[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6M00E7WQ2QC3WXPM36QB9KC":3,"skill-same:01M6M00E7WQ2QC3WXPM36QB9KC":91},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":22,"publishedAt":45,"linkOnly":46,"markdownContent":47,"markdownBody":48,"frontMatter":49,"refName":53,"commitSha":54,"skillDirectory":55,"forksCount":13,"repositoryHtmlUrl":56,"repositoryHomepageUrl":6,"ownerHtmlUrl":57,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":58,"install":59,"resources":72},"01M6M00E7WQ2QC3WXPM36QB9KC","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.","endman100\u002Fskill-router-nexus","endman100","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F31997019?v=4","01M6KPDX17BYDWW4RRB0TNGGMX","01M6KPDX64Y2QBEWVFG5W5PWPF",0,"MIT","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FScience\u002Fbioinformatics-and-omics\u002Fesm\u002FSKILL.md",[17],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000010","后端与接口","SCENARIO","使用场景",[5,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44],"proteins","prediction","sequences","when","generative","sequence","inverse","for","usage","novel","Scalable","protein engineering","design","local","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\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\n",{"name":5,"license":50,"metadata":51,"description":7},"MIT license",{"skill-author":52},"K-Dense Inc.","main","f19b254e8975d0bfddb394686706106930d7964d","Science\u002Fbioinformatics-and-omics\u002Fesm","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus","https:\u002F\u002Fgithub.com\u002Fendman100","2026-11-12 18:13:37",{"source":8,"skillName":5,"commands":60,"prompt":70,"zipAvailable":71},[61,64,67],{"tool":62,"command":63},"npx","npx skills add endman100\u002Fskill-router-nexus --skill esm",{"tool":65,"command":66},"bunx","bunx skills add endman100\u002Fskill-router-nexus --skill esm",{"tool":68,"command":69},"pnpm","pnpm dlx skills add endman100\u002Fskill-router-nexus --skill esm","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FScience\u002Fbioinformatics-and-omics\u002Fesm\u002FSKILL.md",true,[73,79,83,87],{"path":74,"resourceType":75,"resourceTypeDesc":76,"sizeBytes":77,"sourceHtmlUrl":78},"references\u002Fesm-c-api.md","REFERENCE","参考文档",15715,"https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FScience\u002Fbioinformatics-and-omics\u002Fesm\u002Freferences\u002Fesm-c-api.md",{"path":80,"resourceType":75,"resourceTypeDesc":76,"sizeBytes":81,"sourceHtmlUrl":82},"references\u002Fesm3-api.md",11852,"https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FScience\u002Fbioinformatics-and-omics\u002Fesm\u002Freferences\u002Fesm3-api.md",{"path":84,"resourceType":75,"resourceTypeDesc":76,"sizeBytes":85,"sourceHtmlUrl":86},"references\u002Fforge-api.md",17615,"https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FScience\u002Fbioinformatics-and-omics\u002Fesm\u002Freferences\u002Fforge-api.md",{"path":88,"resourceType":75,"resourceTypeDesc":76,"sizeBytes":89,"sourceHtmlUrl":90},"references\u002Fworkflows.md",20667,"https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FScience\u002Fbioinformatics-and-omics\u002Fesm\u002Freferences\u002Fworkflows.md",[92,116,150,172,181,205],{"skillNo":93,"name":94,"nameZh":6,"description":95,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":96,"sourceHtmlUrl":97,"categories":98,"keywords":104,"publishedAt":45},"01M6MPCT5BXVER8HQZTW3QE9AS","create-master","基于佛教经典文献，生成特定高僧大德的 AI 教学角色","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FMetaphysics-and-Spirituality\u002FMaster-skill\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FMetaphysics-and-Spirituality\u002FMaster-skill\u002FSKILL.md",[99],{"categoryNo":100,"categoryName":101,"taxonomyType":102,"taxonomyTypeDesc":103},"01JBSEEDCAT000000000000039","教师与教育","PROFESSION","适用职业",[105,106,107,108,109,110,111,112,113,114,115],"create master","master","create","生成","true","webfetch","edit","write","read","grep","Bash",{"skillNo":117,"name":118,"nameZh":6,"description":119,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":120,"sourceHtmlUrl":121,"categories":122,"keywords":132,"publishedAt":45},"01M6M43AKE95Y64E1JRXEF5QQ3","writing-dna-skill","从至少 20 篇完整文章中蒸馏可复用的写作 DNA，分析语言、文章结构、选题逻辑、素材策略、认知框架和视觉风格，并生成 Writing-DNA.md；按该 DNA 写作时会读取全部蒸馏产物和 5 篇相关原文。用于中英文作者、账号、品牌或出版物的风格分析与一致性写作。 Distill reusable Writing DNA from at least 20 complete articles for Chinese or English authors, publications, brands, and accounts; 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Metaphor-driven process that produces memorable, meaningful names and avoids AI slop.","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fnaming-main\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fnaming-main\u002FSKILL.md",[],[183,189,190,191,192,193,142,194,113,147,195,196,197,198,115,199,200,201,202,26,203,204,160],"repo","websearch","product name","apps","bots","view","brand","pick","gh","projects","open","saas","open source","product","describe","ai slop",{"skillNo":206,"name":207,"nameZh":6,"description":208,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":209,"sourceHtmlUrl":210,"categories":211,"keywords":213,"publishedAt":45},"01M6M3YVXAFAAWJ7F6JMPFREYE","humanizer-zh","去除文本中的 AI 生成痕迹。适用于编辑或审阅文本，使其听起来更自然、更像人类书写。\n基于维基百科的\"AI 写作特征\"综合指南。检测并修复以下模式：夸大的象征意义、\n宣传性语言、以 -ing 结尾的肤浅分析、模糊的归因、破折号过度使用、三段式法则、\nAI 词汇、否定式排比、过多的连接性短语。","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fhumanizer-zh\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fhumanizer-zh\u002FSKILL.md",[212],{"categoryNo":124,"categoryName":125,"taxonomyType":20,"taxonomyTypeDesc":21},[214,215,216,113,112,111,135,108,180,217,160,162],"humanizer","zh","humanizer zh","翻译"]