[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6XGMQZMQPQNP02CAWN18QD0":3,"skill-same:01M6XGMQZMQPQNP02CAWN18QD0":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":23,"publishedAt":44,"linkOnly":45,"markdownContent":46,"markdownBody":47,"frontMatter":48,"refName":56,"commitSha":57,"skillDirectory":58,"forksCount":14,"repositoryHtmlUrl":59,"repositoryHomepageUrl":6,"ownerHtmlUrl":60,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":61,"install":62,"resources":75},"01M6XGMQZMQPQNP02CAWN18QD0","physics-databases",null,"Query physics databases — NIST CODATA constants, NIST Chemistry WebBook, Materials Project, Particle Data Group (PDG), OEIS sequences. Always use these instead of hardcoding physical constants.","fadrienne\u002FMyGitHub","fadrienne","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F197874571?v=4","01M6X64V8VWCRSMFZ4EPS2BWXG","Francesca Adrienne","01M6X64VD5MW4F9PHDWQKS7ZAF",0,"MIT","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fphysics\u002Fphysics-databases\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口","SCENARIO","使用场景",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43],"physics databases","databases","physics","physical","PDG","Materials Project","Physical Constants","CODATA","constants","sequences","materials","synthetic","database","project","scipy","query","numpy","use","NIST","data","2026-11-10 10:12:30",false,"---\nname: physics-databases\ndescription: Query physics databases — NIST CODATA constants, NIST Chemistry WebBook, Materials Project, Particle Data Group (PDG), OEIS sequences. Always use these instead of hardcoding physical constants.\ncategory: physics\nversion: 1.0.0\nauthor: Synthetic Sciences\nlicense: MIT\ntags: [NIST, CODATA, Physical Constants, Database, Materials Project, PDG]\ndependencies: [\"scipy>=1.11.0\", \"numpy>=1.24.0\"]\n---\n\n# Physics Databases\n\n## Overview\n\nAccess physical constants and reference data from authoritative databases. Never hardcode physical constants — always query from `scipy.constants` (CODATA 2018) or NIST APIs.\n\n## CRITICAL: Always Use Database Values\n\n- NEVER type physical constants from memory\n- ALWAYS use `scipy.constants` or NIST lookup\n- Constants change between CODATA editions (e.g., 2014 → 2018 → 2022)\n\n## Core Workflows\n\n### 1. CODATA Physical Constants (scipy.constants)\n\n```python\nfrom scipy import constants as const\n\n# Fundamental constants\nprint(\"=== Fundamental Constants (CODATA 2018) ===\")\nprint(f\"Speed of light:       c  = {const.c:.10e} m\u002Fs\")\nprint(f\"Planck constant:      h  = {const.h:.10e} J·s\")\nprint(f\"Reduced Planck:       ℏ  = {const.hbar:.10e} J·s\")\nprint(f\"Boltzmann constant:   k  = {const.k:.10e} J\u002FK\")\nprint(f\"Gravitational const:  G  = {const.G:.10e} m³\u002F(kg·s²)\")\nprint(f\"Elementary charge:    e  = {const.e:.10e} C\")\nprint(f\"Electron mass:        mₑ = {const.m_e:.10e} kg\")\nprint(f\"Proton mass:          mₚ = {const.m_p:.10e} kg\")\nprint(f\"Neutron mass:         mₙ = {const.m_n:.10e} kg\")\nprint(f\"Vacuum permittivity:  ε₀ = {const.epsilon_0:.10e} F\u002Fm\")\nprint(f\"Vacuum permeability:  μ₀ = {const.mu_0:.10e} H\u002Fm\")\nprint(f\"Avogadro number:      Nₐ = {const.N_A:.10e} mol⁻¹\")\nprint(f\"Gas constant:         R  = {const.R:.10e} J\u002F(mol·K)\")\nprint(f\"Stefan-Boltzmann:     σ  = {const.sigma:.10e} W\u002F(m²·K⁴)\")\nprint(f\"Fine structure:       α  = {const.fine_structure:.10e}\")\nprint(f\"Rydberg constant:     R∞ = {const.Rydberg:.10e} m⁻¹\")\nprint(f\"Bohr radius:          a₀ = {const.physical_constants['Bohr radius'][0]:.10e} m\")\nprint(f\"Standard gravity:     g  = {const.g:.6f} m\u002Fs²\")\nprint(f\"Standard atmosphere:  atm= {const.atm:.2f} Pa\")\n```\n\n### 2. Unit Conversions\n\n```python\n# Energy conversions\nprint(\"\\n=== Energy Conversions ===\")\nprint(f\"1 eV  = {const.eV:.10e} J\")\nprint(f\"1 cal = {const.calorie:.6f} J\")\nprint(f\"1 BTU = {const.Btu:.6f} J\")\nprint(f\"1 erg = {const.erg:.10e} J\")\n\n# Length conversions\nprint(\"\\n=== Length Conversions ===\")\nprint(f\"1 Å   = {const.angstrom:.10e} m\")\nprint(f\"1 au  = {const.au:.6e} m\")  # astronomical unit\nprint(f\"1 ly  = {const.light_year:.6e} m\")\nprint(f\"1 pc  = {const.parsec:.6e} m\")\n\n# Temperature\nprint(f\"\\n0°C = {const.zero_Celsius:.2f} K\")\n\n# Pressure\nprint(f\"\\n1 atm = {const.atm:.2f} Pa\")\nprint(f\"1 bar = {const.bar:.2f} Pa\")\nprint(f\"1 torr = {const.torr:.6f} Pa\")\n```\n\n### 3. Look Up Any CODATA Constant\n\n```python\n# Search by name\ndef find_constant(keyword):\n    \"\"\"Search CODATA constants by keyword.\"\"\"\n    results = []\n    for name, (value, unit, uncertainty) in const.physical_constants.items():\n        if keyword.lower() in name.lower():\n            results.append((name, value, unit, uncertainty))\n            print(f\"  {name}\")\n            print(f\"    = {value:.10e} {unit}\")\n            print(f\"    ± {uncertainty:.4e}\")\n    return results\n\nprint(\"Searching for 'Bohr':\")\nfind_constant(\"Bohr\")\n\nprint(\"\\nSearching for 'magnetic moment':\")\nfind_constant(\"magnetic moment\")\n```\n\n### 4. Materials Project API\n\n```python\n# pip install mp-api\n# Requires API key: https:\u002F\u002Fmaterialsproject.org\u002Fapi\n\ndef query_materials_project(formula, api_key=None):\n    \"\"\"\n    Query Materials Project for material properties.\n    Set MP_API_KEY environment variable or pass api_key.\n    \"\"\"\n    try:\n        from mp_api.client import MPRester\n        import os\n\n        key = api_key or os.environ.get('MP_API_KEY')\n        if not key:\n            print(\"Set MP_API_KEY environment variable\")\n            return None\n\n        with MPRester(key) as mpr:\n            docs = mpr.summary.search(formula=[formula])\n            for doc in docs[:5]:\n                print(f\"  {doc.material_id}: {doc.formula_pretty}\")\n                print(f\"    Space group: {doc.symmetry.symbol}\")\n                print(f\"    Energy above hull: {doc.energy_above_hull:.4f} eV\u002Fatom\")\n                print(f\"    Band gap: {doc.band_gap:.3f} eV\")\n                print(f\"    Density: {doc.density:.3f} g\u002Fcm³\")\n            return docs\n    except ImportError:\n        print(\"Install mp-api: pip install mp-api\")\n        return None\n\n# Example: query_materials_project(\"Si\")\n```\n\n### 5. Particle Data Group (PDG)\n\n```python\n# Key particle properties (from PDG 2024)\n# These are reference values — for precision work, check pdg.lbl.gov\n\nparticles = {\n    'electron': {\n        'mass_MeV': 0.51099895,\n        'charge': -1,\n        'spin': 0.5,\n        'lifetime': 'stable',\n    },\n    'proton': {\n        'mass_MeV': 938.27208816,\n        'charge': +1,\n        'spin': 0.5,\n        'lifetime': 'stable (> 10^34 years)',\n    },\n    'neutron': {\n        'mass_MeV': 939.56542052,\n        'charge': 0,\n        'spin': 0.5,\n        'lifetime_s': 878.4,  # ~14.6 minutes\n    },\n    'muon': {\n        'mass_MeV': 105.6583755,\n        'charge': -1,\n        'spin': 0.5,\n        'lifetime_s': 2.1969811e-6,\n    },\n    'pion_charged': {\n        'mass_MeV': 139.57039,\n        'charge': +1,\n        'spin': 0,\n        'lifetime_s': 2.6033e-8,\n    },\n    'W_boson': {\n        'mass_GeV': 80.3692,\n        'charge': +1,\n        'spin': 1,\n    },\n    'Z_boson': {\n        'mass_GeV': 91.1876,\n        'charge': 0,\n        'spin': 1,\n    },\n    'Higgs': {\n        'mass_GeV': 125.20,\n        'charge': 0,\n        'spin': 0,\n    },\n}\n\n# Always cite: \"Particle Data Group, Phys. Rev. D 110, 030001 (2024)\"\n# For precision values: https:\u002F\u002Fpdg.lbl.gov\n```\n\n### 6. Astronomical Constants\n\n```python\n# Solar system data (IAU 2015 values)\nprint(\"\\n=== Astronomical Constants ===\")\nprint(f\"Solar mass:    M☉ = 1.98892e30 kg\")\nprint(f\"Solar radius:  R☉ = 6.9634e8 m\")\nprint(f\"Solar luminosity: L☉ = 3.828e26 W\")\nprint(f\"Earth mass:    M⊕ = 5.9722e24 kg\")\nprint(f\"Earth radius:  R⊕ = 6.3781e6 m\")\nprint(f\"Moon mass:     M☾ = 7.342e22 kg\")\nprint(f\"AU:            1 AU = {const.au:.6e} m\")\n\n# For precise values, use astropy:\n# from astropy import constants as astro_const\n# print(astro_const.M_sun)\n```\n\n## Quick Reference\n\n| Constant | Symbol | Value | scipy.constants |\n|---|---|---|---|\n| Speed of light | c | 2.998×10⁸ m\u002Fs | `const.c` |\n| Planck | h | 6.626×10⁻³⁴ J·s | `const.h` |\n| Boltzmann | k_B | 1.381×10⁻²³ J\u002FK | `const.k` |\n| Gravitational | G | 6.674×10⁻¹¹ m³\u002F(kg·s²) | `const.G` |\n| Elementary charge | e | 1.602×10⁻¹⁹ C | `const.e` |\n| Electron mass | m_e | 9.109×10⁻³¹ kg | `const.m_e` |\n| Proton mass | m_p | 1.673×10⁻²⁷ kg | `const.m_p` |\n| Avogadro | N_A | 6.022×10²³ mol⁻¹ | `const.N_A` |\n| Gas constant | R | 8.314 J\u002F(mol·K) | `const.R` |\n| 1 eV | — | 1.602×10⁻¹⁹ J | `const.eV` |\n| 1 Å | — | 1.0×10⁻¹⁰ m | `const.angstrom` |\n\n## Tips\n\n1. **Always `from scipy import constants as const`** — never type values manually\n2. **Check units**: `const.physical_constants['Bohr radius']` returns (value, unit, uncertainty)\n3. **For astrophysics**: use `astropy.constants` for additional astronomical constants\n4. **For materials**: use `mp-api` to query the Materials Project database\n5. **Cite your source**: \"CODATA 2018 via scipy.constants\" or \"PDG 2024\"\n","# Physics Databases\n\n## Overview\n\nAccess physical constants and reference data from authoritative databases. Never hardcode physical constants — always query from `scipy.constants` (CODATA 2018) or NIST APIs.\n\n## CRITICAL: Always Use Database Values\n\n- NEVER type physical constants from memory\n- ALWAYS use `scipy.constants` or NIST lookup\n- Constants change between CODATA editions (e.g., 2014 → 2018 → 2022)\n\n## Core Workflows\n\n### 1. CODATA Physical Constants (scipy.constants)\n\n```python\nfrom scipy import constants as const\n\n# Fundamental constants\nprint(\"=== Fundamental Constants (CODATA 2018) ===\")\nprint(f\"Speed of light:       c  = {const.c:.10e} m\u002Fs\")\nprint(f\"Planck constant:      h  = {const.h:.10e} J·s\")\nprint(f\"Reduced Planck:       ℏ  = {const.hbar:.10e} J·s\")\nprint(f\"Boltzmann constant:   k  = {const.k:.10e} J\u002FK\")\nprint(f\"Gravitational const:  G  = {const.G:.10e} m³\u002F(kg·s²)\")\nprint(f\"Elementary charge:    e  = {const.e:.10e} C\")\nprint(f\"Electron mass:        mₑ = {const.m_e:.10e} kg\")\nprint(f\"Proton mass:          mₚ = {const.m_p:.10e} kg\")\nprint(f\"Neutron mass:         mₙ = {const.m_n:.10e} kg\")\nprint(f\"Vacuum permittivity:  ε₀ = {const.epsilon_0:.10e} F\u002Fm\")\nprint(f\"Vacuum permeability:  μ₀ = {const.mu_0:.10e} H\u002Fm\")\nprint(f\"Avogadro number:      Nₐ = {const.N_A:.10e} mol⁻¹\")\nprint(f\"Gas constant:         R  = {const.R:.10e} J\u002F(mol·K)\")\nprint(f\"Stefan-Boltzmann:     σ  = {const.sigma:.10e} W\u002F(m²·K⁴)\")\nprint(f\"Fine structure:       α  = {const.fine_structure:.10e}\")\nprint(f\"Rydberg constant:     R∞ = {const.Rydberg:.10e} m⁻¹\")\nprint(f\"Bohr radius:          a₀ = {const.physical_constants['Bohr radius'][0]:.10e} m\")\nprint(f\"Standard gravity:     g  = {const.g:.6f} m\u002Fs²\")\nprint(f\"Standard atmosphere:  atm= {const.atm:.2f} Pa\")\n```\n\n### 2. Unit Conversions\n\n```python\n# Energy conversions\nprint(\"\\n=== Energy Conversions ===\")\nprint(f\"1 eV  = {const.eV:.10e} J\")\nprint(f\"1 cal = {const.calorie:.6f} J\")\nprint(f\"1 BTU = {const.Btu:.6f} J\")\nprint(f\"1 erg = {const.erg:.10e} J\")\n\n# Length conversions\nprint(\"\\n=== Length Conversions ===\")\nprint(f\"1 Å   = {const.angstrom:.10e} m\")\nprint(f\"1 au  = {const.au:.6e} m\")  # astronomical unit\nprint(f\"1 ly  = {const.light_year:.6e} m\")\nprint(f\"1 pc  = {const.parsec:.6e} m\")\n\n# Temperature\nprint(f\"\\n0°C = {const.zero_Celsius:.2f} K\")\n\n# Pressure\nprint(f\"\\n1 atm = {const.atm:.2f} Pa\")\nprint(f\"1 bar = {const.bar:.2f} Pa\")\nprint(f\"1 torr = {const.torr:.6f} Pa\")\n```\n\n### 3. Look Up Any CODATA Constant\n\n```python\n# Search by name\ndef find_constant(keyword):\n    \"\"\"Search CODATA constants by keyword.\"\"\"\n    results = []\n    for name, (value, unit, uncertainty) in const.physical_constants.items():\n        if keyword.lower() in name.lower():\n            results.append((name, value, unit, uncertainty))\n            print(f\"  {name}\")\n            print(f\"    = {value:.10e} {unit}\")\n            print(f\"    ± {uncertainty:.4e}\")\n    return results\n\nprint(\"Searching for 'Bohr':\")\nfind_constant(\"Bohr\")\n\nprint(\"\\nSearching for 'magnetic moment':\")\nfind_constant(\"magnetic moment\")\n```\n\n### 4. Materials Project API\n\n```python\n# pip install mp-api\n# Requires API key: https:\u002F\u002Fmaterialsproject.org\u002Fapi\n\ndef query_materials_project(formula, api_key=None):\n    \"\"\"\n    Query Materials Project for material properties.\n    Set MP_API_KEY environment variable or pass api_key.\n    \"\"\"\n    try:\n        from mp_api.client import MPRester\n        import os\n\n        key = api_key or os.environ.get('MP_API_KEY')\n        if not key:\n            print(\"Set MP_API_KEY environment variable\")\n            return None\n\n        with MPRester(key) as mpr:\n            docs = mpr.summary.search(formula=[formula])\n            for doc in docs[:5]:\n                print(f\"  {doc.material_id}: {doc.formula_pretty}\")\n                print(f\"    Space group: {doc.symmetry.symbol}\")\n                print(f\"    Energy above hull: {doc.energy_above_hull:.4f} eV\u002Fatom\")\n                print(f\"    Band gap: {doc.band_gap:.3f} eV\")\n                print(f\"    Density: {doc.density:.3f} g\u002Fcm³\")\n            return docs\n    except ImportError:\n        print(\"Install mp-api: pip install mp-api\")\n        return None\n\n# Example: query_materials_project(\"Si\")\n```\n\n### 5. Particle Data Group (PDG)\n\n```python\n# Key particle properties (from PDG 2024)\n# These are reference values — for precision work, check pdg.lbl.gov\n\nparticles = {\n    'electron': {\n        'mass_MeV': 0.51099895,\n        'charge': -1,\n        'spin': 0.5,\n        'lifetime': 'stable',\n    },\n    'proton': {\n        'mass_MeV': 938.27208816,\n        'charge': +1,\n        'spin': 0.5,\n        'lifetime': 'stable (> 10^34 years)',\n    },\n    'neutron': {\n        'mass_MeV': 939.56542052,\n        'charge': 0,\n        'spin': 0.5,\n        'lifetime_s': 878.4,  # ~14.6 minutes\n    },\n    'muon': {\n        'mass_MeV': 105.6583755,\n        'charge': -1,\n        'spin': 0.5,\n        'lifetime_s': 2.1969811e-6,\n    },\n    'pion_charged': {\n        'mass_MeV': 139.57039,\n        'charge': +1,\n        'spin': 0,\n        'lifetime_s': 2.6033e-8,\n    },\n    'W_boson': {\n        'mass_GeV': 80.3692,\n        'charge': +1,\n        'spin': 1,\n    },\n    'Z_boson': {\n        'mass_GeV': 91.1876,\n        'charge': 0,\n        'spin': 1,\n    },\n    'Higgs': {\n        'mass_GeV': 125.20,\n        'charge': 0,\n        'spin': 0,\n    },\n}\n\n# Always cite: \"Particle Data Group, Phys. Rev. D 110, 030001 (2024)\"\n# For precision values: https:\u002F\u002Fpdg.lbl.gov\n```\n\n### 6. Astronomical Constants\n\n```python\n# Solar system data (IAU 2015 values)\nprint(\"\\n=== Astronomical Constants ===\")\nprint(f\"Solar mass:    M☉ = 1.98892e30 kg\")\nprint(f\"Solar radius:  R☉ = 6.9634e8 m\")\nprint(f\"Solar luminosity: L☉ = 3.828e26 W\")\nprint(f\"Earth mass:    M⊕ = 5.9722e24 kg\")\nprint(f\"Earth radius:  R⊕ = 6.3781e6 m\")\nprint(f\"Moon mass:     M☾ = 7.342e22 kg\")\nprint(f\"AU:            1 AU = {const.au:.6e} m\")\n\n# For precise values, use astropy:\n# from astropy import constants as astro_const\n# print(astro_const.M_sun)\n```\n\n## Quick Reference\n\n| Constant | Symbol | Value | scipy.constants |\n|---|---|---|---|\n| Speed of light | c | 2.998×10⁸ m\u002Fs | `const.c` |\n| Planck | h | 6.626×10⁻³⁴ J·s | `const.h` |\n| Boltzmann | k_B | 1.381×10⁻²³ J\u002FK | `const.k` |\n| Gravitational | G | 6.674×10⁻¹¹ m³\u002F(kg·s²) | `const.G` |\n| Elementary charge | e | 1.602×10⁻¹⁹ C | `const.e` |\n| Electron mass | m_e | 9.109×10⁻³¹ kg | `const.m_e` |\n| Proton mass | m_p | 1.673×10⁻²⁷ kg | `const.m_p` |\n| Avogadro | N_A | 6.022×10²³ mol⁻¹ | `const.N_A` |\n| Gas constant | R | 8.314 J\u002F(mol·K) | `const.R` |\n| 1 eV | — | 1.602×10⁻¹⁹ J | `const.eV` |\n| 1 Å | — | 1.0×10⁻¹⁰ m | `const.angstrom` |\n\n## Tips\n\n1. **Always `from scipy import constants as const`** — never type values manually\n2. **Check units**: `const.physical_constants['Bohr radius']` returns (value, unit, uncertainty)\n3. **For astrophysics**: use `astropy.constants` for additional astronomical constants\n4. **For materials**: use `mp-api` to query the Materials Project database\n5. **Cite your source**: \"CODATA 2018 via scipy.constants\" or \"PDG 2024\"\n",{"name":5,"tags":49,"author":51,"license":15,"version":52,"category":26,"description":7,"dependencies":53},[42,31,30,50,29,28],"Database","Synthetic Sciences","1.0.0",[54,55],"scipy>=1.11.0","numpy>=1.24.0","main","4aa39efd1cdab0b4657ecee16c92a92011a5b10f","openscience\u002Fbackend\u002Fcli\u002Fskills\u002Fphysics\u002Fphysics-databases","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub","https:\u002F\u002Fgithub.com\u002Ffadrienne","2026-10-28 00:43:09",{"source":8,"skillName":5,"commands":63,"prompt":73,"zipAvailable":74},[64,67,70],{"tool":65,"command":66},"npx","npx skills add fadrienne\u002FMyGitHub --skill physics-databases",{"tool":68,"command":69},"bunx","bunx skills add fadrienne\u002FMyGitHub --skill physics-databases",{"tool":71,"command":72},"pnpm","pnpm dlx skills add fadrienne\u002FMyGitHub --skill physics-databases","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fphysics\u002Fphysics-databases\u002FSKILL.md",true,[],[77,106,131,165,192,219],{"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":86,"publishedAt":44},"01M7167B28PK20HP7R7ZED2DY0","statistical-power","Sample size calculation, power analysis, and effect size conversion for scientific studies. Use BEFORE data collection (a priori) to determine required N, or after (post-hoc) to compute achieved power. Covers parametric tests (t-test, ANOVA, chi-square, proportions, correlation, regression), simulation-based power for complex\u002Fclustered\u002Fnested designs, and power curves. Use this skill when someone asks 'how many samples do I need', 'what is my power', 'what effect size can I detect', or any variant. Pairs with \u002Fexperimental-design (choose design first, then compute power) and \u002Fstatistical-analysis (analyze data after collection).","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fstatistical-power\u002FSKILL.md",[83],{"categoryNo":84,"categoryName":85,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[87,88,89,38,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105],"power","statistical power","effect","regression","choose","design","uv","when","Bash","collection","analysis","calculation","ANOVA","conversion","analyze","t-test","chi-square","simulation","sample",{"skillNo":107,"name":108,"nameZh":6,"description":109,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":110,"categories":111,"keywords":115,"publishedAt":44},"01M71677EBCM8EPCE9BZCK8505","statistical-analysis","Guided statistical analysis with test selection and reporting. Use when you need help choosing appropriate tests for your data, assumption checking, power analysis, and APA-formatted results. Best for academic research reporting, test selection guidance. For implementing specific models programmatically use statsmodels.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fstatistical-analysis\u002FSKILL.md",[112],{"categoryNo":113,"categoryName":114,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",[97,116,87,117,118,119,94,120,121,43,122,123,41,124,125,126,127,128,129,130],"statistical analysis","academic research","implementing","selection","you","reporting","for","tests","with","test","research","results","statsmodels","models","help",{"skillNo":132,"name":133,"nameZh":6,"description":134,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":135,"categories":136,"keywords":146,"publishedAt":44},"01M71673R93XAHFH81DBNVNG7H","scientific-writing","Core skill for the deep research and writing tool. Write scientific manuscripts in full paragraphs (never bullet points). Use two-stage process with (1) section outlines with key points using research-lookup then (2) convert to flowing prose. IMRAD structure, citations (APA\u002FAMA\u002FVancouver), figures\u002Ftables, reporting guidelines (CONSORT\u002FSTROBE\u002FPRISMA), for research papers and journal submissions.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fscientific-writing\u002FSKILL.md",[137,140,141],{"categoryNo":138,"categoryName":139,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":84,"categoryName":85,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":142,"categoryName":143,"taxonomyType":144,"taxonomyTypeDesc":145},"01JBSEEDCAT000000000000036","内容创作者","PROFESSION","适用职业",[147,148,149,150,151,152,153,154,155,156,95,157,158,159,160,161,162,163,121,164],"writing","scientific","scientific writing","Citations","deep","deep research","never","papers","journal","false","api","prisma","structure","openrouter","Outlines","core skill","read","features",{"skillNo":166,"name":167,"nameZh":6,"description":168,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":169,"categories":170,"keywords":175,"publishedAt":44},"01M7166ZPKXBF4EBPHVTJPQBB1","scientific-visualization","Meta-skill for publication-ready figures. Use when creating journal submission figures requiring multi-panel layouts, significance annotations, error bars, colorblind-safe palettes, and specific journal formatting (Nature, Science, Cell). Orchestrates matplotlib\u002Fseaborn\u002Fplotly with publication styles. For quick exploration use seaborn or plotly directly.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fscientific-visualization\u002FSKILL.md",[171,174],{"categoryNo":172,"categoryName":173,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000013","分析与可视化",{"categoryNo":84,"categoryName":85,"taxonomyType":21,"taxonomyTypeDesc":22},[176,148,177,178,179,180,181,182,183,155,94,184,185,186,187,188,189,190,122,191],"visualization","scientific visualization","error","ready","quick","exploration","meta-skill","Meta","safe","plotly","annotations","formatting","Publication","figures","panel","submission",{"skillNo":193,"name":194,"nameZh":6,"description":195,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":196,"categories":197,"keywords":204,"publishedAt":44},"01M7166VXJWQSSC3RQZP60WBGR","research-code","Debug, profile, review, and restructure Python research and analysis code. Use when analysis scripts crash, produce wrong results, run too slowly, or need a correctness review. Covers: reading tracebacks and diagnosing root causes, hypothesis-driven debugging, reviewing code for common statistical\u002Fnumerical errors, profiling data pipelines, improving reproducibility (seeds, environments, determinism), and structuring analysis scripts for sharing. Distinct from \u002Fexploratory-data-analysis (which understands data) and \u002Fstatistical-analysis (which chooses and runs tests) — this skill focuses on the code itself.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fresearch-code\u002FSKILL.md",[198,201],{"categoryNo":199,"categoryName":200,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000021","资料检索与研究",{"categoryNo":202,"categoryName":203,"taxonomyType":144,"taxonomyTypeDesc":145},"01JBSEEDCAT000000000000030","开发工程师",[205,206,126,207,208,209,210,93,211,94,95,97,212,213,214,215,216,217,163,218],"code","research code","which","memory","data-analysis","errors","debugging","common","review","data pipelines","run","scripts","line","write",{"skillNo":220,"name":221,"nameZh":6,"description":222,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":223,"licenseFileUrl":6,"sourceHtmlUrl":224,"categories":225,"keywords":232,"publishedAt":44},"01M7166CF5HSR7SVZYZFWTDX55","markdown-mermaid-writing","Comprehensive markdown and Mermaid diagram writing skill. Use when creating any scientific document, report, analysis, or visualization. Establishes text-based diagrams as the default documentation standard with full style guides (markdown + mermaid), 24 diagram type references, and 9 document templates.","Apache-2.0","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fmarkdown-mermaid-writing\u002FSKILL.md",[226,229,230,231],{"categoryNo":227,"categoryName":228,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000002","文档处理",{"categoryNo":138,"categoryName":139,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":84,"categoryName":85,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":142,"categoryName":143,"taxonomyType":144,"taxonomyTypeDesc":145},[147,233,234,235,236,237,176,37,238,94,95,97,239,240,241,242,243,244,148,163],"markdown mermaid writing","mermaid","markdown","community","diagrams","default","team","guides","text","documentation","references","diagram"]