[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6J9ZMZJ1992MG2MPXEHBPZP":3,"skill-same:01M6J9ZMZJ1992MG2MPXEHBPZP":76},{"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":42,"linkOnly":43,"markdownContent":44,"markdownBody":45,"frontMatter":46,"refName":34,"commitSha":56,"skillDirectory":57,"forksCount":50,"repositoryHtmlUrl":58,"repositoryHomepageUrl":6,"ownerHtmlUrl":59,"ownerBlogUrl":60,"ownerDescription":61,"updatedAt":62,"install":63,"resources":75},"01M6J9ZMZJ1992MG2MPXEHBPZP","science-qutip",null,"Quantum physics simulation library for open quantum systems. Use when studying master equations, Lindblad dynamics, decoherence, quantum optics, or cavity QED. Best for physics research, open system dynamics, and educational simulations. NOT for circuit-based quantum computing—use qiskit, cirq, or pennylane for quantum algorithms and hardware execution.","Flissel\u002Fvibemind-os","Flissel","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F104565236?v=4","01M6J8Q88NWAJGKPC5TEG9YCNV","Felix Baumann","01M6J8Q8D7XAFY44P70XTJFZBW",1,"MIT","https:\u002F\u002Fgithub.com\u002FFlissel\u002Fvibemind-os\u002Fblob\u002Fmaster\u002Fskills\u002Fscience\u002Fqutip\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41],"qutip","science qutip","science","imported","Simulations","2026","when","source","for","circuit","quantum algorithms","dynamics","physics","algorithms","library","master","repo","system","open","simulation","qiskit","pennylane","cirq","2026-11-10 10:12:30",false,"---\nname: science-qutip\ndescription: Quantum physics simulation library for open quantum systems. Use when\n  studying master equations, Lindblad dynamics, decoherence, quantum optics, or cavity\n  QED. Best for physics research, open system dynamics, and educational simulations.\n  NOT for circuit-based quantum computing—use qiskit, cirq, or pennylane for quantum\n  algorithms and hardware execution.\napp: science\nrequires_approval: true\nagents:\n- '*'\ninputs: []\nexpected_state:\n  description: Imported skill — behavior matches source repo\n  verification_tool: none\nconfidence: 1.0\nattempts: 0\nsuccesses: 0\nlast_adjusted: '2026-05-11T21:21:44+00:00'\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\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\n",{"app":21,"name":5,"agents":47,"inputs":49,"attempts":50,"successes":50,"confidence":14,"description":7,"last_adjusted":51,"expected_state":52,"requires_approval":55},[48],"*",[],0,"2026-05-11T21:21:44+00:00",{"description":53,"verification_tool":54},"Imported skill — behavior matches source repo","none",true,"5f13b5d2b532c0ab6f847e3aa24d4bfe66f7d461","skills\u002Fscience\u002Fqutip","https:\u002F\u002Fgithub.com\u002FFlissel\u002Fvibemind-os","https:\u002F\u002Fgithub.com\u002FFlissel","https:\u002F\u002Fvibemind.space","Co-founder & CTO, VibeMind - a voice-first agentic OS for non-technical founders. Agents that do the work, not describe it.","2026-11-15 02:57:54",{"source":8,"skillName":5,"commands":64,"prompt":74,"zipAvailable":55},[65,68,71],{"tool":66,"command":67},"npx","npx skills add Flissel\u002Fvibemind-os --skill science-qutip",{"tool":69,"command":70},"bunx","bunx skills add Flissel\u002Fvibemind-os --skill science-qutip",{"tool":72,"command":73},"pnpm","pnpm dlx skills add Flissel\u002Fvibemind-os --skill science-qutip","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FFlissel\u002Fvibemind-os\u002Fblob\u002Fmaster\u002Fskills\u002Fscience\u002Fqutip\u002FSKILL.md",[],[77,85,92,120,128,142],{"skillNo":78,"name":79,"nameZh":6,"description":80,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":81,"categories":82,"keywords":83,"publishedAt":42},"01M6JAA67M92TRE7GWDJDBTA9T","review","Review a change.","https:\u002F\u002Fgithub.com\u002FFlissel\u002Fvibemind-os\u002Fblob\u002Fmaster\u002Fspaces\u002Frowboat\u002Frowboat\u002Fpackages\u002Fopenai-plugin-runtime\u002Ftest\u002Ffixtures\u002Fcomplete-plugin\u002Fskills\u002Freview\u002FSKILL.md",[],[79,84],"change",{"skillNo":86,"name":87,"nameZh":6,"description":88,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":89,"categories":90,"keywords":91,"publishedAt":42},"01M6JAA3DZR55G8ZY493BE9NBB","triage","Triage a change.","https:\u002F\u002Fgithub.com\u002FFlissel\u002Fvibemind-os\u002Fblob\u002Fmaster\u002Fspaces\u002Frowboat\u002Frowboat\u002Fpackages\u002Fopenai-plugin-runtime\u002Ftest\u002Ffixtures\u002Fcomplete-plugin\u002Fskills\u002Fgroup\u002Ftriage\u002FSKILL.md",[],[87,84],{"skillNo":93,"name":94,"nameZh":6,"description":95,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":96,"categories":97,"keywords":98,"publishedAt":42},"01M6JA9XPVHENK0FQN7HBK54NV","zentrale-werkzeuge","Werkzeuge der Zentrale – nutzen bei GitHub-Fragen (Projekte updaten, Repos, Issues, PRs, Commits, CI\u002FActions-Status, README), bei Fragen zum eigenen Gitea (Sicherung\u002FSpiegel, Repos, Issues, Commits), wenn neueste Docs\u002FDoku zu einer Bibliothek oder einem Dienst geholt werden sollen, wenn ein Dashboard aus meinen Daten gebaut werden soll und wenn ich sage 'auf meinem PC suchen' oder 'such auf dem Rechner nach …'.","https:\u002F\u002Fgithub.com\u002FFlissel\u002Fvibemind-os\u002Fblob\u002Fmaster\u002Fspaces\u002Frowboat\u002Fdeploy\u002Fheadless\u002Fskills\u002Fzentrale-werkzeuge\u002FSKILL.md",[],[99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119],"zentrale werkzeuge","grep","file","README","list","fetch","todo","gitea","add","URL","mkdir","prs","commits","ci","actions","issues","status","repos","dashboard","github","docs",{"skillNo":121,"name":122,"nameZh":6,"description":123,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":124,"categories":125,"keywords":126,"publishedAt":42},"01M6JA9SQPX187CVC148SQT1SP","wissen-suchen","Bedeutungssuche im Rowboat-Wissen – IMMER zuerst nutzen, wenn nach Kunden, Kontakten, Themen, Interessen, Notizen oder 'was wissen wir über …' gefragt wird, besonders wenn die genauen Wörter unklar sind.","https:\u002F\u002Fgithub.com\u002FFlissel\u002Fvibemind-os\u002Fblob\u002Fmaster\u002Fspaces\u002Frowboat\u002Fdeploy\u002Fheadless\u002Fskills\u002Fwissen-suchen\u002FSKILL.md",[],[127],"wissen suchen",{"skillNo":129,"name":130,"nameZh":6,"description":131,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":132,"categories":133,"keywords":134,"publishedAt":42},"01M6JA9P6GNFPE21101SDZS3BT","reel-aus-material","Reel aus vorhandenen Laura-Videos: passende Stellen im Transkript finden; spricht dort eine Person, bleibt ihr Originalton (mit Untertiteln aus dem Transkript), sonst trägt ein eingesprochener Satz den Clip. Bei Aufträgen der Art 'material'.","https:\u002F\u002Fgithub.com\u002FFlissel\u002Fvibemind-os\u002Fblob\u002Fmaster\u002Fspaces\u002Fmarketing\u002Fvideo_claw\u002Fskills\u002Freel-aus-material\u002FSKILL.md",[],[135,136,137,138,139,140,141],"material","reel","reel aus material","person","videos","Art","clip",{"skillNo":143,"name":144,"nameZh":6,"description":145,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":146,"categories":147,"keywords":148,"publishedAt":42},"01M6JA9KSATRT42ACWT66514ZP","reel-aus-bildern","Reel ohne Filmmaterial: Titelkarte, 3–5 Markenbilder mit wechselnder Bewegung, Schlusskarte mit Handlung; Farben und Logo der Firma. 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