoryName":104,"taxonomyType":105,"taxonomyTypeDesc":106},[113,226,164,227,109,228,229,35,230,31,201,231,232,233,234,235,236,237,238,239],"n8n workflow patterns","workflow patterns","n8n workflow","ai agent","architecture","database","api","planning workflow","structure","database operations","webhook","processing","http api","scheduled"]