[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M71CFNYECSW9YQWJM43JCAH0":3,"skill-same:01M71CFNYECSW9YQWJM43JCAH0":90},{"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":43,"linkOnly":44,"markdownContent":45,"markdownBody":46,"frontMatter":47,"refName":51,"commitSha":52,"skillDirectory":53,"forksCount":54,"repositoryHtmlUrl":55,"repositoryHomepageUrl":6,"ownerHtmlUrl":56,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":57,"install":58,"resources":71},"01M71CFNYECSW9YQWJM43JCAH0","esm",null,"Comprehensive toolkit for protein language models including ESM3 (generative multimodal protein design across sequence, structure, and function) and ESM C (efficient protein embeddings and representations). Use this skill when working with protein sequences, structures, or function prediction; designing novel proteins; generating protein embeddings; performing inverse folding; or conducting protein engineering tasks. Supports both local model usage and cloud-based Forge API for scalable inference.","strmt7\u002Fai_skills_collection_benchmarked","strmt7","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F28989754?v=4","01M71929P914SWKQ7QJV24NBBY","Stratos","01M71929Q9VGKQ7PWYR95VSQQQ",1,"MIT","https:\u002F\u002Fgithub.com\u002Fstrmt7\u002Fai_skills_collection_benchmarked\u002Fblob\u002Fmain\u002Fincluded\u002Fskills\u002Fby-category\u002Fscience-research-data-analysis\u002Flatest-release-community\u002Fesm\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口","SCENARIO","使用场景",[5,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42],"local","prediction","sequences","when","generative","sequence","inverse","Scalable","protein engineering","proteins","design","across","structure","model","api","forge","multimodal","embeddings","tasks","2026-11-10 10:12:30",false,"---\nname: esm\ndescription: Comprehensive toolkit for protein language models including ESM3 (generative multimodal protein design across sequence, structure, and function) and ESM C (efficient protein embeddings and representations). Use this skill when working with protein sequences, structures, or function prediction; designing novel proteins; generating protein embeddings; performing inverse folding; or conducting protein engineering tasks. Supports both local model usage and cloud-based Forge API for scalable inference.\nlicense: MIT license\nmetadata:\n    skill-author: K-Dense Inc.\n---\n\n# ESM: Evolutionary Scale Modeling\n\n## Overview\n\nESM provides state-of-the-art protein language models for understanding, generating, and designing proteins. This skill enables working with two model families: ESM3 for generative protein design across sequence, structure, and function, and ESM C for efficient protein representation learning and embeddings.\n\n## Core Capabilities\n\n### 1. Protein Sequence Generation with ESM3\n\nGenerate novel protein sequences with desired properties using multimodal generative modeling.\n\n**When to use:**\n- Designing proteins with specific functional properties\n- Completing partial protein sequences\n- Generating variants of existing proteins\n- Creating proteins with desired structural characteristics\n\n**Basic usage:**\n\n```python\nfrom esm.models.esm3 import ESM3\nfrom esm.sdk.api import ESM3InferenceClient, ESMProtein, GenerationConfig\n\n# Load model locally\nmodel: ESM3InferenceClient = ESM3.from_pretrained(\"esm3-sm-open-v1\").to(\"cuda\")\n\n# Create protein prompt\nprotein = ESMProtein(sequence=\"MPRT___KEND\")  # '_' represents masked positions\n\n# Generate completion\nprotein = model.generate(protein, GenerationConfig(track=\"sequence\", num_steps=8))\nprint(protein.sequence)\n```\n\n**For remote\u002Fcloud usage via Forge API:**\n\n```python\nfrom esm.sdk.forge import ESM3ForgeInferenceClient\nfrom esm.sdk.api import ESMProtein, GenerationConfig\n\n# Connect to Forge\nmodel = ESM3ForgeInferenceClient(model=\"esm3-medium-2024-08\", url=\"https:\u002F\u002Fforge.evolutionaryscale.ai\", token=\"\u003Ctoken>\")\n\n# Generate\nprotein = model.generate(protein, GenerationConfig(track=\"sequence\", num_steps=8))\n```\n\nSee `references\u002Fesm3-api.md` for detailed ESM3 model specifications, advanced generation configurations, and multimodal prompting examples.\n\n### 2. Structure Prediction and Inverse Folding\n\nUse ESM3's structure track for structure prediction from sequence or inverse folding (sequence design from structure).\n\n**Structure prediction:**\n\n```python\nfrom esm.sdk.api import ESM3InferenceClient, ESMProtein, GenerationConfig\n\n# Predict structure from sequence\nprotein = ESMProtein(sequence=\"MPRTKEINDAGLIVHSP...\")\nprotein_with_structure = model.generate(\n    protein,\n    GenerationConfig(track=\"structure\", num_steps=protein.sequence.count(\"_\"))\n)\n\n# Access predicted structure\ncoordinates = protein_with_structure.coordinates  # 3D coordinates\npdb_string = protein_with_structure.to_pdb()\n```\n\n**Inverse folding (sequence from structure):**\n\n```python\n# Design sequence for a target structure\nprotein_with_structure = ESMProtein.from_pdb(\"target_structure.pdb\")\nprotein_with_structure.sequence = None  # Remove sequence\n\n# Generate sequence that folds to this structure\ndesigned_protein = model.generate(\n    protein_with_structure,\n    GenerationConfig(track=\"sequence\", num_steps=50, temperature=0.7)\n)\n```\n\n### 3. Protein Embeddings with ESM C\n\nGenerate high-quality embeddings for downstream tasks like function prediction, classification, or similarity analysis.\n\n**When to use:**\n- Extracting protein representations for machine learning\n- Computing sequence similarities\n- Feature extraction for protein classification\n- Transfer learning for protein-related tasks\n\n**Basic usage:**\n\n```python\nfrom esm.models.esmc import ESMC\nfrom esm.sdk.api import ESMProtein\n\n# Load ESM C model\nmodel = ESMC.from_pretrained(\"esmc-300m\").to(\"cuda\")\n\n# Get embeddings\nprotein = ESMProtein(sequence=\"MPRTKEINDAGLIVHSP...\")\nprotein_tensor = model.encode(protein)\n\n# Generate embeddings\nembeddings = model.forward(protein_tensor)\n```\n\n**Batch processing:**\n\n```python\n# Encode multiple proteins\nproteins = [\n    ESMProtein(sequence=\"MPRTKEIND...\"),\n    ESMProtein(sequence=\"AGLIVHSPQ...\"),\n    ESMProtein(sequence=\"KTEFLNDGR...\")\n]\n\nembeddings_list = [model.logits(model.forward(model.encode(p))) for p in proteins]\n```\n\nSee `references\u002Fesm-c-api.md` for ESM C model details, efficiency comparisons, and advanced embedding strategies.\n\n### 4. Function Conditioning and Annotation\n\nUse ESM3's function track to generate proteins with specific functional annotations or predict function from sequence.\n\n**Function-conditioned generation:**\n\n```python\nfrom esm.sdk.api import ESMProtein, FunctionAnnotation, GenerationConfig\n\n# Create protein with desired function\nprotein = ESMProtein(\n    sequence=\"_\" * 200,  # Generate 200 residue protein\n    function_annotations=[\n        FunctionAnnotation(label=\"fluorescent_protein\", start=50, end=150)\n    ]\n)\n\n# Generate sequence with specified function\nfunctional_protein = model.generate(\n    protein,\n    GenerationConfig(track=\"sequence\", num_steps=200)\n)\n```\n\n### 5. Chain-of-Thought Generation\n\nIteratively refine protein designs using ESM3's chain-of-thought generation approach.\n\n```python\nfrom esm.sdk.api import GenerationConfig\n\n# Multi-step refinement\nprotein = ESMProtein(sequence=\"MPRT\" + \"_\" * 100 + \"KEND\")\n\n# Step 1: Generate initial structure\nconfig = GenerationConfig(track=\"structure\", num_steps=50)\nprotein = model.generate(protein, config)\n\n# Step 2: Refine sequence based on structure\nconfig = GenerationConfig(track=\"sequence\", num_steps=50, temperature=0.5)\nprotein = model.generate(protein, config)\n\n# Step 3: Predict function\nconfig = GenerationConfig(track=\"function\", num_steps=20)\nprotein = model.generate(protein, config)\n```\n\n### 6. Batch Processing with Forge API\n\nProcess multiple proteins efficiently using Forge's async executor.\n\n```python\nfrom esm.sdk.forge import ESM3ForgeInferenceClient\nimport asyncio\n\nclient = ESM3ForgeInferenceClient(model=\"esm3-medium-2024-08\", token=\"\u003Ctoken>\")\n\n# Async batch processing\nasync def batch_generate(proteins_list):\n    tasks = [\n        client.async_generate(protein, GenerationConfig(track=\"sequence\"))\n        for protein in proteins_list\n    ]\n    return await asyncio.gather(*tasks)\n\n# Execute\nproteins = [ESMProtein(sequence=f\"MPRT{'_' * 50}KEND\") for _ in range(10)]\nresults = asyncio.run(batch_generate(proteins))\n```\n\nSee `references\u002Fforge-api.md` for detailed Forge API documentation, authentication, rate limits, and batch processing patterns.\n\n## Model Selection Guide\n\n**ESM3 Models (Generative):**\n- `esm3-sm-open-v1` (1.4B) - Open weights, local usage, good for experimentation\n- `esm3-medium-2024-08` (7B) - Best balance of quality and speed (Forge only)\n- `esm3-large-2024-03` (98B) - Highest quality, slower (Forge only)\n\n**ESM C Models (Embeddings):**\n- `esmc-300m` (30 layers) - Lightweight, fast inference\n- `esmc-600m` (36 layers) - Balanced performance\n- `esmc-6b` (80 layers) - Maximum representation quality\n\n**Selection criteria:**\n- **Local development\u002Ftesting:** Use `esm3-sm-open-v1` or `esmc-300m`\n- **Production quality:** Use `esm3-medium-2024-08` via Forge\n- **Maximum accuracy:** Use `esm3-large-2024-03` or `esmc-6b`\n- **High throughput:** Use Forge API with batch executor\n- **Cost optimization:** Use smaller models, implement caching strategies\n\n## Installation\n\n**Basic installation:**\n\n```bash\nuv pip install esm\n```\n\n**With Flash Attention (recommended for faster inference):**\n\n```bash\nuv pip install esm\nuv pip install flash-attn --no-build-isolation\n```\n\n**For Forge API access:**\n\n```bash\nuv pip install esm  # SDK includes Forge client\n```\n\nNo additional dependencies needed. Obtain Forge API token at https:\u002F\u002Fforge.evolutionaryscale.ai\n\n## Common Workflows\n\nFor detailed examples and complete workflows, see `references\u002Fworkflows.md` which includes:\n- Novel GFP design with chain-of-thought\n- Protein variant generation and screening\n- Structure-based sequence optimization\n- Function prediction pipelines\n- Embedding-based clustering and analysis\n\n## References\n\nThis skill includes comprehensive reference documentation:\n\n- `references\u002Fesm3-api.md` - ESM3 model architecture, API reference, generation parameters, and multimodal prompting\n- `references\u002Fesm-c-api.md` - ESM C model details, embedding strategies, and performance optimization\n- `references\u002Fforge-api.md` - Forge platform documentation, authentication, batch processing, and deployment\n- `references\u002Fworkflows.md` - Complete examples and common workflow patterns\n\nThese references contain detailed API specifications, parameter descriptions, and advanced usage patterns. Load them as needed for specific tasks.\n\n## Best Practices\n\n**For generation tasks:**\n- Start with smaller models for prototyping (`esm3-sm-open-v1`)\n- Use temperature parameter to control diversity (0.0 = deterministic, 1.0 = diverse)\n- Implement iterative refinement with chain-of-thought for complex designs\n- Validate generated sequences with structure prediction or wet-lab experiments\n\n**For embedding tasks:**\n- Batch process sequences when possible for efficiency\n- Cache embeddings for repeated analyses\n- Normalize embeddings when computing similarities\n- Use appropriate model size based on downstream task requirements\n\n**For production deployment:**\n- Use Forge API for scalability and latest models\n- Implement error handling and retry logic for API calls\n- Monitor token usage and implement rate limiting\n- Consider AWS SageMaker deployment for dedicated infrastructure\n\n## Resources and Documentation\n\n- **GitHub Repository:** https:\u002F\u002Fgithub.com\u002Fevolutionaryscale\u002Fesm\n- **Forge Platform:** https:\u002F\u002Fforge.evolutionaryscale.ai\n- **Scientific Paper:** Hayes et al., Science (2025) - https:\u002F\u002Fwww.science.org\u002Fdoi\u002F10.1126\u002Fscience.ads0018\n- **Blog Posts:**\n  - ESM3 Release: https:\u002F\u002Fwww.evolutionaryscale.ai\u002Fblog\u002Fesm3-release\n  - ESM C Launch: https:\u002F\u002Fwww.evolutionaryscale.ai\u002Fblog\u002Fesm-cambrian\n- **Community:** Slack community at https:\u002F\u002Fbit.ly\u002F3FKwcWd\n- **Model Weights:** HuggingFace EvolutionaryScale organization\n\n## Responsible Use\n\nESM is designed for beneficial applications in protein engineering, drug discovery, and scientific research. Follow the Responsible Biodesign Framework (https:\u002F\u002Fresponsiblebiodesign.ai\u002F) when designing novel proteins. Consider biosafety and ethical implications of protein designs before experimental validation.\n","# ESM: Evolutionary Scale Modeling\n\n## Overview\n\nESM provides state-of-the-art protein language models for understanding, generating, and designing proteins. This skill enables working with two model families: ESM3 for generative protein design across sequence, structure, and function, and ESM C for efficient protein representation learning and embeddings.\n\n## Core Capabilities\n\n### 1. Protein Sequence Generation with ESM3\n\nGenerate novel protein sequences with desired properties using multimodal generative modeling.\n\n**When to use:**\n- Designing proteins with specific functional properties\n- Completing partial protein sequences\n- Generating variants of existing proteins\n- Creating proteins with desired structural characteristics\n\n**Basic usage:**\n\n```python\nfrom esm.models.esm3 import ESM3\nfrom esm.sdk.api import ESM3InferenceClient, ESMProtein, GenerationConfig\n\n# Load model locally\nmodel: ESM3InferenceClient = ESM3.from_pretrained(\"esm3-sm-open-v1\").to(\"cuda\")\n\n# Create protein prompt\nprotein = ESMProtein(sequence=\"MPRT___KEND\")  # '_' represents masked positions\n\n# Generate completion\nprotein = model.generate(protein, GenerationConfig(track=\"sequence\", num_steps=8))\nprint(protein.sequence)\n```\n\n**For remote\u002Fcloud usage via Forge API:**\n\n```python\nfrom esm.sdk.forge import ESM3ForgeInferenceClient\nfrom esm.sdk.api import ESMProtein, GenerationConfig\n\n# Connect to Forge\nmodel = ESM3ForgeInferenceClient(model=\"esm3-medium-2024-08\", url=\"https:\u002F\u002Fforge.evolutionaryscale.ai\", token=\"\u003Ctoken>\")\n\n# Generate\nprotein = model.generate(protein, GenerationConfig(track=\"sequence\", num_steps=8))\n```\n\nSee `references\u002Fesm3-api.md` for detailed ESM3 model specifications, advanced generation configurations, and multimodal prompting examples.\n\n### 2. Structure Prediction and Inverse Folding\n\nUse ESM3's structure track for structure prediction from sequence or inverse folding (sequence design from structure).\n\n**Structure prediction:**\n\n```python\nfrom esm.sdk.api import ESM3InferenceClient, ESMProtein, GenerationConfig\n\n# Predict structure from sequence\nprotein = ESMProtein(sequence=\"MPRTKEINDAGLIVHSP...\")\nprotein_with_structure = model.generate(\n    protein,\n    GenerationConfig(track=\"structure\", num_steps=protein.sequence.count(\"_\"))\n)\n\n# Access predicted structure\ncoordinates = protein_with_structure.coordinates  # 3D coordinates\npdb_string = protein_with_structure.to_pdb()\n```\n\n**Inverse folding (sequence from structure):**\n\n```python\n# Design sequence for a target structure\nprotein_with_structure = ESMProtein.from_pdb(\"target_structure.pdb\")\nprotein_with_structure.sequence = None  # Remove sequence\n\n# Generate sequence that folds to this structure\ndesigned_protein = model.generate(\n    protein_with_structure,\n    GenerationConfig(track=\"sequence\", num_steps=50, temperature=0.7)\n)\n```\n\n### 3. Protein Embeddings with ESM C\n\nGenerate high-quality embeddings for downstream tasks like function prediction, classification, or similarity analysis.\n\n**When to use:**\n- Extracting protein representations for machine learning\n- Computing sequence similarities\n- Feature extraction for protein classification\n- Transfer learning for protein-related tasks\n\n**Basic usage:**\n\n```python\nfrom esm.models.esmc import ESMC\nfrom esm.sdk.api import ESMProtein\n\n# Load ESM C model\nmodel = ESMC.from_pretrained(\"esmc-300m\").to(\"cuda\")\n\n# Get embeddings\nprotein = ESMProtein(sequence=\"MPRTKEINDAGLIVHSP...\")\nprotein_tensor = model.encode(protein)\n\n# Generate embeddings\nembeddings = model.forward(protein_tensor)\n```\n\n**Batch processing:**\n\n```python\n# Encode multiple proteins\nproteins = [\n    ESMProtein(sequence=\"MPRTKEIND...\"),\n    ESMProtein(sequence=\"AGLIVHSPQ...\"),\n    ESMProtein(sequence=\"KTEFLNDGR...\")\n]\n\nembeddings_list = [model.logits(model.forward(model.encode(p))) for p in proteins]\n```\n\nSee `references\u002Fesm-c-api.md` for ESM C model details, efficiency comparisons, and advanced embedding strategies.\n\n### 4. Function Conditioning and Annotation\n\nUse ESM3's function track to generate proteins with specific functional annotations or predict function from sequence.\n\n**Function-conditioned generation:**\n\n```python\nfrom esm.sdk.api import ESMProtein, FunctionAnnotation, GenerationConfig\n\n# Create protein with desired function\nprotein = ESMProtein(\n    sequence=\"_\" * 200,  # Generate 200 residue protein\n    function_annotations=[\n        FunctionAnnotation(label=\"fluorescent_protein\", start=50, end=150)\n    ]\n)\n\n# Generate sequence with specified function\nfunctional_protein = model.generate(\n    protein,\n    GenerationConfig(track=\"sequence\", num_steps=200)\n)\n```\n\n### 5. Chain-of-Thought Generation\n\nIteratively refine protein designs using ESM3's chain-of-thought generation approach.\n\n```python\nfrom esm.sdk.api import GenerationConfig\n\n# Multi-step refinement\nprotein = ESMProtein(sequence=\"MPRT\" + \"_\" * 100 + \"KEND\")\n\n# Step 1: Generate initial structure\nconfig = GenerationConfig(track=\"structure\", num_steps=50)\nprotein = model.generate(protein, config)\n\n# Step 2: Refine sequence based on structure\nconfig = GenerationConfig(track=\"sequence\", num_steps=50, temperature=0.5)\nprotein = model.generate(protein, config)\n\n# Step 3: Predict function\nconfig = GenerationConfig(track=\"function\", num_steps=20)\nprotein = model.generate(protein, config)\n```\n\n### 6. Batch Processing with Forge API\n\nProcess multiple proteins efficiently using Forge's async executor.\n\n```python\nfrom esm.sdk.forge import ESM3ForgeInferenceClient\nimport asyncio\n\nclient = ESM3ForgeInferenceClient(model=\"esm3-medium-2024-08\", token=\"\u003Ctoken>\")\n\n# Async batch processing\nasync def batch_generate(proteins_list):\n    tasks = [\n        client.async_generate(protein, GenerationConfig(track=\"sequence\"))\n        for protein in proteins_list\n    ]\n    return await asyncio.gather(*tasks)\n\n# Execute\nproteins = [ESMProtein(sequence=f\"MPRT{'_' * 50}KEND\") for _ in range(10)]\nresults = asyncio.run(batch_generate(proteins))\n```\n\nSee `references\u002Fforge-api.md` for detailed Forge API documentation, authentication, rate limits, and batch processing patterns.\n\n## Model Selection Guide\n\n**ESM3 Models (Generative):**\n- `esm3-sm-open-v1` (1.4B) - Open weights, local usage, good for experimentation\n- `esm3-medium-2024-08` (7B) - Best balance of quality and speed (Forge only)\n- `esm3-large-2024-03` (98B) - Highest quality, slower (Forge only)\n\n**ESM C Models (Embeddings):**\n- `esmc-300m` (30 layers) - Lightweight, fast inference\n- `esmc-600m` (36 layers) - Balanced performance\n- `esmc-6b` (80 layers) - Maximum representation quality\n\n**Selection criteria:**\n- **Local development\u002Ftesting:** Use `esm3-sm-open-v1` or `esmc-300m`\n- **Production quality:** Use `esm3-medium-2024-08` via Forge\n- **Maximum accuracy:** Use `esm3-large-2024-03` or `esmc-6b`\n- **High throughput:** Use Forge API with batch executor\n- **Cost optimization:** Use smaller models, implement caching strategies\n\n## Installation\n\n**Basic installation:**\n\n```bash\nuv pip install esm\n```\n\n**With Flash Attention (recommended for faster inference):**\n\n```bash\nuv pip install esm\nuv pip install flash-attn --no-build-isolation\n```\n\n**For Forge API access:**\n\n```bash\nuv pip install esm  # SDK includes Forge client\n```\n\nNo additional dependencies needed. Obtain Forge API token at https:\u002F\u002Fforge.evolutionaryscale.ai\n\n## Common Workflows\n\nFor detailed examples and complete workflows, see `references\u002Fworkflows.md` which includes:\n- Novel GFP design with chain-of-thought\n- Protein variant generation and screening\n- Structure-based sequence optimization\n- Function prediction pipelines\n- Embedding-based clustering and analysis\n\n## References\n\nThis skill includes comprehensive reference documentation:\n\n- `references\u002Fesm3-api.md` - ESM3 model architecture, API reference, generation parameters, and multimodal prompting\n- `references\u002Fesm-c-api.md` - ESM C model details, embedding strategies, and performance optimization\n- `references\u002Fforge-api.md` - Forge platform documentation, authentication, batch processing, and deployment\n- `references\u002Fworkflows.md` - Complete examples and common workflow patterns\n\nThese references contain detailed API specifications, parameter descriptions, and advanced usage patterns. Load them as needed for specific tasks.\n\n## Best Practices\n\n**For generation tasks:**\n- Start with smaller models for prototyping (`esm3-sm-open-v1`)\n- Use temperature parameter to control diversity (0.0 = deterministic, 1.0 = diverse)\n- Implement iterative refinement with chain-of-thought for complex designs\n- Validate generated sequences with structure prediction or wet-lab experiments\n\n**For embedding tasks:**\n- Batch process sequences when possible for efficiency\n- Cache embeddings for repeated analyses\n- Normalize embeddings when computing similarities\n- Use appropriate model size based on downstream task requirements\n\n**For production deployment:**\n- Use Forge API for scalability and latest models\n- Implement error handling and retry logic for API calls\n- Monitor token usage and implement rate limiting\n- Consider AWS SageMaker deployment for dedicated infrastructure\n\n## Resources and Documentation\n\n- **GitHub Repository:** https:\u002F\u002Fgithub.com\u002Fevolutionaryscale\u002Fesm\n- **Forge Platform:** https:\u002F\u002Fforge.evolutionaryscale.ai\n- **Scientific Paper:** Hayes et al., Science (2025) - https:\u002F\u002Fwww.science.org\u002Fdoi\u002F10.1126\u002Fscience.ads0018\n- **Blog Posts:**\n  - ESM3 Release: https:\u002F\u002Fwww.evolutionaryscale.ai\u002Fblog\u002Fesm3-release\n  - ESM C Launch: https:\u002F\u002Fwww.evolutionaryscale.ai\u002Fblog\u002Fesm-cambrian\n- **Community:** Slack community at https:\u002F\u002Fbit.ly\u002F3FKwcWd\n- **Model Weights:** HuggingFace EvolutionaryScale organization\n\n## Responsible Use\n\nESM is designed for beneficial applications in protein engineering, drug discovery, and scientific research. Follow the Responsible Biodesign Framework (https:\u002F\u002Fresponsiblebiodesign.ai\u002F) when designing novel proteins. Consider biosafety and ethical implications of protein designs before experimental validation.\n",{"name":5,"license":48,"metadata":49,"description":7},"MIT license",{"skill-author":50},"K-Dense Inc.","main","5c95bcac4646c5aed06bd9aed5235cfc7985aafc","included\u002Fskills\u002Fby-category\u002Fscience-research-data-analysis\u002Flatest-release-community\u002Fesm",0,"https:\u002F\u002Fgithub.com\u002Fstrmt7\u002Fai_skills_collection_benchmarked","https:\u002F\u002Fgithub.com\u002Fstrmt7","2026-11-11 04:18:12",{"source":8,"skillName":5,"commands":59,"prompt":69,"zipAvailable":70},[60,63,66],{"tool":61,"command":62},"npx","npx skills add strmt7\u002Fai_skills_collection_benchmarked --skill esm",{"tool":64,"command":65},"bunx","bunx skills add strmt7\u002Fai_skills_collection_benchmarked --skill esm",{"tool":67,"command":68},"pnpm","pnpm dlx skills add strmt7\u002Fai_skills_collection_benchmarked --skill esm","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fstrmt7\u002Fai_skills_collection_benchmarked\u002Fblob\u002Fmain\u002Fincluded\u002Fskills\u002Fby-category\u002Fscience-research-data-analysis\u002Flatest-release-community\u002Fesm\u002FSKILL.md",true,[72,78,82,86],{"path":73,"resourceType":74,"resourceTypeDesc":75,"sizeBytes":76,"sourceHtmlUrl":77},"references\u002Fesm-c-api.md","REFERENCE","参考文档",15715,"https:\u002F\u002Fgithub.com\u002Fstrmt7\u002Fai_skills_collection_benchmarked\u002Fblob\u002Fmain\u002Fincluded\u002Fskills\u002Fby-category\u002Fscience-research-data-analysis\u002Flatest-release-community\u002Fesm\u002Freferences\u002Fesm-c-api.md",{"path":79,"resourceType":74,"resourceTypeDesc":75,"sizeBytes":80,"sourceHtmlUrl":81},"references\u002Fesm3-api.md",11852,"https:\u002F\u002Fgithub.com\u002Fstrmt7\u002Fai_skills_collection_benchmarked\u002Fblob\u002Fmain\u002Fincluded\u002Fskills\u002Fby-category\u002Fscience-research-data-analysis\u002Flatest-release-community\u002Fesm\u002Freferences\u002Fesm3-api.md",{"path":83,"resourceType":74,"resourceTypeDesc":75,"sizeBytes":84,"sourceHtmlUrl":85},"references\u002Fforge-api.md",17615,"https:\u002F\u002Fgithub.com\u002Fstrmt7\u002Fai_skills_collection_benchmarked\u002Fblob\u002Fmain\u002Fincluded\u002Fskills\u002Fby-category\u002Fscience-research-data-analysis\u002Flatest-release-community\u002Fesm\u002Freferences\u002Fforge-api.md",{"path":87,"resourceType":74,"resourceTypeDesc":75,"sizeBytes":88,"sourceHtmlUrl":89},"references\u002Fworkflows.md",20667,"https:\u002F\u002Fgithub.com\u002Fstrmt7\u002Fai_skills_collection_benchmarked\u002Fblob\u002Fmain\u002Fincluded\u002Fskills\u002Fby-category\u002Fscience-research-data-analysis\u002Flatest-release-community\u002Fesm\u002Freferences\u002Fworkflows.md",[91,122,151,176,202,229],{"skillNo":92,"name":93,"nameZh":6,"description":94,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":95,"licenseFileUrl":96,"sourceHtmlUrl":97,"categories":98,"keywords":105,"publishedAt":43},"01M71D4H3T6M57DFKN8HENAWRV","webapp-testing","Toolkit for interacting with and testing local web applications using Playwright. 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Use when the user wants to test a website, automate browser interactions, validate web functionality, or perform browser-based testing.","https:\u002F\u002Fgithub.com\u002Fstrmt7\u002Fai_skills_collection_benchmarked\u002Fblob\u002Fmain\u002Fincluded\u002Fskills\u002Fby-category\u002Ftesting-qa-benchmarking\u002Flatest-release-browser-automation\u002Fplaywright-skill\u002FSKILL.md",[157,158],{"categoryNo":100,"categoryName":101,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":130,"categoryName":131,"taxonomyType":21,"taxonomyTypeDesc":22},[117,160,161,162,107,163,164,27,165,115,166,167,168,169,170,171,172,173,174,175],"playwright skill","browser automation","flows","auto","access","Bash","website","ux","login","scripts","read","write","node","install","network","responsive",{"skillNo":177,"name":178,"nameZh":6,"description":179,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":95,"licenseFileUrl":180,"sourceHtmlUrl":181,"categories":182,"keywords":184,"publishedAt":43},"01M71CY25GM939M6RES91QSG3T","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). 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Use this skill when the user asks to build web components, pages, artifacts, posters, or applications (examples include websites, landing pages, dashboards, React components, HTML\u002FCSS layouts, or when styling\u002Fbeautifying any web UI). Generates creative, polished code and UI design that avoids generic AI aesthetics.","https:\u002F\u002Fgithub.com\u002Fstrmt7\u002Fai_skills_collection_benchmarked\u002Fblob\u002Fmain\u002Fincluded\u002Fskills\u002Fby-category\u002Fsearch-retrieval-web-automation\u002Fofficial-reference\u002Ffrontend-design\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fstrmt7\u002Fai_skills_collection_benchmarked\u002Fblob\u002Fmain\u002Fincluded\u002Fskills\u002Fby-category\u002Fsearch-retrieval-web-automation\u002Fofficial-reference\u002Ffrontend-design\u002FSKILL.md",[209,210,213],{"categoryNo":103,"categoryName":104,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":211,"categoryName":212,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000015","视觉设计",{"categoryNo":214,"categoryName":215,"taxonomyType":216,"taxonomyTypeDesc":217},"01JBSEEDCAT000000000000032","设计师","PROFESSION","适用职业",[111,34,219,190,220,221,222,110,223,224,194,27,187,225,196,226,197,227,228,116],"frontend design","ui design","generic","aesthetics","landing","pages","code","web ui","react components","examples",{"skillNo":230,"name":231,"nameZh":6,"description":232,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":233,"categories":234,"keywords":235,"publishedAt":43},"01M71CVZ54WFBTVA7R16PFY1ZE","zarr-python","Chunked N-D arrays for cloud storage. Compressed arrays, parallel I\u002FO, S3\u002FGCS integration, NumPy\u002FDask\u002FXarray compatible, for large-scale scientific computing pipelines.","https:\u002F\u002Fgithub.com\u002Fstrmt7\u002Fai_skills_collection_benchmarked\u002Fblob\u002Fmain\u002Fincluded\u002Fskills\u002Fby-category\u002Fscience-research-data-analysis\u002Flatest-release-community\u002Fzarr-python\u002FSKILL.md",[],[236,237,238,239,240,241,242,243,244,109,245,246,247,248,249,250,251,252],"zarr python","python","zarr","arrays","Cloud Storage","scientific","storage","parallel","dask","numpy","gcs","s3","integration","scientific computing","cloud","scale","pipelines"]