[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6EC170P3RG86KNHP3GEEHT5":3,"skill-same:01M6EC170P3RG86KNHP3GEEHT5":53},{"skillNo":4,"name":5,"nameZh":6,"description":6,"descriptionZh":6,"repositoryFullName":7,"ownerLogin":8,"ownerAvatarUrl":9,"ownerAccountNo":10,"ownerDisplayName":11,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":22,"publishedAt":26,"linkOnly":27,"markdownContent":28,"markdownBody":28,"frontMatter":29,"refName":30,"commitSha":31,"skillDirectory":32,"forksCount":33,"repositoryHtmlUrl":34,"repositoryHomepageUrl":35,"ownerHtmlUrl":36,"ownerBlogUrl":6,"ownerDescription":37,"updatedAt":38,"install":39,"resources":52},"01M6EC170P3RG86KNHP3GEEHT5","alphafold-database",null,"ComeOnOliver\u002Fskillshub","ComeOnOliver","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F43215937?v=4","01M6E5JNFECJAAVFE0W7SMWWD2","Haoran Shu","01M6E5JNNDQS7AJZDYFQ8TPZCK",66,"MIT","https:\u002F\u002Fgithub.com\u002FComeOnOliver\u002Fskillshub\u002Fblob\u002Fmain\u002Fskills\u002FK-Dense-AI\u002Fclaude-scientific-skills\u002Falphafold-database\u002FSKILL.md",[17],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000010","后端与接口","SCENARIO","使用场景",[23,24,25],"database","alphafold database","alphafold","2026-11-10 10:12:30",false,"# AlphaFold Database\n\n## Overview\n\nAlphaFold DB is a public repository of AI-predicted 3D protein structures for over 200 million proteins, maintained by DeepMind and EMBL-EBI. Access structure predictions with confidence metrics, download coordinate files, retrieve bulk datasets, and integrate predictions into computational workflows.\n\n## When to Use This Skill\n\nThis skill should be used when working with AI-predicted protein structures in scenarios such as:\n\n- Retrieving protein structure predictions by UniProt ID or protein name\n- Downloading PDB\u002FmmCIF coordinate files for structural analysis\n- Analyzing prediction confidence metrics (pLDDT, PAE) to assess reliability\n- Accessing bulk proteome datasets via Google Cloud Platform\n- Comparing predicted structures with experimental data\n- Performing structure-based drug discovery or protein engineering\n- Building structural models for proteins lacking experimental structures\n- Integrating AlphaFold predictions into computational pipelines\n\n## Core Capabilities\n\n### 1. Searching and Retrieving Predictions\n\n**Using Biopython (Recommended):**\n\nThe Biopython library provides the simplest interface for retrieving AlphaFold structures:\n\n```python\nfrom Bio.PDB import alphafold_db\n\n# Get all predictions for a UniProt accession\npredictions = list(alphafold_db.get_predictions(\"P00520\"))\n\n# Download structure file (mmCIF format)\nfor prediction in predictions:\n    cif_file = alphafold_db.download_cif_for(prediction, directory=\".\u002Fstructures\")\n    print(f\"Downloaded: {cif_file}\")\n\n# Get Structure objects directly\nfrom Bio.PDB import MMCIFParser\nstructures = list(alphafold_db.get_structural_models_for(\"P00520\"))\n```\n\n**Direct API Access:**\n\nQuery predictions using REST endpoints:\n\n```python\nimport requests\n\n# Get prediction metadata for a UniProt accession\nuniprot_id = \"P00520\"\napi_url = f\"https:\u002F\u002Falphafold.ebi.ac.uk\u002Fapi\u002Fprediction\u002F{uniprot_id}\"\nresponse = requests.get(api_url)\nprediction_data = response.json()\n\n# Extract AlphaFold ID\nalphafold_id = prediction_data[0]['entryId']\nprint(f\"AlphaFold ID: {alphafold_id}\")\n```\n\n**Using UniProt to Find Accessions:**\n\nSearch UniProt to find protein accessions first:\n\n```python\nimport urllib.parse, urllib.request\n\ndef get_uniprot_ids(query, query_type='PDB_ID'):\n    \"\"\"Query UniProt to get accession IDs\"\"\"\n    url = 'https:\u002F\u002Fwww.uniprot.org\u002Fuploadlists\u002F'\n    params = {\n        'from': query_type,\n        'to': 'ACC',\n        'format': 'txt',\n        'query': query\n    }\n    data = urllib.parse.urlencode(params).encode('ascii')\n    with urllib.request.urlopen(urllib.request.Request(url, data)) as response:\n        return response.read().decode('utf-8').splitlines()\n\n# Example: Find UniProt IDs for a protein name\nprotein_ids = get_uniprot_ids(\"hemoglobin\", query_type=\"GENE_NAME\")\n```\n\n### 2. Downloading Structure Files\n\nAlphaFold provides multiple file formats for each prediction:\n\n**File Types Available:**\n\n- **Model coordinates** (`model_v4.cif`): Atomic coordinates in mmCIF\u002FPDBx format\n- **Confidence scores** (`confidence_v4.json`): Per-residue pLDDT scores (0-100)\n- **Predicted Aligned Error** (`predicted_aligned_error_v4.json`): PAE matrix for residue pair confidence\n\n**Download URLs:**\n\n```python\nimport requests\n\nalphafold_id = \"AF-P00520-F1\"\nversion = \"v4\"\n\n# Model coordinates (mmCIF)\nmodel_url = f\"https:\u002F\u002Falphafold.ebi.ac.uk\u002Ffiles\u002F{alphafold_id}-model_{version}.cif\"\nresponse = requests.get(model_url)\nwith open(f\"{alphafold_id}.cif\", \"w\") as f:\n    f.write(response.text)\n\n# Confidence scores (JSON)\nconfidence_url = f\"https:\u002F\u002Falphafold.ebi.ac.uk\u002Ffiles\u002F{alphafold_id}-confidence_{version}.json\"\nresponse = requests.get(confidence_url)\nconfidence_data = response.json()\n\n# Predicted Aligned Error (JSON)\npae_url = f\"https:\u002F\u002Falphafold.ebi.ac.uk\u002Ffiles\u002F{alphafold_id}-predicted_aligned_error_{version}.json\"\nresponse = requests.get(pae_url)\npae_data = response.json()\n```\n\n**PDB Format (Alternative):**\n\n```python\n# Download as PDB format instead of mmCIF\npdb_url = f\"https:\u002F\u002Falphafold.ebi.ac.uk\u002Ffiles\u002F{alphafold_id}-model_{version}.pdb\"\nresponse = requests.get(pdb_url)\nwith open(f\"{alphafold_id}.pdb\", \"wb\") as f:\n    f.write(response.content)\n```\n\n### 3. Working with Confidence Metrics\n\nAlphaFold predictions include confidence estimates critical for interpretation:\n\n**pLDDT (per-residue confidence):**\n\n```python\nimport json\nimport requests\n\n# Load confidence scores\nalphafold_id = \"AF-P00520-F1\"\nconfidence_url = f\"https:\u002F\u002Falphafold.ebi.ac.uk\u002Ffiles\u002F{alphafold_id}-confidence_v4.json\"\nconfidence = requests.get(confidence_url).json()\n\n# Extract pLDDT scores\nplddt_scores = confidence['confidenceScore']\n\n# Interpret confidence levels\n# pLDDT > 90: Very high confidence\n# pLDDT 70-90: High confidence\n# pLDDT 50-70: Low confidence\n# pLDDT \u003C 50: Very low confidence\n\nhigh_confidence_residues = [i for i, score in enumerate(plddt_scores) if score > 90]\nprint(f\"High confidence residues: {len(high_confidence_residues)}\u002F{len(plddt_scores)}\")\n```\n\n**PAE (Predicted Aligned Error):**\n\nPAE indicates confidence in relative domain positions:\n\n```python\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# Load PAE matrix\npae_url = f\"https:\u002F\u002Falphafold.ebi.ac.uk\u002Ffiles\u002F{alphafold_id}-predicted_aligned_error_v4.json\"\npae = requests.get(pae_url).json()\n\n# Visualize PAE matrix\npae_matrix = np.array(pae['distance'])\nplt.figure(figsize=(10, 8))\nplt.imshow(pae_matrix, cmap='viridis_r', vmin=0, vmax=30)\nplt.colorbar(label='PAE (Å)')\nplt.title(f'Predicted Aligned Error: {alphafold_id}')\nplt.xlabel('Residue')\nplt.ylabel('Residue')\nplt.savefig(f'{alphafold_id}_pae.png', dpi=300, bbox_inches='tight')\n\n# Low PAE values (\u003C5 Å) indicate confident relative positioning\n# High PAE values (>15 Å) suggest uncertain domain arrangements\n```\n\n### 4. Bulk Data Access via Google Cloud\n\nFor large-scale analyses, use Google Cloud datasets:\n\n**Google Cloud Storage:**\n\n```bash\n# Install gsutil\nuv pip install gsutil\n\n# List available data\ngsutil ls gs:\u002F\u002Fpublic-datasets-deepmind-alphafold-v4\u002F\n\n# Download entire proteomes (by taxonomy ID)\ngsutil -m cp gs:\u002F\u002Fpublic-datasets-deepmind-alphafold-v4\u002Fproteomes\u002Fproteome-tax_id-9606-*.tar .\n\n# Download specific files\ngsutil cp gs:\u002F\u002Fpublic-datasets-deepmind-alphafold-v4\u002Faccession_ids.csv .\n```\n\n**BigQuery Metadata Access:**\n\n```python\nfrom google.cloud import bigquery\n\n# Initialize client\nclient = bigquery.Client()\n\n# Query metadata\nquery = \"\"\"\nSELECT\n  entryId,\n  uniprotAccession,\n  organismScientificName,\n  globalMetricValue,\n  fractionPlddtVeryHigh\nFROM `bigquery-public-data.deepmind_alphafold.metadata`\nWHERE organismScientificName = 'Homo sapiens'\n  AND fractionPlddtVeryHigh > 0.8\nLIMIT 100\n\"\"\"\n\nresults = client.query(query).to_dataframe()\nprint(f\"Found {len(results)} high-confidence human proteins\")\n```\n\n**Download by Species:**\n\n> ⚠️ **Security Note**: The example below uses `shell=True` for simplicity. In production environments, prefer using `subprocess.run()` with a list of arguments to prevent command injection vulnerabilities. See [Python subprocess security](https:\u002F\u002Fdocs.python.org\u002F3\u002Flibrary\u002Fsubprocess.html#security-considerations).\n\n```python\nimport subprocess\nimport shlex\n\ndef download_proteome(taxonomy_id, output_dir=\".\u002Fproteomes\"):\n    \"\"\"Download all AlphaFold predictions for a species\"\"\"\n    # Validate taxonomy_id is an integer to prevent injection\n    if not isinstance(taxonomy_id, int):\n        raise ValueError(\"taxonomy_id must be an integer\")\n    \n    pattern = f\"gs:\u002F\u002Fpublic-datasets-deepmind-alphafold-v4\u002Fproteomes\u002Fproteome-tax_id-{taxonomy_id}-*_v4.tar\"\n    # Use list form instead of shell=True for security\n    subprocess.run([\"gsutil\", \"-m\", \"cp\", pattern, f\"{output_dir}\u002F\"], check=True)\n\n# Download E. coli proteome (tax ID: 83333)\ndownload_proteome(83333)\n\n# Download human proteome (tax ID: 9606)\ndownload_proteome(9606)\n```\n\n### 5. Parsing and Analyzing Structures\n\nWork with downloaded AlphaFold structures using BioPython:\n\n```python\nfrom Bio.PDB import MMCIFParser, PDBIO\nimport numpy as np\n\n# Parse mmCIF file\nparser = MMCIFParser(QUIET=True)\nstructure = parser.get_structure(\"protein\", \"AF-P00520-F1-model_v4.cif\")\n\n# Extract coordinates\ncoords = []\nfor model in structure:\n    for chain in model:\n        for residue in chain:\n            if 'CA' in residue:  # Alpha carbons only\n                coords.append(residue['CA'].get_coord())\n\ncoords = np.array(coords)\nprint(f\"Structure has {len(coords)} residues\")\n\n# Calculate distances\nfrom scipy.spatial.distance import pdist, squareform\ndistance_matrix = squareform(pdist(coords))\n\n# Identify contacts (\u003C 8 Å)\ncontacts = np.where((distance_matrix > 0) & (distance_matrix \u003C 8))\nprint(f\"Number of contacts: {len(contacts[0]) \u002F\u002F 2}\")\n```\n\n**Extract B-factors (pLDDT values):**\n\nAlphaFold stores pLDDT scores in the B-factor column:\n\n```python\nfrom Bio.PDB import MMCIFParser\n\nparser = MMCIFParser(QUIET=True)\nstructure = parser.get_structure(\"protein\", \"AF-P00520-F1-model_v4.cif\")\n\n# Extract pLDDT from B-factors\nplddt_scores = []\nfor model in structure:\n    for chain in model:\n        for residue in chain:\n            if 'CA' in residue:\n                plddt_scores.append(residue['CA'].get_bfactor())\n\n# Identify high-confidence regions\nhigh_conf_regions = [(i, score) for i, score in enumerate(plddt_scores, 1) if score > 90]\nprint(f\"High confidence residues: {len(high_conf_regions)}\")\n```\n\n### 6. Batch Processing Multiple Proteins\n\nProcess multiple predictions efficiently:\n\n```python\nfrom Bio.PDB import alphafold_db\nimport pandas as pd\n\nuniprot_ids = [\"P00520\", \"P12931\", \"P04637\"]  # Multiple proteins\nresults = []\n\nfor uniprot_id in uniprot_ids:\n    try:\n        # Get prediction\n        predictions = list(alphafold_db.get_predictions(uniprot_id))\n\n        if predictions:\n            pred = predictions[0]\n\n            # Download structure\n            cif_file = alphafold_db.download_cif_for(pred, directory=\".\u002Fbatch_structures\")\n\n            # Get confidence data\n            alphafold_id = pred['entryId']\n            conf_url = f\"https:\u002F\u002Falphafold.ebi.ac.uk\u002Ffiles\u002F{alphafold_id}-confidence_v4.json\"\n            conf_data = requests.get(conf_url).json()\n\n            # Calculate statistics\n            plddt_scores = conf_data['confidenceScore']\n            avg_plddt = np.mean(plddt_scores)\n            high_conf_fraction = sum(1 for s in plddt_scores if s > 90) \u002F len(plddt_scores)\n\n            results.append({\n                'uniprot_id': uniprot_id,\n                'alphafold_id': alphafold_id,\n                'avg_plddt': avg_plddt,\n                'high_conf_fraction': high_conf_fraction,\n                'length': len(plddt_scores)\n            })\n    except Exception as e:\n        print(f\"Error processing {uniprot_id}: {e}\")\n\n# Create summary DataFrame\ndf = pd.DataFrame(results)\nprint(df)\n```\n\n## Installation and Setup\n\n### Python Libraries\n\n```bash\n# Install Biopython for structure access\nuv pip install biopython\n\n# Install requests for API access\nuv pip install requests\n\n# For visualization and analysis\nuv pip install numpy matplotlib pandas scipy\n\n# For Google Cloud access (optional)\nuv pip install google-cloud-bigquery gsutil\n```\n\n### 3D-Beacons API Alternative\n\nAlphaFold can also be accessed via the 3D-Beacons federated API:\n\n```python\nimport requests\n\n# Query via 3D-Beacons\nuniprot_id = \"P00520\"\nurl = f\"https:\u002F\u002Fwww.ebi.ac.uk\u002Fpdbe\u002Fpdbe-kb\u002F3dbeacons\u002Fapi\u002Funiprot\u002Fsummary\u002F{uniprot_id}.json\"\nresponse = requests.get(url)\ndata = response.json()\n\n# Filter for AlphaFold structures\naf_structures = [s for s in data['structures'] if s['provider'] == 'AlphaFold DB']\n```\n\n## Common Use Cases\n\n### Structural Proteomics\n- Download complete proteome predictions for analysis\n- Identify high-confidence structural regions across proteins\n- Compare predicted structures with experimental data\n- Build structural models for protein families\n\n### Drug Discovery\n- Retrieve target protein structures for docking studies\n- Analyze binding site conformations\n- Identify druggable pockets in predicted structures\n- Compare structures across homologs\n\n### Protein Engineering\n- Identify stable\u002Funstable regions using pLDDT\n- Design mutations in high-confidence regions\n- Analyze domain architectures using PAE\n- Model protein variants and mutations\n\n### Evolutionary Studies\n- Compare ortholog structures across species\n- Analyze conservation of structural features\n- Study domain evolution patterns\n- Identify functionally important regions\n\n## Key Concepts\n\n**UniProt Accession:** Primary identifier for proteins (e.g., \"P00520\"). Required for querying AlphaFold DB.\n\n**AlphaFold ID:** Internal identifier format: `AF-[UniProt accession]-F[fragment number]` (e.g., \"AF-P00520-F1\").\n\n**pLDDT (predicted Local Distance Difference Test):** Per-residue confidence metric (0-100). Higher values indicate more confident predictions.\n\n**PAE (Predicted Aligned Error):** Matrix indicating confidence in relative positions between residue pairs. Low values (\u003C5 Å) suggest confident relative positioning.\n\n**Database Version:** Current version is v4. File URLs include version suffix (e.g., `model_v4.cif`).\n\n**Fragment Number:** Large proteins may be split into fragments. Fragment number appears in AlphaFold ID (e.g., F1, F2).\n\n## Confidence Interpretation Guidelines\n\n**pLDDT Thresholds:**\n- **>90**: Very high confidence - suitable for detailed analysis\n- **70-90**: High confidence - generally reliable backbone structure\n- **50-70**: Low confidence - use with caution, flexible regions\n- **\u003C50**: Very low confidence - likely disordered or unreliable\n\n**PAE Guidelines:**\n- **\u003C5 Å**: Confident relative positioning of domains\n- **5-10 Å**: Moderate confidence in arrangement\n- **>15 Å**: Uncertain relative positions, domains may be mobile\n\n## Resources\n\n### references\u002Fapi_reference.md\n\nComprehensive API documentation covering:\n- Complete REST API endpoint specifications\n- File format details and data schemas\n- Google Cloud dataset structure and access patterns\n- Advanced query examples and batch processing strategies\n- Rate limiting, caching, and best practices\n- Troubleshooting common issues\n\nConsult this reference for detailed API information, bulk download strategies, or when working with large-scale datasets.\n\n## Important Notes\n\n### Data Usage and Attribution\n\n- AlphaFold DB is freely available under CC-BY-4.0 license\n- Cite: Jumper et al. (2021) Nature and Varadi et al. (2022) Nucleic Acids Research\n- Predictions are computational models, not experimental structures\n- Always assess confidence metrics before downstream analysis\n\n### Version Management\n\n- Current database version: v4 (as of 2024-2025)\n- File URLs include version suffix (e.g., `_v4.cif`)\n- Check for database updates regularly\n- Older versions may be deprecated over time\n\n### Data Quality Considerations\n\n- High pLDDT doesn't guarantee functional accuracy\n- Low confidence regions may be disordered in vivo\n- PAE indicates relative domain confidence, not absolute positioning\n- Predictions lack ligands, post-translational modifications, and cofactors\n- Multi-chain complexes are not predicted (single chains only)\n\n### Performance Tips\n\n- Use Biopython for simple single-protein access\n- Use Google Cloud for bulk downloads (much faster than individual files)\n- Cache downloaded files locally to avoid repeated downloads\n- BigQuery free tier: 1 TB processed data per month\n- Consider network bandwidth for large-scale downloads\n\n## Additional Resources\n\n- **AlphaFold DB Website:** https:\u002F\u002Falphafold.ebi.ac.uk\u002F\n- **API Documentation:** https:\u002F\u002Falphafold.ebi.ac.uk\u002Fapi-docs\n- **Google Cloud Dataset:** https:\u002F\u002Fcloud.google.com\u002Fblog\u002Fproducts\u002Fai-machine-learning\u002Falphafold-protein-structure-database\n- **3D-Beacons API:** https:\u002F\u002Fwww.ebi.ac.uk\u002Fpdbe\u002Fpdbe-kb\u002F3dbeacons\u002F\n- **AlphaFold Papers:**\n  - Nature (2021): https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41586-021-03819-2\n  - Nucleic Acids Research (2024): https:\u002F\u002Fdoi.org\u002F10.1093\u002Fnar\u002Fgkad1011\n- **Biopython Documentation:** https:\u002F\u002Fbiopython.org\u002Fdocs\u002Fdev\u002Fapi\u002FBio.PDB.alphafold_db.html\n- **GitHub Repository:** https:\u002F\u002Fgithub.com\u002Fgoogle-deepmind\u002Falphafold\n",{},"main","def8531e65114c0fca8fb8551c1871ee0eed705c","skills\u002FK-Dense-AI\u002Fclaude-scientific-skills\u002Falphafold-database",24,"https:\u002F\u002Fgithub.com\u002FComeOnOliver\u002Fskillshub","https:\u002F\u002Fskillshub.wtf","https:\u002F\u002Fgithub.com\u002FComeOnOliver","AI & Blockchain Engineer\r\n","2026-06-24 22:07:19",{"source":7,"skillName":5,"commands":40,"prompt":50,"zipAvailable":51},[41,44,47],{"tool":42,"command":43},"npx","npx skills add ComeOnOliver\u002Fskillshub --skill alphafold-database",{"tool":45,"command":46},"bunx","bunx skills add ComeOnOliver\u002Fskillshub --skill alphafold-database",{"tool":48,"command":49},"pnpm","pnpm dlx skills add ComeOnOliver\u002Fskillshub --skill alphafold-database","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FComeOnOliver\u002Fskillshub\u002Fblob\u002Fmain\u002Fskills\u002FK-Dense-AI\u002Fclaude-scientific-skills\u002Falphafold-database\u002FSKILL.md",true,[],[54,66,76,85,91,98],{"skillNo":55,"name":56,"nameZh":6,"description":6,"descriptionZh":6,"repositoryFullName":7,"ownerLogin":8,"ownerAvatarUrl":9,"ownerAccountNo":10,"ownerDisplayName":11,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":57,"categories":58,"keywords":62,"publishedAt":26},"01M6FKJN13GMG4KY57J3QD8GY5","model-hierarchy-skill","https:\u002F\u002Fgithub.com\u002FComeOnOliver\u002Fskillshub\u002Fblob\u002Fmain\u002Fskills\u002Fzscole\u002Fmodel-hierarchy-skill\u002Fmodel-hierarchy-skill\u002FSKILL.md",[59],{"categoryNo":60,"categoryName":61,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000023","Skill与智能体开发",[63,64,65],"model","model hierarchy","model hierarchy skill",{"skillNo":67,"name":68,"nameZh":6,"description":6,"descriptionZh":6,"repositoryFullName":7,"ownerLogin":8,"ownerAvatarUrl":9,"ownerAccountNo":10,"ownerDisplayName":11,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":69,"categories":70,"keywords":72,"publishedAt":26},"01M6FKJJJ6GCAMR4PPPRD12M5X","zoom-mcp","https:\u002F\u002Fgithub.com\u002FComeOnOliver\u002Fskillshub\u002Fblob\u002Fmain\u002Fskills\u002Fzoom\u002Fskills\u002Fzoom-mcp\u002FSKILL.md",[71],{"categoryNo":60,"categoryName":61,"taxonomyType":20,"taxonomyTypeDesc":21},[73,74,75],"MCP","zoom","zoom mcp",{"skillNo":77,"name":78,"nameZh":6,"description":6,"descriptionZh":6,"repositoryFullName":7,"ownerLogin":8,"ownerAvatarUrl":9,"ownerAccountNo":10,"ownerDisplayName":11,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":79,"categories":80,"keywords":81,"publishedAt":26},"01M6FKJE3KYT3KPEZMEEHYKA6K","zoom-apps-sdk","https:\u002F\u002Fgithub.com\u002FComeOnOliver\u002Fskillshub\u002Fblob\u002Fmain\u002Fskills\u002Fzoom\u002Fskills\u002Fzoom-apps-sdk\u002FSKILL.md",[],[82,83,84,74],"sdk","zoom apps sdk","apps",{"skillNo":86,"name":87,"nameZh":6,"description":6,"descriptionZh":6,"repositoryFullName":7,"ownerLogin":8,"ownerAvatarUrl":9,"ownerAccountNo":10,"ownerDisplayName":11,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":88,"categories":89,"keywords":90,"publishedAt":26},"01M6FKJ9EDH9Q5VWDF8HAJRSP0","windows","https:\u002F\u002Fgithub.com\u002FComeOnOliver\u002Fskillshub\u002Fblob\u002Fmain\u002Fskills\u002Fzoom\u002Fskills\u002Fwindows\u002FSKILL.md",[],[87],{"skillNo":92,"name":93,"nameZh":6,"description":6,"descriptionZh":6,"repositoryFullName":7,"ownerLogin":8,"ownerAvatarUrl":9,"ownerAccountNo":10,"ownerDisplayName":11,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":94,"categories":95,"keywords":97,"publishedAt":26},"01M6FKJ666BEEFC3AE9GJ72Q93","whiteboard","https:\u002F\u002Fgithub.com\u002FComeOnOliver\u002Fskillshub\u002Fblob\u002Fmain\u002Fskills\u002Fzoom\u002Fskills\u002Fwhiteboard\u002FSKILL.md",[96],{"categoryNo":60,"categoryName":61,"taxonomyType":20,"taxonomyTypeDesc":21},[93],{"skillNo":99,"name":100,"nameZh":6,"description":6,"descriptionZh":6,"repositoryFullName":7,"ownerLogin":8,"ownerAvatarUrl":9,"ownerAccountNo":10,"ownerDisplayName":11,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":101,"categories":102,"keywords":103,"publishedAt":26},"01M6FKJ21WFCDGTJQ2Q91T9DVW","websockets","https:\u002F\u002Fgithub.com\u002FComeOnOliver\u002Fskillshub\u002Fblob\u002Fmain\u002Fskills\u002Fzoom\u002Fskills\u002Fwebsockets\u002FSKILL.md",[],[100]]