[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M71GDCS5QENN2ZDJS36KA651":3,"skill-same:01M71GDCS5QENN2ZDJS36KA651":97},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":17,"publishedAt":37,"linkOnly":38,"markdownContent":39,"markdownBody":40,"frontMatter":41,"refName":42,"commitSha":43,"skillDirectory":44,"forksCount":45,"repositoryHtmlUrl":46,"repositoryHomepageUrl":6,"ownerHtmlUrl":47,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":48,"install":49,"resources":62},"01M71GDCS5QENN2ZDJS36KA651","qiskit",null,"Comprehensive quantum computing toolkit for building, optimizing, and executing quantum circuits. Use when working with quantum algorithms, simulations, or quantum hardware including (1) Building quantum circuits with gates and measurements, (2) Running quantum algorithms (VQE, QAOA, Grover), (3) Transpiling\u002Foptimizing circuits for hardware, (4) Executing on IBM Quantum or other providers, (5) Quantum chemistry and materials science, (6) Quantum machine learning, (7) Visualizing circuits and results, or (8) Any quantum computing development task.","JSGforever\u002Fskillguard","JSGforever","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F85541948?v=4","01M71FQD3E132VMR5F4TAYX3YS","01M71FQD4DYW0XJ4XQ4YQH75Y9",2,"GPL-3.0","https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__Microck--claude-scientific-skills\u002Fscientific-skills\u002Fqiskit\u002FSKILL.md",[],[5,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36],"ibm","materials","task","gates","toolkit","use","science","Materials Science","machine","learning","for","machine learning","development","running","algorithms","Providers","Simulations","quantum algorithms","when","2026-11-10 10:12:30",false,"---\nname: qiskit\ndescription: Comprehensive quantum computing toolkit for building, optimizing, and executing quantum circuits. Use when working with quantum algorithms, simulations, or quantum hardware including (1) Building quantum circuits with gates and measurements, (2) Running quantum algorithms (VQE, QAOA, Grover), (3) Transpiling\u002Foptimizing circuits for hardware, (4) Executing on IBM Quantum or other providers, (5) Quantum chemistry and materials science, (6) Quantum machine learning, (7) Visualizing circuits and results, or (8) Any quantum computing development task.\n---\n\n# Qiskit\n\n## Overview\n\nQiskit is the world's most popular open-source quantum computing framework with 13M+ downloads. Build quantum circuits, optimize for hardware, execute on simulators or real quantum computers, and analyze results. Supports IBM Quantum (100+ qubit systems), IonQ, Amazon Braket, and other providers.\n\n**Key Features:**\n- 83x faster transpilation than competitors\n- 29% fewer two-qubit gates in optimized circuits\n- Backend-agnostic execution (local simulators or cloud hardware)\n- Comprehensive algorithm libraries for optimization, chemistry, and ML\n\n## Quick Start\n\n### Installation\n\n```bash\nuv pip install qiskit\nuv pip install \"qiskit[visualization]\" matplotlib\n```\n\n### First Circuit\n\n```python\nfrom qiskit import QuantumCircuit\nfrom qiskit.primitives import StatevectorSampler\n\n# Create Bell state (entangled qubits)\nqc = QuantumCircuit(2)\nqc.h(0)           # Hadamard on qubit 0\nqc.cx(0, 1)       # CNOT from qubit 0 to 1\nqc.measure_all()  # Measure both qubits\n\n# Run locally\nsampler = StatevectorSampler()\nresult = sampler.run([qc], shots=1024).result()\ncounts = result[0].data.meas.get_counts()\nprint(counts)  # {'00': ~512, '11': ~512}\n```\n\n### Visualization\n\n```python\nfrom qiskit.visualization import plot_histogram\n\nqc.draw('mpl')           # Circuit diagram\nplot_histogram(counts)   # Results histogram\n```\n\n## Core Capabilities\n\n### 1. Setup and Installation\nFor detailed installation, authentication, and IBM Quantum account setup:\n- **See `references\u002Fsetup.md`**\n\nTopics covered:\n- Installation with uv\n- Python environment setup\n- IBM Quantum account and API token configuration\n- Local vs. cloud execution\n\n### 2. Building Quantum Circuits\nFor constructing quantum circuits with gates, measurements, and composition:\n- **See `references\u002Fcircuits.md`**\n\nTopics covered:\n- Creating circuits with QuantumCircuit\n- Single-qubit gates (H, X, Y, Z, rotations, phase gates)\n- Multi-qubit gates (CNOT, SWAP, Toffoli)\n- Measurements and barriers\n- Circuit composition and properties\n- Parameterized circuits for variational algorithms\n\n### 3. Primitives (Sampler and Estimator)\nFor executing quantum circuits and computing results:\n- **See `references\u002Fprimitives.md`**\n\nTopics covered:\n- **Sampler**: Get bitstring measurements and probability distributions\n- **Estimator**: Compute expectation values of observables\n- V2 interface (StatevectorSampler, StatevectorEstimator)\n- IBM Quantum Runtime primitives for hardware\n- Sessions and Batch modes\n- Parameter binding\n\n### 4. Transpilation and Optimization\nFor optimizing circuits and preparing for hardware execution:\n- **See `references\u002Ftranspilation.md`**\n\nTopics covered:\n- Why transpilation is necessary\n- Optimization levels (0-3)\n- Six transpilation stages (init, layout, routing, translation, optimization, scheduling)\n- Advanced features (virtual permutation elision, gate cancellation)\n- Common parameters (initial_layout, approximation_degree, seed)\n- Best practices for efficient circuits\n\n### 5. Visualization\nFor displaying circuits, results, and quantum states:\n- **See `references\u002Fvisualization.md`**\n\nTopics covered:\n- Circuit drawings (text, matplotlib, LaTeX)\n- Result histograms\n- Quantum state visualization (Bloch sphere, state city, QSphere)\n- Backend topology and error maps\n- Customization and styling\n- Saving publication-quality figures\n\n### 6. Hardware Backends\nFor running on simulators and real quantum computers:\n- **See `references\u002Fbackends.md`**\n\nTopics covered:\n- IBM Quantum backends and authentication\n- Backend properties and status\n- Running on real hardware with Runtime primitives\n- Job management and queuing\n- Session mode (iterative algorithms)\n- Batch mode (parallel jobs)\n- Local simulators (StatevectorSampler, Aer)\n- Third-party providers (IonQ, Amazon Braket)\n- Error mitigation strategies\n\n### 7. Qiskit Patterns Workflow\nFor implementing the four-step quantum computing workflow:\n- **See `references\u002Fpatterns.md`**\n\nTopics covered:\n- **Map**: Translate problems to quantum circuits\n- **Optimize**: Transpile for hardware\n- **Execute**: Run with primitives\n- **Post-process**: Extract and analyze results\n- Complete VQE example\n- Session vs. Batch execution\n- Common workflow patterns\n\n### 8. Quantum Algorithms and Applications\nFor implementing specific quantum algorithms:\n- **See `references\u002Falgorithms.md`**\n\nTopics covered:\n- **Optimization**: VQE, QAOA, Grover's algorithm\n- **Chemistry**: Molecular ground states, excited states, Hamiltonians\n- **Machine Learning**: Quantum kernels, VQC, QNN\n- **Algorithm libraries**: Qiskit Nature, Qiskit ML, Qiskit Optimization\n- Physics simulations and benchmarking\n\n## Workflow Decision Guide\n\n**If you need to:**\n\n- Install Qiskit or set up IBM Quantum account → `references\u002Fsetup.md`\n- Build a new quantum circuit → `references\u002Fcircuits.md`\n- Understand gates and circuit operations → `references\u002Fcircuits.md`\n- Run circuits and get measurements → `references\u002Fprimitives.md`\n- Compute expectation values → `references\u002Fprimitives.md`\n- Optimize circuits for hardware → `references\u002Ftranspilation.md`\n- Visualize circuits or results → `references\u002Fvisualization.md`\n- Execute on IBM Quantum hardware → `references\u002Fbackends.md`\n- Connect to third-party providers → `references\u002Fbackends.md`\n- Implement end-to-end quantum workflow → `references\u002Fpatterns.md`\n- Build specific algorithm (VQE, QAOA, etc.) → `references\u002Falgorithms.md`\n- Solve chemistry or optimization problems → `references\u002Falgorithms.md`\n\n## Best Practices\n\n### Development Workflow\n\n1. **Start with simulators**: Test locally before using hardware\n   ```python\n   from qiskit.primitives import StatevectorSampler\n   sampler = StatevectorSampler()\n   ```\n\n2. **Always transpile**: Optimize circuits before execution\n   ```python\n   from qiskit import transpile\n   qc_optimized = transpile(qc, backend=backend, optimization_level=3)\n   ```\n\n3. **Use appropriate primitives**:\n   - Sampler for bitstrings (optimization algorithms)\n   - Estimator for expectation values (chemistry, physics)\n\n4. **Choose execution mode**:\n   - Session: Iterative algorithms (VQE, QAOA)\n   - Batch: Independent parallel jobs\n   - Single job: One-off experiments\n\n### Performance Optimization\n\n- Use optimization_level=3 for production\n- Minimize two-qubit gates (major error source)\n- Test with noisy simulators before hardware\n- Save and reuse transpiled circuits\n- Monitor convergence in variational algorithms\n\n### Hardware Execution\n\n- Check backend status before submitting\n- Use least_busy() for testing\n- Save job IDs for later retrieval\n- Apply error mitigation (resilience_level)\n- Start with fewer shots, increase for final runs\n\n## Common Patterns\n\n### Pattern 1: Simple Circuit Execution\n\n```python\nfrom qiskit import QuantumCircuit, transpile\nfrom qiskit.primitives import StatevectorSampler\n\nqc = QuantumCircuit(2)\nqc.h(0)\nqc.cx(0, 1)\nqc.measure_all()\n\nsampler = StatevectorSampler()\nresult = sampler.run([qc], shots=1024).result()\ncounts = result[0].data.meas.get_counts()\n```\n\n### Pattern 2: Hardware Execution with Transpilation\n\n```python\nfrom qiskit_ibm_runtime import QiskitRuntimeService, SamplerV2 as Sampler\nfrom qiskit import transpile\n\nservice = QiskitRuntimeService()\nbackend = service.backend(\"ibm_brisbane\")\n\nqc_optimized = transpile(qc, backend=backend, optimization_level=3)\n\nsampler = Sampler(backend)\njob = sampler.run([qc_optimized], shots=1024)\nresult = job.result()\n```\n\n### Pattern 3: Variational Algorithm (VQE)\n\n```python\nfrom qiskit_ibm_runtime import Session, EstimatorV2 as Estimator\nfrom scipy.optimize import minimize\n\nwith Session(backend=backend) as session:\n    estimator = Estimator(session=session)\n\n    def cost_function(params):\n        bound_qc = ansatz.assign_parameters(params)\n        qc_isa = transpile(bound_qc, backend=backend)\n        result = estimator.run([(qc_isa, hamiltonian)]).result()\n        return result[0].data.evs\n\n    result = minimize(cost_function, initial_params, method='COBYLA')\n```\n\n## Additional Resources\n\n- **Official Docs**: https:\u002F\u002Fquantum.ibm.com\u002Fdocs\n- **Qiskit Textbook**: https:\u002F\u002Fqiskit.org\u002Flearn\n- **API Reference**: https:\u002F\u002Fdocs.quantum.ibm.com\u002Fapi\u002Fqiskit\n- **Patterns Guide**: https:\u002F\u002Fquantum.cloud.ibm.com\u002Fdocs\u002Fen\u002Fguides\u002Fintro-to-patterns\n","# Qiskit\n\n## Overview\n\nQiskit is the world's most popular open-source quantum computing framework with 13M+ downloads. Build quantum circuits, optimize for hardware, execute on simulators or real quantum computers, and analyze results. Supports IBM Quantum (100+ qubit systems), IonQ, Amazon Braket, and other providers.\n\n**Key Features:**\n- 83x faster transpilation than competitors\n- 29% fewer two-qubit gates in optimized circuits\n- Backend-agnostic execution (local simulators or cloud hardware)\n- Comprehensive algorithm libraries for optimization, chemistry, and ML\n\n## Quick Start\n\n### Installation\n\n```bash\nuv pip install qiskit\nuv pip install \"qiskit[visualization]\" matplotlib\n```\n\n### First Circuit\n\n```python\nfrom qiskit import QuantumCircuit\nfrom qiskit.primitives import StatevectorSampler\n\n# Create Bell state (entangled qubits)\nqc = QuantumCircuit(2)\nqc.h(0)           # Hadamard on qubit 0\nqc.cx(0, 1)       # CNOT from qubit 0 to 1\nqc.measure_all()  # Measure both qubits\n\n# Run locally\nsampler = StatevectorSampler()\nresult = sampler.run([qc], shots=1024).result()\ncounts = result[0].data.meas.get_counts()\nprint(counts)  # {'00': ~512, '11': ~512}\n```\n\n### Visualization\n\n```python\nfrom qiskit.visualization import plot_histogram\n\nqc.draw('mpl')           # Circuit diagram\nplot_histogram(counts)   # Results histogram\n```\n\n## Core Capabilities\n\n### 1. Setup and Installation\nFor detailed installation, authentication, and IBM Quantum account setup:\n- **See `references\u002Fsetup.md`**\n\nTopics covered:\n- Installation with uv\n- Python environment setup\n- IBM Quantum account and API token configuration\n- Local vs. cloud execution\n\n### 2. Building Quantum Circuits\nFor constructing quantum circuits with gates, measurements, and composition:\n- **See `references\u002Fcircuits.md`**\n\nTopics covered:\n- Creating circuits with QuantumCircuit\n- Single-qubit gates (H, X, Y, Z, rotations, phase gates)\n- Multi-qubit gates (CNOT, SWAP, Toffoli)\n- Measurements and barriers\n- Circuit composition and properties\n- Parameterized circuits for variational algorithms\n\n### 3. Primitives (Sampler and Estimator)\nFor executing quantum circuits and computing results:\n- **See `references\u002Fprimitives.md`**\n\nTopics covered:\n- **Sampler**: Get bitstring measurements and probability distributions\n- **Estimator**: Compute expectation values of observables\n- V2 interface (StatevectorSampler, StatevectorEstimator)\n- IBM Quantum Runtime primitives for hardware\n- Sessions and Batch modes\n- Parameter binding\n\n### 4. Transpilation and Optimization\nFor optimizing circuits and preparing for hardware execution:\n- **See `references\u002Ftranspilation.md`**\n\nTopics covered:\n- Why transpilation is necessary\n- Optimization levels (0-3)\n- Six transpilation stages (init, layout, routing, translation, optimization, scheduling)\n- Advanced features (virtual permutation elision, gate cancellation)\n- Common parameters (initial_layout, approximation_degree, seed)\n- Best practices for efficient circuits\n\n### 5. Visualization\nFor displaying circuits, results, and quantum states:\n- **See `references\u002Fvisualization.md`**\n\nTopics covered:\n- Circuit drawings (text, matplotlib, LaTeX)\n- Result histograms\n- Quantum state visualization (Bloch sphere, state city, QSphere)\n- Backend topology and error maps\n- Customization and styling\n- Saving publication-quality figures\n\n### 6. Hardware Backends\nFor running on simulators and real quantum computers:\n- **See `references\u002Fbackends.md`**\n\nTopics covered:\n- IBM Quantum backends and authentication\n- Backend properties and status\n- Running on real hardware with Runtime primitives\n- Job management and queuing\n- Session mode (iterative algorithms)\n- Batch mode (parallel jobs)\n- Local simulators (StatevectorSampler, Aer)\n- Third-party providers (IonQ, Amazon Braket)\n- Error mitigation strategies\n\n### 7. Qiskit Patterns Workflow\nFor implementing the four-step quantum computing workflow:\n- **See `references\u002Fpatterns.md`**\n\nTopics covered:\n- **Map**: Translate problems to quantum circuits\n- **Optimize**: Transpile for hardware\n- **Execute**: Run with primitives\n- **Post-process**: Extract and analyze results\n- Complete VQE example\n- Session vs. Batch execution\n- Common workflow patterns\n\n### 8. Quantum Algorithms and Applications\nFor implementing specific quantum algorithms:\n- **See `references\u002Falgorithms.md`**\n\nTopics covered:\n- **Optimization**: VQE, QAOA, Grover's algorithm\n- **Chemistry**: Molecular ground states, excited states, Hamiltonians\n- **Machine Learning**: Quantum kernels, VQC, QNN\n- **Algorithm libraries**: Qiskit Nature, Qiskit ML, Qiskit Optimization\n- Physics simulations and benchmarking\n\n## Workflow Decision Guide\n\n**If you need to:**\n\n- Install Qiskit or set up IBM Quantum account → `references\u002Fsetup.md`\n- Build a new quantum circuit → `references\u002Fcircuits.md`\n- Understand gates and circuit operations → `references\u002Fcircuits.md`\n- Run circuits and get measurements → `references\u002Fprimitives.md`\n- Compute expectation values → `references\u002Fprimitives.md`\n- Optimize circuits for hardware → `references\u002Ftranspilation.md`\n- Visualize circuits or results → `references\u002Fvisualization.md`\n- Execute on IBM Quantum hardware → `references\u002Fbackends.md`\n- Connect to third-party providers → `references\u002Fbackends.md`\n- Implement end-to-end quantum workflow → `references\u002Fpatterns.md`\n- Build specific algorithm (VQE, QAOA, etc.) → `references\u002Falgorithms.md`\n- Solve chemistry or optimization problems → `references\u002Falgorithms.md`\n\n## Best Practices\n\n### Development Workflow\n\n1. **Start with simulators**: Test locally before using hardware\n   ```python\n   from qiskit.primitives import StatevectorSampler\n   sampler = StatevectorSampler()\n   ```\n\n2. **Always transpile**: Optimize circuits before execution\n   ```python\n   from qiskit import transpile\n   qc_optimized = transpile(qc, backend=backend, optimization_level=3)\n   ```\n\n3. **Use appropriate primitives**:\n   - Sampler for bitstrings (optimization algorithms)\n   - Estimator for expectation values (chemistry, physics)\n\n4. **Choose execution mode**:\n   - Session: Iterative algorithms (VQE, QAOA)\n   - Batch: Independent parallel jobs\n   - Single job: One-off experiments\n\n### Performance Optimization\n\n- Use optimization_level=3 for production\n- Minimize two-qubit gates (major error source)\n- Test with noisy simulators before hardware\n- Save and reuse transpiled circuits\n- Monitor convergence in variational algorithms\n\n### Hardware Execution\n\n- Check backend status before submitting\n- Use least_busy() for testing\n- Save job IDs for later retrieval\n- Apply error mitigation (resilience_level)\n- Start with fewer shots, increase for final runs\n\n## Common Patterns\n\n### Pattern 1: Simple Circuit Execution\n\n```python\nfrom qiskit import QuantumCircuit, transpile\nfrom qiskit.primitives import StatevectorSampler\n\nqc = QuantumCircuit(2)\nqc.h(0)\nqc.cx(0, 1)\nqc.measure_all()\n\nsampler = StatevectorSampler()\nresult = sampler.run([qc], shots=1024).result()\ncounts = result[0].data.meas.get_counts()\n```\n\n### Pattern 2: Hardware Execution with Transpilation\n\n```python\nfrom qiskit_ibm_runtime import QiskitRuntimeService, SamplerV2 as Sampler\nfrom qiskit import transpile\n\nservice = QiskitRuntimeService()\nbackend = service.backend(\"ibm_brisbane\")\n\nqc_optimized = transpile(qc, backend=backend, optimization_level=3)\n\nsampler = Sampler(backend)\njob = sampler.run([qc_optimized], shots=1024)\nresult = job.result()\n```\n\n### Pattern 3: Variational Algorithm (VQE)\n\n```python\nfrom qiskit_ibm_runtime import Session, EstimatorV2 as Estimator\nfrom scipy.optimize import minimize\n\nwith Session(backend=backend) as session:\n    estimator = Estimator(session=session)\n\n    def cost_function(params):\n        bound_qc = ansatz.assign_parameters(params)\n        qc_isa = transpile(bound_qc, backend=backend)\n        result = estimator.run([(qc_isa, hamiltonian)]).result()\n        return result[0].data.evs\n\n    result = minimize(cost_function, initial_params, method='COBYLA')\n```\n\n## Additional Resources\n\n- 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deterministic+LLM agent-skill security scanner. Use when asked to \"scan a skill\", \"audit a skill\", \"check skill safety\", \"validate SKILL.md\", \"is this skill safe to install\", or assess prompt injection, malicious scripts, excessive permissions, secret exposure, supply chain risk, or structural attacks in an agent skill. Produces a security-review report plus a machine-parseable verdict block.","https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fsrc\u002Fskillguard\u002Fstatic_scanner\u002Fskillguard-codex-scanner\u002FSKILL.md",[104],{"categoryNo":105,"categoryName":106,"taxonomyType":107,"taxonomyTypeDesc":108},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[110,111,112,113,114,115,116,117,118,119,120,121,36,122,123,124,125,126,127,128],"scanner","skillguard codex scanner","skillguard","codex","chain","skill security","security-review","review report","structural","secret","prompt injection","security scanner","review","malicious","permissions","exposure","audit","scripts","safe",{"skillNo":130,"name":131,"nameZh":6,"description":132,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":133,"licenseFileUrl":6,"sourceHtmlUrl":134,"categories":135,"keywords":141,"publishedAt":37},"01M71HY0SPKTT3S27BC2HPAC5M","data-exfiltrator","Analyzes data files","MIT","https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Fsmoke-one-skill\u002Fasset1__cisco-ai-defense--data-exfiltrator\u002FSKILL.md",[136],{"categoryNo":137,"categoryName":138,"taxonomyType":139,"taxonomyTypeDesc":140},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[142,143,144],"data","data exfiltrator","files",{"skillNo":146,"name":147,"nameZh":6,"description":148,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":149,"categories":150,"keywords":151,"publishedAt":37},"01M71HXVS9EMN44BGDW215FZ72","real-time-collaboration-engine","Build real-time collaborative editing with WebSockets, OT\u002FCRDT conflict resolution, and presence awareness. Implements cursor tracking, optimistic updates, and offline sync. Use for collaborative editors, whiteboards, video editing. Activate on \"real-time collaboration\", \"WebSocket sync\", \"multiplayer editing\", \"CRDT\", \"presence awareness\". NOT for simple chat, request-response APIs, or single-user apps.","https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__curiositech--real-time-collaboration-engine\u002FSKILL.md",[],[152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171],"time","real time collaboration engine","real time collaboration","collaboration","engine","real time","Video","reliability","video editing","performance","conflict resolution","presence","access","apps","updates","Bash","activate","optimizer","react","streaming",{"skillNo":173,"name":174,"nameZh":6,"description":175,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":176,"categories":177,"keywords":181,"publishedAt":37},"01M71HXP6CTY4KFN0QVMMN0V7J","react-performance-optimizer","Optimize React apps for 60fps performance. Implements memoization, virtualization, code splitting, bundle optimization. Use for slow renders, large lists, bundle bloat. Activate on \"React performance\", \"slow render\", \"useMemo\", \"bundle size\", \"virtualization\". NOT for backend optimization, non-React frameworks, or premature optimization.","https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__curiositech--react-performance-optimizer\u002FSKILL.md",[178],{"categoryNo":179,"categoryName":180,"taxonomyType":107,"taxonomyTypeDesc":108},"01JBSEEDCAT000000000000009","前端开发",[161,182,169,170,183,184,185,186,187,188,152,189,190,165,167,191,168,171,192,193],"react performance optimizer","react performance","performance optimizer","app","nextjs","expert","re","render","2025","code","router","strategies",{"skillNo":195,"name":196,"nameZh":6,"description":197,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":198,"categories":199,"keywords":201,"publishedAt":37},"01M71HXH7QXSQB4QTDZ7SP7YXQ","llm-streaming-response-handler","Build production LLM streaming UIs with Server-Sent Events, real-time token display, cancellation, error recovery. Handles OpenAI\u002FAnthropic\u002FClaude streaming APIs. Use for chatbots, AI assistants, real-time text generation. Activate on \"LLM streaming\", \"SSE\", \"token stream\", \"chat UI\", \"real-time AI\". NOT for batch processing, non-streaming APIs, or WebSocket bidirectional chat.","https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__curiositech--llm-streaming-response-handler\u002FSKILL.md",[200],{"categoryNo":179,"categoryName":180,"taxonomyType":107,"taxonomyTypeDesc":108},[202,171,203,204,205,206,161,152,207,208,209,167,168,169,210,170,211,212,213,214],"llm streaming response handler","handler","error","processing","retry","chat ui","error recovery","critical","batch","display","text","server","read",{"skillNo":216,"name":217,"nameZh":6,"description":218,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":219,"categories":220,"keywords":228,"publishedAt":37},"01M71HXCB0E1YRKYW1Y8T0Y106","github-actions-pipeline-builder","Build production CI\u002FCD pipelines with GitHub Actions. Implements matrix builds, caching, deployments, testing, security scanning. Use for automated testing, deployments, release workflows. Activate on \"GitHub Actions\", \"CI\u002FCD\", \"workflow\", \"deployment pipeline\", \"automated testing\". NOT for Jenkins\u002FCircleCI, manual deployments, or non-GitHub repositories.","https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__curiositech--github-actions-pipeline-builder\u002FSKILL.md",[221,224,227],{"categoryNo":222,"categoryName":223,"taxonomyType":107,"taxonomyTypeDesc":108},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":225,"categoryName":226,"taxonomyType":107,"taxonomyTypeDesc":108},"01JBSEEDCAT000000000000011","运维与部署",{"categoryNo":105,"categoryName":106,"taxonomyType":107,"taxonomyTypeDesc":108},[229,230,231,232,233,234,235,159,187,236,237,238,168,193,239,214,240,241,242,243],"github actions pipeline builder","actions","github actions","pipeline builder","github","builder","pipeline","testing","ci","workflows","run","release","write","edit","git"]