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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.","GongYuanCaiJi\u002Fdsh-claude-code-templates","GongYuanCaiJi","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F129952120?v=4","01M7AK39Q4W3VPF7C1K8M58BFX","劉昱廷 Yu-Ting Liu","01M7AK39QPS2RAJKRQY0EPQJPZ",0,"MIT","https:\u002F\u002Fgithub.com\u002FGongYuanCaiJi\u002Fdsh-claude-code-templates\u002Fblob\u002Fmain\u002Fskills\u002Fscientific\u002Fesm\u002FSKILL.md",[],[],"2026-11-15 14:19:17",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.\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,"description":7},"main","51493f354fd03e3f941d4df7fd64c0b8c7a2632d","skills\u002Fscientific\u002Fesm","https:\u002F\u002Fgithub.com\u002FGongYuanCaiJi\u002Fdsh-claude-code-templates","https:\u002F\u002Fgithub.com\u002FGongYuanCaiJi","漏洞研究、根因分析、黑箱測試｜做 AI agent 工具｜Vulnerability research, root-cause analysis, AI agent tooling","2026-08-16 02:24:07",{"source":8,"skillName":5,"commands":32,"prompt":42,"zipAvailable":43},[33,36,39],{"tool":34,"command":35},"npx","npx skills add GongYuanCaiJi\u002Fdsh-claude-code-templates --skill esm",{"tool":37,"command":38},"bunx","bunx skills add GongYuanCaiJi\u002Fdsh-claude-code-templates --skill esm",{"tool":40,"command":41},"pnpm","pnpm dlx skills add GongYuanCaiJi\u002Fdsh-claude-code-templates --skill esm","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FGongYuanCaiJi\u002Fdsh-claude-code-templates\u002Fblob\u002Fmain\u002Fskills\u002Fscientific\u002Fesm\u002FSKILL.md",true,[45,51,55,59],{"path":46,"resourceType":47,"resourceTypeDesc":48,"sizeBytes":49,"sourceHtmlUrl":50},"references\u002Fesm-c-api.md","REFERENCE","参考文档",15715,"https:\u002F\u002Fgithub.com\u002FGongYuanCaiJi\u002Fdsh-claude-code-templates\u002Fblob\u002Fmain\u002Fskills\u002Fscientific\u002Fesm\u002Freferences\u002Fesm-c-api.md",{"path":52,"resourceType":47,"resourceTypeDesc":48,"sizeBytes":53,"sourceHtmlUrl":54},"references\u002Fesm3-api.md",11852,"https:\u002F\u002Fgithub.com\u002FGongYuanCaiJi\u002Fdsh-claude-code-templates\u002Fblob\u002Fmain\u002Fskills\u002Fscientific\u002Fesm\u002Freferences\u002Fesm3-api.md",{"path":56,"resourceType":47,"resourceTypeDesc":48,"sizeBytes":57,"sourceHtmlUrl":58},"references\u002Fforge-api.md",17615,"https:\u002F\u002Fgithub.com\u002FGongYuanCaiJi\u002Fdsh-claude-code-templates\u002Fblob\u002Fmain\u002Fskills\u002Fscientific\u002Fesm\u002Freferences\u002Fforge-api.md",{"path":60,"resourceType":47,"resourceTypeDesc":48,"sizeBytes":61,"sourceHtmlUrl":62},"references\u002Fworkflows.md",20667,"https:\u002F\u002Fgithub.com\u002FGongYuanCaiJi\u002Fdsh-claude-code-templates\u002Fblob\u002Fmain\u002Fskills\u002Fscientific\u002Fesm\u002Freferences\u002Fworkflows.md",[64,71,80,87,94,101],{"skillNo":65,"name":66,"nameZh":6,"description":67,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":68,"categories":69,"keywords":70,"publishedAt":19},"01M7ARMYTZ5D4B7N5SKFXV345V","zapier-make-patterns","No-code automation democratizes workflow building. Zapier and Make (formerly Integromat) let non-developers automate business processes without writing code. But no-code doesn't mean no-complexity - these platforms have their own patterns, pitfalls, and breaking points.  This skill covers when to use which platform, how to build reliable automations, and when to graduate to code-based solutions. Key insight: Zapier optimizes for simplicity and integrations (7000+ apps), Make optimizes for power","https:\u002F\u002Fgithub.com\u002FGongYuanCaiJi\u002Fdsh-claude-code-templates\u002Fblob\u002Fmain\u002Fskills\u002Fworkflow-automation\u002Fzapier-make-patterns\u002FSKILL.md",[],[],{"skillNo":72,"name":73,"nameZh":6,"description":74,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":75,"licenseFileUrl":76,"sourceHtmlUrl":77,"categories":78,"keywords":79,"publishedAt":19},"01M7ARMX1BVNEAR98SBANMDF2V","yeet","Use only when the user explicitly asks to stage, commit, push, and open a GitHub pull request in one flow using the GitHub CLI (`gh`).","Apache-2.0","https:\u002F\u002Fgithub.com\u002FGongYuanCaiJi\u002Fdsh-claude-code-templates\u002Fblob\u002Fmain\u002Fskills\u002Fworkflow-automation\u002Fyeet\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002FGongYuanCaiJi\u002Fdsh-claude-code-templates\u002Fblob\u002Fmain\u002Fskills\u002Fworkflow-automation\u002Fyeet\u002FSKILL.md",[],[],{"skillNo":81,"name":82,"nameZh":6,"description":83,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":84,"categories":85,"keywords":86,"publishedAt":19},"01M7ARMPR4RQ417MG1HMPGNQHH","workflow-automation","Workflow automation is the infrastructure that makes AI agents reliable. 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Key insight: The platforms make different tradeoffs. n8n optimizes for accessibility","https:\u002F\u002Fgithub.com\u002FGongYuanCaiJi\u002Fdsh-claude-code-templates\u002Fblob\u002Fmain\u002Fskills\u002Fworkflow-automation\u002Fworkflow-automation\u002FSKILL.md",[],[],{"skillNo":88,"name":89,"nameZh":6,"description":90,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":91,"categories":92,"keywords":93,"publishedAt":19},"01M7ARMHBERDMKGDMV7K6MQ6YP","trigger-dev","Trigger.dev expert for background jobs, AI workflows, and reliable async execution with excellent developer experience and TypeScript-first design. Use when: trigger.dev, trigger dev, background task, ai background job, long running task.","https:\u002F\u002Fgithub.com\u002FGongYuanCaiJi\u002Fdsh-claude-code-templates\u002Fblob\u002Fmain\u002Fskills\u002Fworkflow-automation\u002Ftrigger-dev\u002FSKILL.md",[],[],{"skillNo":95,"name":96,"nameZh":6,"description":97,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":98,"categories":99,"keywords":100,"publishedAt":19},"01M7ARME0A7KFT330Q2H25HE2B","slack-automation","Automate Slack workspace operations including messaging, search, channel management, and reaction workflows through Composio's Slack toolkit.","https:\u002F\u002Fgithub.com\u002FGongYuanCaiJi\u002Fdsh-claude-code-templates\u002Fblob\u002Fmain\u002Fskills\u002Fworkflow-automation\u002Fslack-automation\u002FSKILL.md",[],[],{"skillNo":102,"name":103,"nameZh":6,"description":104,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":105,"categories":106,"keywords":107,"publishedAt":19},"01M7ARM8D87V18WDG0GEZS11ZT","planning-with-files","Transforms workflow to use Manus-style persistent markdown files for planning, progress tracking, and knowledge storage. Use when starting complex tasks, multi-step projects, research tasks, or when the user mentions planning, organizing work, tracking progress, or wants structured output.","https:\u002F\u002Fgithub.com\u002FGongYuanCaiJi\u002Fdsh-claude-code-templates\u002Fblob\u002Fmain\u002Fskills\u002Fworkflow-automation\u002Fplanning-with-files\u002FSKILL.md",[],[]]