[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6AFM5CJCCM32V34AT425FHS":3,"skill-same:01M6AFM5CJCCM32V34AT425FHS":103},{"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":40,"linkOnly":41,"markdownContent":42,"markdownBody":43,"frontMatter":44,"refName":45,"commitSha":46,"skillDirectory":47,"forksCount":48,"repositoryHtmlUrl":49,"repositoryHomepageUrl":50,"ownerHtmlUrl":51,"ownerBlogUrl":52,"ownerDescription":53,"updatedAt":54,"install":55,"resources":68},"01M6AFM5CJCCM32V34AT425FHS","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.","davila7\u002Fclaude-code-templates","davila7","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F6216945?v=4","01M6ADEWWFPS191NFM0Y1EX1JH","Daniel Avila","01M6ADJHGM78PHQRD3XZTFDVKK",33332,"MIT","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fqiskit\u002FSKILL.md",[],[5,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39],"task","Simulations","running","when","materials","for","quantum algorithms","machine","gates","Materials Science","algorithms","machine learning","use","learning","science","Providers","ibm","quantum computing","quantum chemistry","toolkit","development","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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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",[110],{"categoryNo":111,"categoryName":112,"taxonomyType":113,"taxonomyTypeDesc":114},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[116,117,118,119,120,24,121,122,31,22,123,124,125,126,127,128,129,130,131,132],"scoring","pool","wealthville pool scoring","liquidity","which","exit","now","reduce","with","record","track","verdict","evm","solana","how","public","look",{"skillNo":134,"name":135,"nameZh":6,"description":136,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":137,"categories":138,"keywords":140,"publishedAt":40},"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",[139],{"categoryNo":111,"categoryName":112,"taxonomyType":113,"taxonomyTypeDesc":114},[141,142,143,144,145,146,147,148,149,150,151,22,152,153,154,155,156,157,158,159],"technical debt","debt","technical","technical debt auditor","auditor","coverage","complexity","markers","project","cleanup","unused","code","todo","work","plan","focus","signals","audit","dependency",{"skillNo":161,"name":162,"nameZh":6,"description":163,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":164,"categories":165,"keywords":175,"publishedAt":40},"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",[166,169,170],{"categoryNo":167,"categoryName":168,"taxonomyType":113,"taxonomyTypeDesc":114},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":111,"categoryName":112,"taxonomyType":113,"taxonomyTypeDesc":114},{"categoryNo":171,"categoryName":172,"taxonomyType":173,"taxonomyTypeDesc":174},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[176,177,178,179,180,181,182,24,183,184,185,186,187,188,189,31,190,191,22,192],"readonly","guard","sql guard","sql","readonly sql guard","erp","effect","read-only","db","just","tool","calls","only","MCP","access","database","query",{"skillNo":194,"name":195,"nameZh":6,"description":196,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":197,"licenseFileUrl":198,"sourceHtmlUrl":199,"categories":200,"keywords":204,"publishedAt":40},"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",[201],{"categoryNo":202,"categoryName":203,"taxonomyType":113,"taxonomyTypeDesc":114},"01JBSEEDCAT000000000000009","前端开发",[205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,24,227],"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":229,"name":230,"nameZh":6,"description":231,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":197,"licenseFileUrl":232,"sourceHtmlUrl":233,"categories":234,"keywords":239,"publishedAt":40},"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",[235,238],{"categoryNo":236,"categoryName":237,"taxonomyType":113,"taxonomyTypeDesc":114},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":202,"categoryName":203,"taxonomyType":113,"taxonomyTypeDesc":114},[240,241,242,243,244,206,245,215,216,246,38,247,248,24,249,124,250],"webapp testing","testing","webapp","browser","playwright","applications","debugging","logs","screenshots","local","behavior",{"skillNo":252,"name":253,"nameZh":6,"description":254,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":197,"licenseFileUrl":255,"sourceHtmlUrl":256,"categories":257,"keywords":265,"publishedAt":40},"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",[258,261,262],{"categoryNo":259,"categoryName":260,"taxonomyType":113,"taxonomyTypeDesc":114},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":111,"categoryName":112,"taxonomyType":113,"taxonomyTypeDesc":114},{"categoryNo":263,"categoryName":264,"taxonomyType":173,"taxonomyTypeDesc":174},"01JBSEEDCAT000000000000036","内容创作者",[266,267,268,269,270,271,272,273,208,274,218,152,275,276,277,278,279,188,280,22,281,282,283,284,285,130,286],"discernment nudge","nudge","interpretation","help","run","reasoning","data","writing","lookup","content","review","conversation","short","up","invoke","you","analysis","claims","argument","assemble","recommendations"]