[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6EEX2VRNWV3N4911V3F4VC6":3,"skill-same:01M6EEX2VRNWV3N4911V3F4VC6":92},{"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":57,"ownerDescription":58,"updatedAt":59,"install":60,"resources":73},"01M6EEX2VRNWV3N4911V3F4VC6","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.","levalencia\u002Fagent-god-mode","levalencia","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F6962857?v=4","01M6EB0FZZ4BPQPC4HET4QY6NM","Luis Valencia ","01M6EB0G4HWHPWW0H9X7QKSAHP",3,"MIT","https:\u002F\u002Fgithub.com\u002Flevalencia\u002Fagent-god-mode\u002Fblob\u002Fmain\u002Forganized-skills\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],"model","Scalable","protein engineering","proteins","local","across","structure","language","models","use","design","api","forge","engineering","cloud","multimodal","embeddings","toolkit","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":48,"metadata":49,"description":7},"MIT license",{"skill-author":50},"K-Dense Inc.","main","fa638b97a18ff6d9f26cf0eaf3dfa123531bcc87","organized-skills\u002Fesm",1,"https:\u002F\u002Fgithub.com\u002Flevalencia\u002Fagent-god-mode","https:\u002F\u002Fgithub.com\u002Flevalencia","https:\u002F\u002Fmedium.com\u002F@luisevalencia","Microsoft MVP - Artificial Intelligence\r\nMachine Learning Enthusiast\r\n","2026-05-02 06:55:36",{"source":8,"skillName":5,"commands":61,"prompt":71,"zipAvailable":72},[62,65,68],{"tool":63,"command":64},"npx","npx skills add levalencia\u002Fagent-god-mode --skill esm",{"tool":66,"command":67},"bunx","bunx skills add levalencia\u002Fagent-god-mode --skill esm",{"tool":69,"command":70},"pnpm","pnpm dlx skills add levalencia\u002Fagent-god-mode --skill esm","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Flevalencia\u002Fagent-god-mode\u002Fblob\u002Fmain\u002Forganized-skills\u002Fesm\u002FSKILL.md",true,[74,80,84,88],{"path":75,"resourceType":76,"resourceTypeDesc":77,"sizeBytes":78,"sourceHtmlUrl":79},"references\u002Fesm-c-api.md","REFERENCE","参考文档",15715,"https:\u002F\u002Fgithub.com\u002Flevalencia\u002Fagent-god-mode\u002Fblob\u002Fmain\u002Forganized-skills\u002Fesm\u002Freferences\u002Fesm-c-api.md",{"path":81,"resourceType":76,"resourceTypeDesc":77,"sizeBytes":82,"sourceHtmlUrl":83},"references\u002Fesm3-api.md",11852,"https:\u002F\u002Fgithub.com\u002Flevalencia\u002Fagent-god-mode\u002Fblob\u002Fmain\u002Forganized-skills\u002Fesm\u002Freferences\u002Fesm3-api.md",{"path":85,"resourceType":76,"resourceTypeDesc":77,"sizeBytes":86,"sourceHtmlUrl":87},"references\u002Fforge-api.md",17615,"https:\u002F\u002Fgithub.com\u002Flevalencia\u002Fagent-god-mode\u002Fblob\u002Fmain\u002Forganized-skills\u002Fesm\u002Freferences\u002Fforge-api.md",{"path":89,"resourceType":76,"resourceTypeDesc":77,"sizeBytes":90,"sourceHtmlUrl":91},"references\u002Fworkflows.md",20667,"https:\u002F\u002Fgithub.com\u002Flevalencia\u002Fagent-god-mode\u002Fblob\u002Fmain\u002Forganized-skills\u002Fesm\u002Freferences\u002Fworkflows.md",[93,114,146,173,199,226],{"skillNo":94,"name":95,"nameZh":6,"description":96,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":97,"categories":98,"keywords":99,"publishedAt":43},"01M6EM8ES5M9J4S8N78NJST0YC","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\u002Flevalencia\u002Fagent-god-mode\u002Fblob\u002Fmain\u002Forganized-skills\u002Fzarr-python\u002FSKILL.md",[],[100,101,102,103,104,105,38,106,107,108,109,110,111,112,113],"python","zarr python","storage","integration","s3","gcs","dask","parallel","pipelines","arrays","numpy","scientific computing","scale","scientific",{"skillNo":115,"name":116,"nameZh":6,"description":117,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":118,"categories":119,"keywords":129,"publishedAt":43},"01M6EM8AR6ZFKZ9YVW8TTG850Z","zabbix","Zabbix monitoring system automation via API and Python. Use when: (1) Managing hosts, templates, items, triggers, or host groups, (2) Automating monitoring configuration, (3) Sending data via Zabbix trapper\u002Fsender, (4) Querying historical data or events, (5) Bulk operations on Zabbix objects, (6) Maintenance window management, (7) User\u002Fpermission management","https:\u002F\u002Fgithub.com\u002Flevalencia\u002Fagent-god-mode\u002Fblob\u002Fmain\u002Forganized-skills\u002Fzabbix-api-skill\u002FSKILL.md",[120,121,124],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":122,"categoryName":123,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",{"categoryNo":125,"categoryName":126,"taxonomyType":127,"taxonomyTypeDesc":128},"01JBSEEDCAT000000000000030","开发工程师","PROFESSION","适用职业",[116,100,130,131,132,133,134,35,135,136,33,137,138,139,140,141,142,143,144,145],"automation","window management","monitoring","data","templates","system","operations","maintenance","objects","permission","bulk operations","triggers","host","events","via","bulk",{"skillNo":147,"name":148,"nameZh":6,"description":149,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":150,"categories":151,"keywords":152,"publishedAt":43},"01M6EM6Q9JG846ECQY9A210A26","x-twitter-growth","X\u002FTwitter growth engine for building audience, crafting viral content, and analyzing engagement. Use when the user wants to grow on X\u002FTwitter, write tweets or threads, analyze their X profile, research competitors on X, plan a posting strategy, or optimize engagement. Complements social-content (generic multi-platform) with X-specific depth: algorithm mechanics, thread engineering, reply strategy, profile optimization, and competitive intelligence via web search.","https:\u002F\u002Fgithub.com\u002Flevalencia\u002Fagent-god-mode\u002Fblob\u002Fmain\u002Forganized-skills\u002Fx-twitter-growth\u002FSKILL.md",[],[153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172],"twitter","x twitter growth","growth","competitive intelligence","search","analyzing","plan","marketing","web search","competitors","analyze","write","optimize","social","intelligence","viral","growth engine","mechanics","grow","competitive",{"skillNo":174,"name":175,"nameZh":6,"description":176,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":177,"categories":178,"keywords":179,"publishedAt":43},"01M6EM3FQNPYNKPC4CV9TCVNGD","what-if-oracle","Run structured What-If scenario analysis with multi-branch possibility exploration. Use this skill when the user asks speculative questions like \"what if...\", \"what would happen if...\", \"what are the possibilities\", \"explore scenarios\", \"scenario analysis\", \"possibility space\", \"what could go wrong\", \"best case \u002F worst case\", \"risk analysis\", \"contingency planning\", \"strategic options\", or any question about uncertain futures. Also trigger when the user faces a fork-in-the-road decision, wants to stress-test an idea, or needs to think through consequences before committing.","https:\u002F\u002Fgithub.com\u002Flevalencia\u002Fagent-god-mode\u002Fblob\u002Fmain\u002Forganized-skills\u002Fwhat-if-oracle\u002FSKILL.md",[],[180,181,182,183,184,185,186,187,188,189,190,191,33,192,193,194,195,196,197,198],"oracle","what if oracle","test","planning","ux","futures","risk","analysis","explore","decision","branch","question","exploration","options","stress-test","strategies","trigger","case","multi",{"skillNo":200,"name":201,"nameZh":6,"description":202,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":203,"categories":204,"keywords":208,"publishedAt":43},"01M6EM08C06RGDYCJ86HAPK0FF","vercel-react-best-practices","React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React\u002FNext.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.","https:\u002F\u002Fgithub.com\u002Flevalencia\u002Fagent-god-mode\u002Fblob\u002Fmain\u002Forganized-skills\u002Fvercel-react-best-practices\u002FSKILL.md",[205],{"categoryNo":206,"categoryName":207,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000009","前端开发",[209,210,211,212,213,214,215,216,217,218,42,219,220,133,221,222,223,224,225,141],"react","vercel react best practices","best practices","react best practices","vercel","writing","performance","components","react components","guidelines","code","Optimization","Next.js","refactoring","performance optimization","next","pages",{"skillNo":227,"name":228,"nameZh":6,"description":229,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":230,"categories":231,"keywords":242,"publishedAt":43},"01M6EM00S13326QM08DTM94J6Z","venue-templates","Access comprehensive LaTeX templates, formatting requirements, and submission guidelines for major scientific publication venues (Nature, Science, PLOS, IEEE, ACM), academic conferences (NeurIPS, ICML, CVPR, CHI), research posters, and grant proposals (NSF, NIH, DOE, DARPA). This skill should be used when preparing manuscripts for journal submission, conference papers, research posters, or grant proposals and need venue-specific formatting requirements and templates.","https:\u002F\u002Fgithub.com\u002Flevalencia\u002Fagent-god-mode\u002Fblob\u002Fmain\u002Forganized-skills\u002Fvenue-templates\u002FSKILL.md",[232,235,238,239],{"categoryNo":233,"categoryName":234,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":236,"categoryName":237,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000021","资料检索与研究",{"categoryNo":122,"categoryName":123,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":240,"categoryName":241,"taxonomyType":127,"taxonomyTypeDesc":128},"01JBSEEDCAT000000000000036","内容创作者",[134,243,244,218,245,246,247,248,249,250,251,164,252,253,254,255,256,257,113,258],"venue templates","research","NeurIPS","ICML","LaTeX","science","Bash","access","read","edit","proposals","Academic","requirements","formatting","journal","chi"]