[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6AFM98A9D4NBC01C6F3J0S3":3,"skill-same:01M6AFM98A9D4NBC01C6F3J0S3":97},{"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":23,"publishedAt":46,"linkOnly":47,"markdownContent":48,"markdownBody":49,"frontMatter":50,"refName":51,"commitSha":52,"skillDirectory":53,"forksCount":54,"repositoryHtmlUrl":55,"repositoryHomepageUrl":56,"ownerHtmlUrl":57,"ownerBlogUrl":58,"ownerDescription":59,"updatedAt":60,"install":61,"resources":74},"01M6AFM98A9D4NBC01C6F3J0S3","qutip",null,"Quantum mechanics simulations and analysis using QuTiP (Quantum Toolbox in Python). Use when working with quantum systems including: (1) quantum states (kets, bras, density matrices), (2) quantum operators and gates, (3) time evolution and dynamics (Schrödinger, master equations, Monte Carlo), (4) open quantum systems with dissipation, (5) quantum measurements and entanglement, (6) visualization (Bloch sphere, Wigner functions), (7) steady states and correlation functions, or (8) advanced methods (Floquet theory, HEOM, stochastic solvers). Handles both closed and open quantum systems across various domains including quantum optics, quantum computing, and condensed matter physics.","davila7\u002Fclaude-code-templates","davila7","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F6216945?v=4","01M6ADEWWFPS191NFM0Y1EX1JH","Daniel Avila","01M6ADJHGM78PHQRD3XZTFDVKK",33481,"MIT","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fqutip\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000013","分析与可视化","SCENARIO","使用场景",[5,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45],"quantum mechanics","Simulations","when","advanced","states","dynamics","domains","entanglement","operators","schrödinger","mechanics","condensed matter","python","physics","across","time","master","open","analysis","correlation","visualization","quantum computing","2026-11-10 10:12:30",false,"---\nname: qutip\ndescription: \"Quantum mechanics simulations and analysis using QuTiP (Quantum Toolbox in Python). Use when working with quantum systems including: (1) quantum states (kets, bras, density matrices), (2) quantum operators and gates, (3) time evolution and dynamics (Schrödinger, master equations, Monte Carlo), (4) open quantum systems with dissipation, (5) quantum measurements and entanglement, (6) visualization (Bloch sphere, Wigner functions), (7) steady states and correlation functions, or (8) advanced methods (Floquet theory, HEOM, stochastic solvers). Handles both closed and open quantum systems across various domains including quantum optics, quantum computing, and condensed matter physics.\"\n---\n\n# QuTiP: Quantum Toolbox in Python\n\n## Overview\n\nQuTiP provides comprehensive tools for simulating and analyzing quantum mechanical systems. It handles both closed (unitary) and open (dissipative) quantum systems with multiple solvers optimized for different scenarios.\n\n## Installation\n\n```bash\nuv pip install qutip\n```\n\nOptional packages for additional functionality:\n\n```bash\n# Quantum information processing (circuits, gates)\nuv pip install qutip-qip\n\n# Quantum trajectory viewer\nuv pip install qutip-qtrl\n```\n\n## Quick Start\n\n```python\nfrom qutip import *\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# Create quantum state\npsi = basis(2, 0)  # |0⟩ state\n\n# Create operator\nH = sigmaz()  # Hamiltonian\n\n# Time evolution\ntlist = np.linspace(0, 10, 100)\nresult = sesolve(H, psi, tlist, e_ops=[sigmaz()])\n\n# Plot results\nplt.plot(tlist, result.expect[0])\nplt.xlabel('Time')\nplt.ylabel('⟨σz⟩')\nplt.show()\n```\n\n## Core Capabilities\n\n### 1. Quantum Objects and States\n\nCreate and manipulate quantum states and operators:\n\n```python\n# States\npsi = basis(N, n)  # Fock state |n⟩\npsi = coherent(N, alpha)  # Coherent state |α⟩\nrho = thermal_dm(N, n_avg)  # Thermal density matrix\n\n# Operators\na = destroy(N)  # Annihilation operator\nH = num(N)  # Number operator\nsx, sy, sz = sigmax(), sigmay(), sigmaz()  # Pauli matrices\n\n# Composite systems\npsi_AB = tensor(psi_A, psi_B)  # Tensor product\n```\n\n**See** `references\u002Fcore_concepts.md` for comprehensive coverage of quantum objects, states, operators, and tensor products.\n\n### 2. Time Evolution and Dynamics\n\nMultiple solvers for different scenarios:\n\n```python\n# Closed systems (unitary evolution)\nresult = sesolve(H, psi0, tlist, e_ops=[num(N)])\n\n# Open systems (dissipation)\nc_ops = [np.sqrt(0.1) * destroy(N)]  # Collapse operators\nresult = mesolve(H, psi0, tlist, c_ops, e_ops=[num(N)])\n\n# Quantum trajectories (Monte Carlo)\nresult = mcsolve(H, psi0, tlist, c_ops, ntraj=500, e_ops=[num(N)])\n```\n\n**Solver selection guide:**\n- `sesolve`: Pure states, unitary evolution\n- `mesolve`: Mixed states, dissipation, general open systems\n- `mcsolve`: Quantum jumps, photon counting, individual trajectories\n- `brmesolve`: Weak system-bath coupling\n- `fmmesolve`: Time-periodic Hamiltonians (Floquet)\n\n**See** `references\u002Ftime_evolution.md` for detailed solver documentation, time-dependent Hamiltonians, and advanced options.\n\n### 3. Analysis and Measurement\n\nCompute physical quantities:\n\n```python\n# Expectation values\nn_avg = expect(num(N), psi)\n\n# Entropy measures\nS = entropy_vn(rho)  # Von Neumann entropy\nC = concurrence(rho)  # Entanglement (two qubits)\n\n# Fidelity and distance\nF = fidelity(psi1, psi2)\nD = tracedist(rho1, rho2)\n\n# Correlation functions\ncorr = correlation_2op_1t(H, rho0, taulist, c_ops, A, B)\nw, S = spectrum_correlation_fft(taulist, corr)\n\n# Steady states\nrho_ss = steadystate(H, c_ops)\n```\n\n**See** `references\u002Fanalysis.md` for entropy, fidelity, measurements, correlation functions, and steady state calculations.\n\n### 4. Visualization\n\nVisualize quantum states and dynamics:\n\n```python\n# Bloch sphere\nb = Bloch()\nb.add_states(psi)\nb.show()\n\n# Wigner function (phase space)\nxvec = np.linspace(-5, 5, 200)\nW = wigner(psi, xvec, xvec)\nplt.contourf(xvec, xvec, W, 100, cmap='RdBu')\n\n# Fock distribution\nplot_fock_distribution(psi)\n\n# Matrix visualization\nhinton(rho)  # Hinton diagram\nmatrix_histogram(H.full())  # 3D bars\n```\n\n**See** `references\u002Fvisualization.md` for Bloch sphere animations, Wigner functions, Q-functions, and matrix visualizations.\n\n### 5. Advanced Methods\n\nSpecialized techniques for complex scenarios:\n\n```python\n# Floquet theory (periodic Hamiltonians)\nT = 2 * np.pi \u002F w_drive\nf_modes, f_energies = floquet_modes(H, T, args)\nresult = fmmesolve(H, psi0, tlist, c_ops, T=T, args=args)\n\n# HEOM (non-Markovian, strong coupling)\nfrom qutip.nonmarkov.heom import HEOMSolver, BosonicBath\nbath = BosonicBath(Q, ck_real, vk_real)\nhsolver = HEOMSolver(H_sys, [bath], max_depth=5)\nresult = hsolver.run(rho0, tlist)\n\n# Permutational invariance (identical particles)\npsi = dicke(N, j, m)  # Dicke states\nJz = jspin(N, 'z')  # Collective operators\n```\n\n**See** `references\u002Fadvanced.md` for Floquet theory, HEOM, permutational invariance, stochastic solvers, superoperators, and performance optimization.\n\n## Common Workflows\n\n### Simulating a Damped Harmonic Oscillator\n\n```python\n# System parameters\nN = 20  # Hilbert space dimension\nomega = 1.0  # Oscillator frequency\nkappa = 0.1  # Decay rate\n\n# Hamiltonian and collapse operators\nH = omega * num(N)\nc_ops = [np.sqrt(kappa) * destroy(N)]\n\n# Initial state\npsi0 = coherent(N, 3.0)\n\n# Time evolution\ntlist = np.linspace(0, 50, 200)\nresult = mesolve(H, psi0, tlist, c_ops, e_ops=[num(N)])\n\n# Visualize\nplt.plot(tlist, result.expect[0])\nplt.xlabel('Time')\nplt.ylabel('⟨n⟩')\nplt.title('Photon Number Decay')\nplt.show()\n```\n\n### Two-Qubit Entanglement Dynamics\n\n```python\n# Create Bell state\npsi0 = bell_state('00')\n\n# Local dephasing on each qubit\ngamma = 0.1\nc_ops = [\n    np.sqrt(gamma) * tensor(sigmaz(), qeye(2)),\n    np.sqrt(gamma) * tensor(qeye(2), sigmaz())\n]\n\n# Track entanglement\ndef compute_concurrence(t, psi):\n    rho = ket2dm(psi) if psi.isket else psi\n    return concurrence(rho)\n\ntlist = np.linspace(0, 10, 100)\nresult = mesolve(qeye([2, 2]), psi0, tlist, c_ops)\n\n# Compute concurrence for each state\nC_t = [concurrence(state.proj()) for state in result.states]\n\nplt.plot(tlist, C_t)\nplt.xlabel('Time')\nplt.ylabel('Concurrence')\nplt.title('Entanglement Decay')\nplt.show()\n```\n\n### Jaynes-Cummings Model\n\n```python\n# System parameters\nN = 10  # Cavity Fock space\nwc = 1.0  # Cavity frequency\nwa = 1.0  # Atom frequency\ng = 0.05  # Coupling strength\n\n# Operators\na = tensor(destroy(N), qeye(2))  # Cavity\nsm = tensor(qeye(N), sigmam())  # Atom\n\n# Hamiltonian (RWA)\nH = wc * a.dag() * a + wa * sm.dag() * sm + g * (a.dag() * sm + a * sm.dag())\n\n# Initial state: cavity in coherent state, atom in ground state\npsi0 = tensor(coherent(N, 2), basis(2, 0))\n\n# Dissipation\nkappa = 0.1  # Cavity decay\ngamma = 0.05  # Atomic decay\nc_ops = [np.sqrt(kappa) * a, np.sqrt(gamma) * sm]\n\n# Observables\nn_cav = a.dag() * a\nn_atom = sm.dag() * sm\n\n# Evolve\ntlist = np.linspace(0, 50, 200)\nresult = mesolve(H, psi0, tlist, c_ops, e_ops=[n_cav, n_atom])\n\n# Plot\nfig, axes = plt.subplots(2, 1, figsize=(8, 6), sharex=True)\naxes[0].plot(tlist, result.expect[0])\naxes[0].set_ylabel('⟨n_cavity⟩')\naxes[1].plot(tlist, result.expect[1])\naxes[1].set_ylabel('⟨n_atom⟩')\naxes[1].set_xlabel('Time')\nplt.tight_layout()\nplt.show()\n```\n\n## Tips for Efficient Simulations\n\n1. **Truncate Hilbert spaces**: Use smallest dimension that captures dynamics\n2. **Choose appropriate solver**: `sesolve` for pure states is faster than `mesolve`\n3. **Time-dependent terms**: String format (e.g., `'cos(w*t)'`) is fastest\n4. **Store only needed data**: Use `e_ops` instead of storing all states\n5. **Adjust tolerances**: Balance accuracy with computation time via `Options`\n6. **Parallel trajectories**: `mcsolve` automatically uses multiple CPUs\n7. **Check convergence**: Vary `ntraj`, Hilbert space size, and tolerances\n\n## Troubleshooting\n\n**Memory issues**: Reduce Hilbert space dimension, use `store_final_state` option, or consider Krylov methods\n\n**Slow simulations**: Use string-based time-dependence, increase tolerances slightly, or try `method='bdf'` for stiff problems\n\n**Numerical instabilities**: Decrease time steps (`nsteps` option), increase tolerances, or check Hamiltonian\u002Foperators are properly defined\n\n**Import errors**: Ensure QuTiP is installed correctly; quantum gates require `qutip-qip` package\n\n## References\n\nThis skill includes detailed reference documentation:\n\n- **`references\u002Fcore_concepts.md`**: Quantum objects, states, operators, tensor products, composite systems\n- **`references\u002Ftime_evolution.md`**: All solvers (sesolve, mesolve, mcsolve, brmesolve, etc.), time-dependent Hamiltonians, solver options\n- **`references\u002Fvisualization.md`**: Bloch sphere, Wigner functions, Q-functions, Fock distributions, matrix plots\n- **`references\u002Fanalysis.md`**: Expectation values, entropy, fidelity, entanglement measures, correlation functions, steady states\n- **`references\u002Fadvanced.md`**: Floquet theory, HEOM, permutational invariance, stochastic methods, superoperators, performance tips\n\n## External Resources\n\n- Documentation: https:\u002F\u002Fqutip.readthedocs.io\u002F\n- Tutorials: https:\u002F\u002Fqutip.org\u002Fqutip-tutorials\u002F\n- API Reference: https:\u002F\u002Fqutip.readthedocs.io\u002Fen\u002Fstable\u002Fapidoc\u002Fapidoc.html\n- GitHub: https:\u002F\u002Fgithub.com\u002Fqutip\u002Fqutip\n","# QuTiP: Quantum Toolbox in Python\n\n## Overview\n\nQuTiP provides comprehensive tools for simulating and analyzing quantum mechanical systems. It handles both closed (unitary) and open (dissipative) quantum systems with multiple solvers optimized for different scenarios.\n\n## Installation\n\n```bash\nuv pip install qutip\n```\n\nOptional packages for additional functionality:\n\n```bash\n# Quantum information processing (circuits, gates)\nuv pip install qutip-qip\n\n# Quantum trajectory viewer\nuv pip install qutip-qtrl\n```\n\n## Quick Start\n\n```python\nfrom qutip import *\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# Create quantum state\npsi = basis(2, 0)  # |0⟩ state\n\n# Create operator\nH = sigmaz()  # Hamiltonian\n\n# Time evolution\ntlist = np.linspace(0, 10, 100)\nresult = sesolve(H, psi, tlist, e_ops=[sigmaz()])\n\n# Plot results\nplt.plot(tlist, result.expect[0])\nplt.xlabel('Time')\nplt.ylabel('⟨σz⟩')\nplt.show()\n```\n\n## Core Capabilities\n\n### 1. Quantum Objects and States\n\nCreate and manipulate quantum states and operators:\n\n```python\n# States\npsi = basis(N, n)  # Fock state |n⟩\npsi = coherent(N, alpha)  # Coherent state |α⟩\nrho = thermal_dm(N, n_avg)  # Thermal density matrix\n\n# Operators\na = destroy(N)  # Annihilation operator\nH = num(N)  # Number operator\nsx, sy, sz = sigmax(), sigmay(), sigmaz()  # Pauli matrices\n\n# Composite systems\npsi_AB = tensor(psi_A, psi_B)  # Tensor product\n```\n\n**See** `references\u002Fcore_concepts.md` for comprehensive coverage of quantum objects, states, operators, and tensor products.\n\n### 2. Time Evolution and Dynamics\n\nMultiple solvers for different scenarios:\n\n```python\n# Closed systems (unitary evolution)\nresult = sesolve(H, psi0, tlist, e_ops=[num(N)])\n\n# Open systems (dissipation)\nc_ops = [np.sqrt(0.1) * destroy(N)]  # Collapse operators\nresult = mesolve(H, psi0, tlist, c_ops, e_ops=[num(N)])\n\n# Quantum trajectories (Monte Carlo)\nresult = mcsolve(H, psi0, tlist, c_ops, ntraj=500, e_ops=[num(N)])\n```\n\n**Solver selection guide:**\n- `sesolve`: Pure states, unitary evolution\n- `mesolve`: Mixed states, dissipation, general open systems\n- `mcsolve`: Quantum jumps, photon counting, individual trajectories\n- `brmesolve`: Weak system-bath coupling\n- `fmmesolve`: Time-periodic Hamiltonians (Floquet)\n\n**See** `references\u002Ftime_evolution.md` for detailed solver documentation, time-dependent Hamiltonians, and advanced options.\n\n### 3. Analysis and Measurement\n\nCompute physical quantities:\n\n```python\n# Expectation values\nn_avg = expect(num(N), psi)\n\n# Entropy measures\nS = entropy_vn(rho)  # Von Neumann entropy\nC = concurrence(rho)  # Entanglement (two qubits)\n\n# Fidelity and distance\nF = fidelity(psi1, psi2)\nD = tracedist(rho1, rho2)\n\n# Correlation functions\ncorr = correlation_2op_1t(H, rho0, taulist, c_ops, A, B)\nw, S = spectrum_correlation_fft(taulist, corr)\n\n# Steady states\nrho_ss = steadystate(H, c_ops)\n```\n\n**See** `references\u002Fanalysis.md` for entropy, fidelity, measurements, correlation functions, and steady state calculations.\n\n### 4. Visualization\n\nVisualize quantum states and dynamics:\n\n```python\n# Bloch sphere\nb = Bloch()\nb.add_states(psi)\nb.show()\n\n# Wigner function (phase space)\nxvec = np.linspace(-5, 5, 200)\nW = wigner(psi, xvec, xvec)\nplt.contourf(xvec, xvec, W, 100, cmap='RdBu')\n\n# Fock distribution\nplot_fock_distribution(psi)\n\n# Matrix visualization\nhinton(rho)  # Hinton diagram\nmatrix_histogram(H.full())  # 3D bars\n```\n\n**See** `references\u002Fvisualization.md` for Bloch sphere animations, Wigner functions, Q-functions, and matrix visualizations.\n\n### 5. Advanced Methods\n\nSpecialized techniques for complex scenarios:\n\n```python\n# Floquet theory (periodic Hamiltonians)\nT = 2 * np.pi \u002F w_drive\nf_modes, f_energies = floquet_modes(H, T, args)\nresult = fmmesolve(H, psi0, tlist, c_ops, T=T, args=args)\n\n# HEOM (non-Markovian, strong coupling)\nfrom qutip.nonmarkov.heom import HEOMSolver, BosonicBath\nbath = BosonicBath(Q, ck_real, vk_real)\nhsolver = HEOMSolver(H_sys, [bath], max_depth=5)\nresult = hsolver.run(rho0, tlist)\n\n# Permutational invariance (identical particles)\npsi = dicke(N, j, m)  # Dicke states\nJz = jspin(N, 'z')  # Collective operators\n```\n\n**See** `references\u002Fadvanced.md` for Floquet theory, HEOM, permutational invariance, stochastic solvers, superoperators, and performance optimization.\n\n## Common Workflows\n\n### Simulating a Damped Harmonic Oscillator\n\n```python\n# System parameters\nN = 20  # Hilbert space dimension\nomega = 1.0  # Oscillator frequency\nkappa = 0.1  # Decay rate\n\n# Hamiltonian and collapse operators\nH = omega * num(N)\nc_ops = [np.sqrt(kappa) * destroy(N)]\n\n# Initial state\npsi0 = coherent(N, 3.0)\n\n# Time evolution\ntlist = np.linspace(0, 50, 200)\nresult = mesolve(H, psi0, tlist, c_ops, e_ops=[num(N)])\n\n# Visualize\nplt.plot(tlist, result.expect[0])\nplt.xlabel('Time')\nplt.ylabel('⟨n⟩')\nplt.title('Photon Number Decay')\nplt.show()\n```\n\n### Two-Qubit Entanglement Dynamics\n\n```python\n# Create Bell state\npsi0 = bell_state('00')\n\n# Local dephasing on each qubit\ngamma = 0.1\nc_ops = [\n    np.sqrt(gamma) * tensor(sigmaz(), qeye(2)),\n    np.sqrt(gamma) * tensor(qeye(2), sigmaz())\n]\n\n# Track entanglement\ndef compute_concurrence(t, psi):\n    rho = ket2dm(psi) if psi.isket else psi\n    return concurrence(rho)\n\ntlist = np.linspace(0, 10, 100)\nresult = mesolve(qeye([2, 2]), psi0, tlist, c_ops)\n\n# Compute concurrence for each state\nC_t = [concurrence(state.proj()) for state in result.states]\n\nplt.plot(tlist, C_t)\nplt.xlabel('Time')\nplt.ylabel('Concurrence')\nplt.title('Entanglement Decay')\nplt.show()\n```\n\n### Jaynes-Cummings Model\n\n```python\n# System parameters\nN = 10  # Cavity Fock space\nwc = 1.0  # Cavity frequency\nwa = 1.0  # Atom frequency\ng = 0.05  # Coupling strength\n\n# Operators\na = tensor(destroy(N), qeye(2))  # Cavity\nsm = tensor(qeye(N), sigmam())  # Atom\n\n# Hamiltonian (RWA)\nH = wc * a.dag() * a + wa * sm.dag() * sm + g * (a.dag() * sm + a * sm.dag())\n\n# Initial state: cavity in coherent state, atom in ground state\npsi0 = tensor(coherent(N, 2), basis(2, 0))\n\n# Dissipation\nkappa = 0.1  # Cavity decay\ngamma = 0.05  # Atomic decay\nc_ops = [np.sqrt(kappa) * a, np.sqrt(gamma) * sm]\n\n# Observables\nn_cav = a.dag() * a\nn_atom = sm.dag() * sm\n\n# Evolve\ntlist = np.linspace(0, 50, 200)\nresult = mesolve(H, psi0, tlist, c_ops, e_ops=[n_cav, n_atom])\n\n# Plot\nfig, axes = plt.subplots(2, 1, figsize=(8, 6), sharex=True)\naxes[0].plot(tlist, result.expect[0])\naxes[0].set_ylabel('⟨n_cavity⟩')\naxes[1].plot(tlist, result.expect[1])\naxes[1].set_ylabel('⟨n_atom⟩')\naxes[1].set_xlabel('Time')\nplt.tight_layout()\nplt.show()\n```\n\n## Tips for Efficient Simulations\n\n1. **Truncate Hilbert spaces**: Use smallest dimension that captures dynamics\n2. **Choose appropriate solver**: `sesolve` for pure states is faster than `mesolve`\n3. **Time-dependent terms**: String format (e.g., `'cos(w*t)'`) is fastest\n4. **Store only needed data**: Use `e_ops` instead of storing all states\n5. **Adjust tolerances**: Balance accuracy with computation time via `Options`\n6. **Parallel trajectories**: `mcsolve` automatically uses multiple CPUs\n7. **Check convergence**: Vary `ntraj`, Hilbert space size, and tolerances\n\n## Troubleshooting\n\n**Memory issues**: Reduce Hilbert space dimension, use `store_final_state` option, or consider Krylov methods\n\n**Slow simulations**: Use string-based time-dependence, increase tolerances slightly, or try `method='bdf'` for stiff problems\n\n**Numerical instabilities**: Decrease time steps (`nsteps` option), increase tolerances, or check Hamiltonian\u002Foperators are properly defined\n\n**Import errors**: Ensure QuTiP is installed correctly; quantum gates require `qutip-qip` package\n\n## References\n\nThis skill includes detailed reference documentation:\n\n- **`references\u002Fcore_concepts.md`**: Quantum objects, states, operators, tensor products, composite systems\n- **`references\u002Ftime_evolution.md`**: All solvers (sesolve, mesolve, mcsolve, brmesolve, etc.), time-dependent Hamiltonians, solver options\n- **`references\u002Fvisualization.md`**: Bloch sphere, Wigner functions, Q-functions, Fock distributions, matrix plots\n- **`references\u002Fanalysis.md`**: Expectation values, entropy, fidelity, entanglement measures, correlation functions, steady states\n- **`references\u002Fadvanced.md`**: Floquet theory, HEOM, permutational invariance, stochastic methods, superoperators, performance tips\n\n## External Resources\n\n- Documentation: https:\u002F\u002Fqutip.readthedocs.io\u002F\n- Tutorials: https:\u002F\u002Fqutip.org\u002Fqutip-tutorials\u002F\n- API Reference: https:\u002F\u002Fqutip.readthedocs.io\u002Fen\u002Fstable\u002Fapidoc\u002Fapidoc.html\n- GitHub: https:\u002F\u002Fgithub.com\u002Fqutip\u002Fqutip\n",{"name":5,"description":7},"main","2b749c82b5d11fac39d9a6e021ce3f5b8a541bd1","cli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fqutip",3931,"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","2026-11-14 06:14:46",{"source":8,"skillName":5,"commands":62,"prompt":72,"zipAvailable":73},[63,66,69],{"tool":64,"command":65},"npx","npx skills add davila7\u002Fclaude-code-templates --skill qutip",{"tool":67,"command":68},"bunx","bunx skills add davila7\u002Fclaude-code-templates --skill qutip",{"tool":70,"command":71},"pnpm","pnpm dlx skills add davila7\u002Fclaude-code-templates --skill qutip","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fqutip\u002FSKILL.md",true,[75,81,85,89,93],{"path":76,"resourceType":77,"resourceTypeDesc":78,"sizeBytes":79,"sourceHtmlUrl":80},"references\u002Fadvanced.md","REFERENCE","参考文档",11628,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fqutip\u002Freferences\u002Fadvanced.md",{"path":82,"resourceType":77,"resourceTypeDesc":78,"sizeBytes":83,"sourceHtmlUrl":84},"references\u002Fanalysis.md",9387,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fqutip\u002Freferences\u002Fanalysis.md",{"path":86,"resourceType":77,"resourceTypeDesc":78,"sizeBytes":87,"sourceHtmlUrl":88},"references\u002Fcore_concepts.md",5595,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fqutip\u002Freferences\u002Fcore_concepts.md",{"path":90,"resourceType":77,"resourceTypeDesc":78,"sizeBytes":91,"sourceHtmlUrl":92},"references\u002Ftime_evolution.md",7866,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fqutip\u002Freferences\u002Ftime_evolution.md",{"path":94,"resourceType":77,"resourceTypeDesc":78,"sizeBytes":95,"sourceHtmlUrl":96},"references\u002Fvisualization.md",8288,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fqutip\u002Freferences\u002Fvisualization.md",[98,127,154,187,222,246],{"skillNo":99,"name":100,"nameZh":6,"description":101,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":102,"categories":103,"keywords":107,"publishedAt":46},"01M70NHBTQ8BV0A2SCAZXEPHCX","wealthville-pool-scoring","Score DeFi liquidity pools before providing liquidity. Returns a 0-100 pool score and an Enter\u002FHold\u002FExit\u002FReduce\u002FAvoid verdict with calibrated confidence for ~68,800 Solana pools and 575 EVM pools, plus a public miss-inclusive track record. Use when asked whether a pool is worth LPing into, which pools look best right now, or how accurate the scores have actually been.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fweb-data\u002Fwealthville-pool-scoring\u002FSKILL.md",[104],{"categoryNo":105,"categoryName":106,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[108,109,110,111,112,113,114,115,116,26,117,118,119,120,121,122,123,124,125,126],"scoring","pool","wealthville pool scoring","liquidity","which","for","exit","now","use","reduce","with","record","track","verdict","evm","solana","how","public","look",{"skillNo":128,"name":129,"nameZh":6,"description":130,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":131,"categories":132,"keywords":134,"publishedAt":46},"01M70MB6VR8ERJ4EM8D5KHBMEV","technical-debt-auditor","Scans a codebase and produces a quantified technical debt report with a prioritized remediation plan. Analyzes dead code, TODO\u002FFIXME\u002FHACK markers, dependency staleness, complexity hotspots, code duplication signals, test coverage gaps, unused exports, and outdated patterns. Use this skill when the user asks to audit their codebase for tech debt, wants a health check of their code quality, says things like \"where should I focus refactoring\", \"find code smells\", \"audit my codebase\", \"what technical debt do we have\", \"prioritize cleanup work\", or asks about code quality metrics. Also trigger when the user mentions \"code rot\", \"legacy cleanup\", \"refactoring priorities\", or wants to understand the overall health of a project before a major effort. 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",[133],{"categoryNo":105,"categoryName":106,"taxonomyType":21,"taxonomyTypeDesc":22},[135,136,137,138,139,140,141,142,143,144,145,26,146,147,148,149,150,151,152,153],"technical debt","debt","technical","technical debt auditor","auditor","coverage","complexity","markers","project","cleanup","unused","code","todo","work","plan","focus","signals","audit","dependency",{"skillNo":155,"name":156,"nameZh":6,"description":157,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":158,"categories":159,"keywords":169,"publishedAt":46},"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",[160,163,164],{"categoryNo":161,"categoryName":162,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":105,"categoryName":106,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":165,"categoryName":166,"taxonomyType":167,"taxonomyTypeDesc":168},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[170,171,172,173,174,175,176,113,177,178,179,180,181,182,183,116,184,185,26,186],"readonly","guard","sql guard","sql","readonly sql guard","erp","effect","read-only","db","just","tool","calls","only","MCP","access","database","query",{"skillNo":188,"name":189,"nameZh":6,"description":190,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":191,"licenseFileUrl":192,"sourceHtmlUrl":193,"categories":194,"keywords":198,"publishedAt":46},"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",[195],{"categoryNo":196,"categoryName":197,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000009","前端开发",[199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,113,221],"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":223,"name":224,"nameZh":6,"description":225,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":191,"licenseFileUrl":226,"sourceHtmlUrl":227,"categories":228,"keywords":233,"publishedAt":46},"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",[229,232],{"categoryNo":230,"categoryName":231,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":196,"categoryName":197,"taxonomyType":21,"taxonomyTypeDesc":22},[234,235,236,237,238,200,239,209,210,240,241,242,243,113,244,118,245],"webapp testing","testing","webapp","browser","playwright","applications","debugging","toolkit","logs","screenshots","local","behavior",{"skillNo":247,"name":248,"nameZh":6,"description":249,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":191,"licenseFileUrl":250,"sourceHtmlUrl":251,"categories":252,"keywords":260,"publishedAt":46},"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",[253,256,257],{"categoryNo":254,"categoryName":255,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":105,"categoryName":106,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":258,"categoryName":259,"taxonomyType":167,"taxonomyTypeDesc":168},"01JBSEEDCAT000000000000036","内容创作者",[261,262,263,264,265,266,267,268,202,269,212,146,270,271,272,273,274,182,275,26,276,42,277,278,279,124,280],"discernment nudge","nudge","interpretation","help","run","reasoning","data","writing","lookup","content","review","conversation","short","up","invoke","you","claims","argument","assemble","recommendations"]