[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6ADXYY9G48J4Q0R5DWVXF4G":3,"skill-same:01M6ADXYY9G48J4Q0R5DWVXF4G":108},{"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":55,"linkOnly":56,"markdownContent":57,"markdownBody":58,"frontMatter":59,"refName":70,"commitSha":71,"skillDirectory":72,"forksCount":73,"repositoryHtmlUrl":74,"repositoryHomepageUrl":75,"ownerHtmlUrl":76,"ownerBlogUrl":77,"ownerDescription":78,"updatedAt":79,"install":80,"resources":93},"01M6ADXYY9G48J4Q0R5DWVXF4G","prompt-engineering-outlines",null,"Guarantee valid JSON\u002FXML\u002Fcode structure during generation, use Pydantic models for type-safe outputs, support local models (Transformers, vLLM), and maximize inference speed with Outlines - dottxt.ai's structured generation library","davila7\u002Fclaude-code-templates","davila7","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F6216945?v=4","01M6ADEWWFPS191NFM0Y1EX1JH","Daniel Avila","01M6ADJHGM78PHQRD3XZTFDVKK",33481,"MIT","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fprompt-engineering-outlines\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54],"prompt engineering","prompt engineering outlines","prompt","engineering","Transformers","outputs","Type Safety","Safety","json","use","models","structure","local","type","for","code structure","safe","Grammar-Based Generation","Local Models","Pydantic","Outlines","vLLM","Structured Generation","library","xml","code","json schema","support","research","valid","inference","2026-11-10 10:12:30",false,"---\nname: prompt-engineering-outlines\ndescription: Guarantee valid JSON\u002FXML\u002Fcode structure during generation, use Pydantic models for type-safe outputs, support local models (Transformers, vLLM), and maximize inference speed with Outlines - dottxt.ai's structured generation library\nversion: 1.0.0\nauthor: Orchestra Research\nlicense: MIT\ntags: [Prompt Engineering, Outlines, Structured Generation, JSON Schema, Pydantic, Local Models, Grammar-Based Generation, vLLM, Transformers, Type Safety]\ndependencies: [outlines, transformers, vllm, pydantic]\n---\n\n# Outlines: Structured Text Generation\n\n## When to Use This Skill\n\nUse Outlines when you need to:\n- **Guarantee valid JSON\u002FXML\u002Fcode** structure during generation\n- **Use Pydantic models** for type-safe outputs\n- **Support local models** (Transformers, llama.cpp, vLLM)\n- **Maximize inference speed** with zero-overhead structured generation\n- **Generate against JSON schemas** automatically\n- **Control token sampling** at the grammar level\n\n**GitHub Stars**: 8,000+ | **From**: dottxt.ai (formerly .txt)\n\n## Installation\n\n```bash\n# Base installation\npip install outlines\n\n# With specific backends\npip install outlines transformers  # Hugging Face models\npip install outlines llama-cpp-python  # llama.cpp\npip install outlines vllm  # vLLM for high-throughput\n```\n\n## Quick Start\n\n### Basic Example: Classification\n\n```python\nimport outlines\nfrom typing import Literal\n\n# Load model\nmodel = outlines.models.transformers(\"microsoft\u002FPhi-3-mini-4k-instruct\")\n\n# Generate with type constraint\nprompt = \"Sentiment of 'This product is amazing!': \"\ngenerator = outlines.generate.choice(model, [\"positive\", \"negative\", \"neutral\"])\nsentiment = generator(prompt)\n\nprint(sentiment)  # \"positive\" (guaranteed one of these)\n```\n\n### With Pydantic Models\n\n```python\nfrom pydantic import BaseModel\nimport outlines\n\nclass User(BaseModel):\n    name: str\n    age: int\n    email: str\n\nmodel = outlines.models.transformers(\"microsoft\u002FPhi-3-mini-4k-instruct\")\n\n# Generate structured output\nprompt = \"Extract user: John Doe, 30 years old, john@example.com\"\ngenerator = outlines.generate.json(model, User)\nuser = generator(prompt)\n\nprint(user.name)   # \"John Doe\"\nprint(user.age)    # 30\nprint(user.email)  # \"john@example.com\"\n```\n\n## Core Concepts\n\n### 1. Constrained Token Sampling\n\nOutlines uses Finite State Machines (FSM) to constrain token generation at the logit level.\n\n**How it works:**\n1. Convert schema (JSON\u002FPydantic\u002Fregex) to context-free grammar (CFG)\n2. Transform CFG into Finite State Machine (FSM)\n3. Filter invalid tokens at each step during generation\n4. Fast-forward when only one valid token exists\n\n**Benefits:**\n- **Zero overhead**: Filtering happens at token level\n- **Speed improvement**: Fast-forward through deterministic paths\n- **Guaranteed validity**: Invalid outputs impossible\n\n```python\nimport outlines\n\n# Pydantic model -> JSON schema -> CFG -> FSM\nclass Person(BaseModel):\n    name: str\n    age: int\n\nmodel = outlines.models.transformers(\"microsoft\u002FPhi-3-mini-4k-instruct\")\n\n# Behind the scenes:\n# 1. Person -> JSON schema\n# 2. JSON schema -> CFG\n# 3. CFG -> FSM\n# 4. FSM filters tokens during generation\n\ngenerator = outlines.generate.json(model, Person)\nresult = generator(\"Generate person: Alice, 25\")\n```\n\n### 2. Structured Generators\n\nOutlines provides specialized generators for different output types.\n\n#### Choice Generator\n\n```python\n# Multiple choice selection\ngenerator = outlines.generate.choice(\n    model,\n    [\"positive\", \"negative\", \"neutral\"]\n)\n\nsentiment = generator(\"Review: This is great!\")\n# Result: One of the three choices\n```\n\n#### JSON Generator\n\n```python\nfrom pydantic import BaseModel\n\nclass Product(BaseModel):\n    name: str\n    price: float\n    in_stock: bool\n\n# Generate valid JSON matching schema\ngenerator = outlines.generate.json(model, Product)\nproduct = generator(\"Extract: iPhone 15, $999, available\")\n\n# Guaranteed valid Product instance\nprint(type(product))  # \u003Cclass '__main__.Product'>\n```\n\n#### Regex Generator\n\n```python\n# Generate text matching regex\ngenerator = outlines.generate.regex(\n    model,\n    r\"[0-9]{3}-[0-9]{3}-[0-9]{4}\"  # Phone number pattern\n)\n\nphone = generator(\"Generate phone number:\")\n# Result: \"555-123-4567\" (guaranteed to match pattern)\n```\n\n#### Integer\u002FFloat Generators\n\n```python\n# Generate specific numeric types\nint_generator = outlines.generate.integer(model)\nage = int_generator(\"Person's age:\")  # Guaranteed integer\n\nfloat_generator = outlines.generate.float(model)\nprice = float_generator(\"Product price:\")  # Guaranteed float\n```\n\n### 3. Model Backends\n\nOutlines supports multiple local and API-based backends.\n\n#### Transformers (Hugging Face)\n\n```python\nimport outlines\n\n# Load from Hugging Face\nmodel = outlines.models.transformers(\n    \"microsoft\u002FPhi-3-mini-4k-instruct\",\n    device=\"cuda\"  # Or \"cpu\"\n)\n\n# Use with any generator\ngenerator = outlines.generate.json(model, YourModel)\n```\n\n#### llama.cpp\n\n```python\n# Load GGUF model\nmodel = outlines.models.llamacpp(\n    \".\u002Fmodels\u002Fllama-3.1-8b-instruct.Q4_K_M.gguf\",\n    n_gpu_layers=35\n)\n\ngenerator = outlines.generate.json(model, YourModel)\n```\n\n#### vLLM (High Throughput)\n\n```python\n# For production deployments\nmodel = outlines.models.vllm(\n    \"meta-llama\u002FLlama-3.1-8B-Instruct\",\n    tensor_parallel_size=2  # Multi-GPU\n)\n\ngenerator = outlines.generate.json(model, YourModel)\n```\n\n#### OpenAI (Limited Support)\n\n```python\n# Basic OpenAI support\nmodel = outlines.models.openai(\n    \"gpt-4o-mini\",\n    api_key=\"your-api-key\"\n)\n\n# Note: Some features limited with API models\ngenerator = outlines.generate.json(model, YourModel)\n```\n\n### 4. Pydantic Integration\n\nOutlines has first-class Pydantic support with automatic schema translation.\n\n#### Basic Models\n\n```python\nfrom pydantic import BaseModel, Field\n\nclass Article(BaseModel):\n    title: str = Field(description=\"Article title\")\n    author: str = Field(description=\"Author name\")\n    word_count: int = Field(description=\"Number of words\", gt=0)\n    tags: list[str] = Field(description=\"List of tags\")\n\nmodel = outlines.models.transformers(\"microsoft\u002FPhi-3-mini-4k-instruct\")\ngenerator = outlines.generate.json(model, Article)\n\narticle = generator(\"Generate article about AI\")\nprint(article.title)\nprint(article.word_count)  # Guaranteed > 0\n```\n\n#### Nested Models\n\n```python\nclass Address(BaseModel):\n    street: str\n    city: str\n    country: str\n\nclass Person(BaseModel):\n    name: str\n    age: int\n    address: Address  # Nested model\n\ngenerator = outlines.generate.json(model, Person)\nperson = generator(\"Generate person in New York\")\n\nprint(person.address.city)  # \"New York\"\n```\n\n#### Enums and Literals\n\n```python\nfrom enum import Enum\nfrom typing import Literal\n\nclass Status(str, Enum):\n    PENDING = \"pending\"\n    APPROVED = \"approved\"\n    REJECTED = \"rejected\"\n\nclass Application(BaseModel):\n    applicant: str\n    status: Status  # Must be one of enum values\n    priority: Literal[\"low\", \"medium\", \"high\"]  # Must be one of literals\n\ngenerator = outlines.generate.json(model, Application)\napp = generator(\"Generate application\")\n\nprint(app.status)  # Status.PENDING (or APPROVED\u002FREJECTED)\n```\n\n## Common Patterns\n\n### Pattern 1: Data Extraction\n\n```python\nfrom pydantic import BaseModel\nimport outlines\n\nclass CompanyInfo(BaseModel):\n    name: str\n    founded_year: int\n    industry: str\n    employees: int\n\nmodel = outlines.models.transformers(\"microsoft\u002FPhi-3-mini-4k-instruct\")\ngenerator = outlines.generate.json(model, CompanyInfo)\n\ntext = \"\"\"\nApple Inc. was founded in 1976 in the technology industry.\nThe company employs approximately 164,000 people worldwide.\n\"\"\"\n\nprompt = f\"Extract company information:\\n{text}\\n\\nCompany:\"\ncompany = generator(prompt)\n\nprint(f\"Name: {company.name}\")\nprint(f\"Founded: {company.founded_year}\")\nprint(f\"Industry: {company.industry}\")\nprint(f\"Employees: {company.employees}\")\n```\n\n### Pattern 2: Classification\n\n```python\nfrom typing import Literal\nimport outlines\n\nmodel = outlines.models.transformers(\"microsoft\u002FPhi-3-mini-4k-instruct\")\n\n# Binary classification\ngenerator = outlines.generate.choice(model, [\"spam\", \"not_spam\"])\nresult = generator(\"Email: Buy now! 50% off!\")\n\n# Multi-class classification\ncategories = [\"technology\", \"business\", \"sports\", \"entertainment\"]\ncategory_gen = outlines.generate.choice(model, categories)\ncategory = category_gen(\"Article: Apple announces new iPhone...\")\n\n# With confidence\nclass Classification(BaseModel):\n    label: Literal[\"positive\", \"negative\", \"neutral\"]\n    confidence: float\n\nclassifier = outlines.generate.json(model, Classification)\nresult = classifier(\"Review: This product is okay, nothing special\")\n```\n\n### Pattern 3: Structured Forms\n\n```python\nclass UserProfile(BaseModel):\n    full_name: str\n    age: int\n    email: str\n    phone: str\n    country: str\n    interests: list[str]\n\nmodel = outlines.models.transformers(\"microsoft\u002FPhi-3-mini-4k-instruct\")\ngenerator = outlines.generate.json(model, UserProfile)\n\nprompt = \"\"\"\nExtract user profile from:\nName: Alice Johnson\nAge: 28\nEmail: alice@example.com\nPhone: 555-0123\nCountry: USA\nInterests: hiking, photography, cooking\n\"\"\"\n\nprofile = generator(prompt)\nprint(profile.full_name)\nprint(profile.interests)  # [\"hiking\", \"photography\", \"cooking\"]\n```\n\n### Pattern 4: Multi-Entity Extraction\n\n```python\nclass Entity(BaseModel):\n    name: str\n    type: Literal[\"PERSON\", \"ORGANIZATION\", \"LOCATION\"]\n\nclass DocumentEntities(BaseModel):\n    entities: list[Entity]\n\nmodel = outlines.models.transformers(\"microsoft\u002FPhi-3-mini-4k-instruct\")\ngenerator = outlines.generate.json(model, DocumentEntities)\n\ntext = \"Tim Cook met with Satya Nadella at Microsoft headquarters in Redmond.\"\nprompt = f\"Extract entities from: {text}\"\n\nresult = generator(prompt)\nfor entity in result.entities:\n    print(f\"{entity.name} ({entity.type})\")\n```\n\n### Pattern 5: Code Generation\n\n```python\nclass PythonFunction(BaseModel):\n    function_name: str\n    parameters: list[str]\n    docstring: str\n    body: str\n\nmodel = outlines.models.transformers(\"microsoft\u002FPhi-3-mini-4k-instruct\")\ngenerator = outlines.generate.json(model, PythonFunction)\n\nprompt = \"Generate a Python function to calculate factorial\"\nfunc = generator(prompt)\n\nprint(f\"def {func.function_name}({', '.join(func.parameters)}):\")\nprint(f'    \"\"\"{func.docstring}\"\"\"')\nprint(f\"    {func.body}\")\n```\n\n### Pattern 6: Batch Processing\n\n```python\ndef batch_extract(texts: list[str], schema: type[BaseModel]):\n    \"\"\"Extract structured data from multiple texts.\"\"\"\n    model = outlines.models.transformers(\"microsoft\u002FPhi-3-mini-4k-instruct\")\n    generator = outlines.generate.json(model, schema)\n\n    results = []\n    for text in texts:\n        result = generator(f\"Extract from: {text}\")\n        results.append(result)\n\n    return results\n\nclass Person(BaseModel):\n    name: str\n    age: int\n\ntexts = [\n    \"John is 30 years old\",\n    \"Alice is 25 years old\",\n    \"Bob is 40 years old\"\n]\n\npeople = batch_extract(texts, Person)\nfor person in people:\n    print(f\"{person.name}: {person.age}\")\n```\n\n## Backend Configuration\n\n### Transformers\n\n```python\nimport outlines\n\n# Basic usage\nmodel = outlines.models.transformers(\"microsoft\u002FPhi-3-mini-4k-instruct\")\n\n# GPU configuration\nmodel = outlines.models.transformers(\n    \"microsoft\u002FPhi-3-mini-4k-instruct\",\n    device=\"cuda\",\n    model_kwargs={\"torch_dtype\": \"float16\"}\n)\n\n# Popular models\nmodel = outlines.models.transformers(\"meta-llama\u002FLlama-3.1-8B-Instruct\")\nmodel = outlines.models.transformers(\"mistralai\u002FMistral-7B-Instruct-v0.3\")\nmodel = outlines.models.transformers(\"Qwen\u002FQwen2.5-7B-Instruct\")\n```\n\n### llama.cpp\n\n```python\n# Load GGUF model\nmodel = outlines.models.llamacpp(\n    \".\u002Fmodels\u002Fllama-3.1-8b.Q4_K_M.gguf\",\n    n_ctx=4096,         # Context window\n    n_gpu_layers=35,    # GPU layers\n    n_threads=8         # CPU threads\n)\n\n# Full GPU offload\nmodel = outlines.models.llamacpp(\n    \".\u002Fmodels\u002Fmodel.gguf\",\n    n_gpu_layers=-1  # All layers on GPU\n)\n```\n\n### vLLM (Production)\n\n```python\n# Single GPU\nmodel = outlines.models.vllm(\"meta-llama\u002FLlama-3.1-8B-Instruct\")\n\n# Multi-GPU\nmodel = outlines.models.vllm(\n    \"meta-llama\u002FLlama-3.1-70B-Instruct\",\n    tensor_parallel_size=4  # 4 GPUs\n)\n\n# With quantization\nmodel = outlines.models.vllm(\n    \"meta-llama\u002FLlama-3.1-8B-Instruct\",\n    quantization=\"awq\"  # Or \"gptq\"\n)\n```\n\n## Best Practices\n\n### 1. Use Specific Types\n\n```python\n# ✅ Good: Specific types\nclass Product(BaseModel):\n    name: str\n    price: float  # Not str\n    quantity: int  # Not str\n    in_stock: bool  # Not str\n\n# ❌ Bad: Everything as string\nclass Product(BaseModel):\n    name: str\n    price: str  # Should be float\n    quantity: str  # Should be int\n```\n\n### 2. Add Constraints\n\n```python\nfrom pydantic import Field\n\n# ✅ Good: With constraints\nclass User(BaseModel):\n    name: str = Field(min_length=1, max_length=100)\n    age: int = Field(ge=0, le=120)\n    email: str = Field(pattern=r\"^[\\w\\.-]+@[\\w\\.-]+\\.\\w+$\")\n\n# ❌ Bad: No constraints\nclass User(BaseModel):\n    name: str\n    age: int\n    email: str\n```\n\n### 3. Use Enums for Categories\n\n```python\n# ✅ Good: Enum for fixed set\nclass Priority(str, Enum):\n    LOW = \"low\"\n    MEDIUM = \"medium\"\n    HIGH = \"high\"\n\nclass Task(BaseModel):\n    title: str\n    priority: Priority\n\n# ❌ Bad: Free-form string\nclass Task(BaseModel):\n    title: str\n    priority: str  # Can be anything\n```\n\n### 4. Provide Context in Prompts\n\n```python\n# ✅ Good: Clear context\nprompt = \"\"\"\nExtract product information from the following text.\nText: iPhone 15 Pro costs $999 and is currently in stock.\nProduct:\n\"\"\"\n\n# ❌ Bad: Minimal context\nprompt = \"iPhone 15 Pro costs $999 and is currently in stock.\"\n```\n\n### 5. Handle Optional Fields\n\n```python\nfrom typing import Optional\n\n# ✅ Good: Optional fields for incomplete data\nclass Article(BaseModel):\n    title: str  # Required\n    author: Optional[str] = None  # Optional\n    date: Optional[str] = None  # Optional\n    tags: list[str] = []  # Default empty list\n\n# Can succeed even if author\u002Fdate missing\n```\n\n## Comparison to Alternatives\n\n| Feature | Outlines | Instructor | Guidance | LMQL |\n|---------|----------|------------|----------|------|\n| Pydantic Support | ✅ Native | ✅ Native | ❌ No | ❌ No |\n| JSON Schema | ✅ Yes | ✅ Yes | ⚠️ Limited | ✅ Yes |\n| Regex Constraints | ✅ Yes | ❌ No | ✅ Yes | ✅ Yes |\n| Local Models | ✅ Full | ⚠️ Limited | ✅ Full | ✅ Full |\n| API Models | ⚠️ Limited | ✅ Full | ✅ Full | ✅ Full |\n| Zero Overhead | ✅ Yes | ❌ No | ⚠️ Partial | ✅ Yes |\n| Automatic Retrying | ❌ No | ✅ Yes | ❌ No | ❌ No |\n| Learning Curve | Low | Low | Low | High |\n\n**When to choose Outlines:**\n- Using local models (Transformers, llama.cpp, vLLM)\n- Need maximum inference speed\n- Want Pydantic model support\n- Require zero-overhead structured generation\n- Control token sampling process\n\n**When to choose alternatives:**\n- Instructor: Need API models with automatic retrying\n- Guidance: Need token healing and complex workflows\n- LMQL: Prefer declarative query syntax\n\n## Performance Characteristics\n\n**Speed:**\n- **Zero overhead**: Structured generation as fast as unconstrained\n- **Fast-forward optimization**: Skips deterministic tokens\n- **1.2-2x faster** than post-generation validation approaches\n\n**Memory:**\n- FSM compiled once per schema (cached)\n- Minimal runtime overhead\n- Efficient with vLLM for high throughput\n\n**Accuracy:**\n- **100% valid outputs** (guaranteed by FSM)\n- No retry loops needed\n- Deterministic token filtering\n\n## Resources\n\n- **Documentation**: https:\u002F\u002Foutlines-dev.github.io\u002Foutlines\n- **GitHub**: https:\u002F\u002Fgithub.com\u002Foutlines-dev\u002Foutlines (8k+ stars)\n- **Discord**: https:\u002F\u002Fdiscord.gg\u002FR9DSu34mGd\n- **Blog**: https:\u002F\u002Fblog.dottxt.co\n\n## See Also\n\n- `references\u002Fjson_generation.md` - Comprehensive JSON and Pydantic patterns\n- `references\u002Fbackends.md` - Backend-specific configuration\n- `references\u002Fexamples.md` - Production-ready examples\n\n\n","# Outlines: Structured Text Generation\n\n## When to Use This Skill\n\nUse Outlines when you need to:\n- **Guarantee valid JSON\u002FXML\u002Fcode** structure during generation\n- **Use Pydantic models** for type-safe outputs\n- **Support local models** (Transformers, llama.cpp, vLLM)\n- **Maximize inference speed** with zero-overhead structured generation\n- **Generate against JSON schemas** automatically\n- **Control token sampling** at the grammar level\n\n**GitHub Stars**: 8,000+ | **From**: dottxt.ai (formerly .txt)\n\n## Installation\n\n```bash\n# Base installation\npip install outlines\n\n# With specific backends\npip install outlines transformers  # Hugging Face models\npip install outlines llama-cpp-python  # llama.cpp\npip install outlines vllm  # vLLM for high-throughput\n```\n\n## Quick Start\n\n### Basic Example: Classification\n\n```python\nimport outlines\nfrom typing import Literal\n\n# Load model\nmodel = outlines.models.transformers(\"microsoft\u002FPhi-3-mini-4k-instruct\")\n\n# Generate with type constraint\nprompt = \"Sentiment of 'This product is amazing!': \"\ngenerator = outlines.generate.choice(model, [\"positive\", \"negative\", \"neutral\"])\nsentiment = generator(prompt)\n\nprint(sentiment)  # \"positive\" (guaranteed one of these)\n```\n\n### With Pydantic Models\n\n```python\nfrom pydantic import BaseModel\nimport outlines\n\nclass User(BaseModel):\n    name: str\n    age: int\n    email: str\n\nmodel = outlines.models.transformers(\"microsoft\u002FPhi-3-mini-4k-instruct\")\n\n# Generate structured output\nprompt = \"Extract user: John Doe, 30 years old, john@example.com\"\ngenerator = outlines.generate.json(model, User)\nuser = generator(prompt)\n\nprint(user.name)   # \"John Doe\"\nprint(user.age)    # 30\nprint(user.email)  # \"john@example.com\"\n```\n\n## Core Concepts\n\n### 1. Constrained Token Sampling\n\nOutlines uses Finite State Machines (FSM) to constrain token generation at the logit level.\n\n**How it works:**\n1. Convert schema (JSON\u002FPydantic\u002Fregex) to context-free grammar (CFG)\n2. Transform CFG into Finite State Machine (FSM)\n3. Filter invalid tokens at each step during generation\n4. Fast-forward when only one valid token exists\n\n**Benefits:**\n- **Zero overhead**: Filtering happens at token level\n- **Speed improvement**: Fast-forward through deterministic paths\n- **Guaranteed validity**: Invalid outputs impossible\n\n```python\nimport outlines\n\n# Pydantic model -> JSON schema -> CFG -> FSM\nclass Person(BaseModel):\n    name: str\n    age: int\n\nmodel = outlines.models.transformers(\"microsoft\u002FPhi-3-mini-4k-instruct\")\n\n# Behind the scenes:\n# 1. Person -> JSON schema\n# 2. JSON schema -> CFG\n# 3. CFG -> FSM\n# 4. FSM filters tokens during generation\n\ngenerator = outlines.generate.json(model, Person)\nresult = generator(\"Generate person: Alice, 25\")\n```\n\n### 2. Structured Generators\n\nOutlines provides specialized generators for different output types.\n\n#### Choice Generator\n\n```python\n# Multiple choice selection\ngenerator = outlines.generate.choice(\n    model,\n    [\"positive\", \"negative\", \"neutral\"]\n)\n\nsentiment = generator(\"Review: This is great!\")\n# Result: One of the three choices\n```\n\n#### JSON Generator\n\n```python\nfrom pydantic import BaseModel\n\nclass Product(BaseModel):\n    name: str\n    price: float\n    in_stock: bool\n\n# Generate valid JSON matching schema\ngenerator = outlines.generate.json(model, Product)\nproduct = generator(\"Extract: iPhone 15, $999, available\")\n\n# Guaranteed valid Product instance\nprint(type(product))  # \u003Cclass '__main__.Product'>\n```\n\n#### Regex Generator\n\n```python\n# Generate text matching regex\ngenerator = outlines.generate.regex(\n    model,\n    r\"[0-9]{3}-[0-9]{3}-[0-9]{4}\"  # Phone number pattern\n)\n\nphone = generator(\"Generate phone number:\")\n# Result: \"555-123-4567\" (guaranteed to match pattern)\n```\n\n#### Integer\u002FFloat Generators\n\n```python\n# Generate specific numeric types\nint_generator = outlines.generate.integer(model)\nage = int_generator(\"Person's age:\")  # Guaranteed integer\n\nfloat_generator = outlines.generate.float(model)\nprice = float_generator(\"Product price:\")  # Guaranteed float\n```\n\n### 3. Model Backends\n\nOutlines supports multiple local and API-based backends.\n\n#### Transformers (Hugging Face)\n\n```python\nimport outlines\n\n# Load from Hugging Face\nmodel = outlines.models.transformers(\n    \"microsoft\u002FPhi-3-mini-4k-instruct\",\n    device=\"cuda\"  # Or \"cpu\"\n)\n\n# Use with any generator\ngenerator = outlines.generate.json(model, YourModel)\n```\n\n#### llama.cpp\n\n```python\n# Load GGUF model\nmodel = outlines.models.llamacpp(\n    \".\u002Fmodels\u002Fllama-3.1-8b-instruct.Q4_K_M.gguf\",\n    n_gpu_layers=35\n)\n\ngenerator = outlines.generate.json(model, YourModel)\n```\n\n#### vLLM (High Throughput)\n\n```python\n# For production deployments\nmodel = outlines.models.vllm(\n    \"meta-llama\u002FLlama-3.1-8B-Instruct\",\n    tensor_parallel_size=2  # Multi-GPU\n)\n\ngenerator = outlines.generate.json(model, YourModel)\n```\n\n#### OpenAI (Limited Support)\n\n```python\n# Basic OpenAI support\nmodel = outlines.models.openai(\n    \"gpt-4o-mini\",\n    api_key=\"your-api-key\"\n)\n\n# Note: Some features limited with API models\ngenerator = outlines.generate.json(model, YourModel)\n```\n\n### 4. Pydantic Integration\n\nOutlines has first-class Pydantic support with automatic schema translation.\n\n#### Basic Models\n\n```python\nfrom pydantic import BaseModel, Field\n\nclass Article(BaseModel):\n    title: str = Field(description=\"Article title\")\n    author: str = Field(description=\"Author name\")\n    word_count: int = Field(description=\"Number of words\", gt=0)\n    tags: list[str] = Field(description=\"List of tags\")\n\nmodel = outlines.models.transformers(\"microsoft\u002FPhi-3-mini-4k-instruct\")\ngenerator = outlines.generate.json(model, Article)\n\narticle = generator(\"Generate article about AI\")\nprint(article.title)\nprint(article.word_count)  # Guaranteed > 0\n```\n\n#### Nested Models\n\n```python\nclass Address(BaseModel):\n    street: str\n    city: str\n    country: str\n\nclass Person(BaseModel):\n    name: str\n    age: int\n    address: Address  # Nested model\n\ngenerator = outlines.generate.json(model, Person)\nperson = generator(\"Generate person in New York\")\n\nprint(person.address.city)  # \"New York\"\n```\n\n#### Enums and Literals\n\n```python\nfrom enum import Enum\nfrom typing import Literal\n\nclass Status(str, Enum):\n    PENDING = \"pending\"\n    APPROVED = \"approved\"\n    REJECTED = \"rejected\"\n\nclass Application(BaseModel):\n    applicant: str\n    status: Status  # Must be one of enum values\n    priority: Literal[\"low\", \"medium\", \"high\"]  # Must be one of literals\n\ngenerator = outlines.generate.json(model, Application)\napp = generator(\"Generate application\")\n\nprint(app.status)  # Status.PENDING (or APPROVED\u002FREJECTED)\n```\n\n## Common Patterns\n\n### Pattern 1: Data Extraction\n\n```python\nfrom pydantic import BaseModel\nimport outlines\n\nclass CompanyInfo(BaseModel):\n    name: str\n    founded_year: int\n    industry: str\n    employees: int\n\nmodel = outlines.models.transformers(\"microsoft\u002FPhi-3-mini-4k-instruct\")\ngenerator = outlines.generate.json(model, CompanyInfo)\n\ntext = \"\"\"\nApple Inc. was founded in 1976 in the technology industry.\nThe company employs approximately 164,000 people worldwide.\n\"\"\"\n\nprompt = f\"Extract company information:\\n{text}\\n\\nCompany:\"\ncompany = generator(prompt)\n\nprint(f\"Name: {company.name}\")\nprint(f\"Founded: {company.founded_year}\")\nprint(f\"Industry: {company.industry}\")\nprint(f\"Employees: {company.employees}\")\n```\n\n### Pattern 2: Classification\n\n```python\nfrom typing import Literal\nimport outlines\n\nmodel = outlines.models.transformers(\"microsoft\u002FPhi-3-mini-4k-instruct\")\n\n# Binary classification\ngenerator = outlines.generate.choice(model, [\"spam\", \"not_spam\"])\nresult = generator(\"Email: Buy now! 50% off!\")\n\n# Multi-class classification\ncategories = [\"technology\", \"business\", \"sports\", \"entertainment\"]\ncategory_gen = outlines.generate.choice(model, categories)\ncategory = category_gen(\"Article: Apple announces new iPhone...\")\n\n# With confidence\nclass Classification(BaseModel):\n    label: Literal[\"positive\", \"negative\", \"neutral\"]\n    confidence: float\n\nclassifier = outlines.generate.json(model, Classification)\nresult = classifier(\"Review: This product is okay, nothing special\")\n```\n\n### Pattern 3: Structured Forms\n\n```python\nclass UserProfile(BaseModel):\n    full_name: str\n    age: int\n    email: str\n    phone: str\n    country: str\n    interests: list[str]\n\nmodel = outlines.models.transformers(\"microsoft\u002FPhi-3-mini-4k-instruct\")\ngenerator = outlines.generate.json(model, UserProfile)\n\nprompt = \"\"\"\nExtract user profile from:\nName: Alice Johnson\nAge: 28\nEmail: alice@example.com\nPhone: 555-0123\nCountry: USA\nInterests: hiking, photography, cooking\n\"\"\"\n\nprofile = generator(prompt)\nprint(profile.full_name)\nprint(profile.interests)  # [\"hiking\", \"photography\", \"cooking\"]\n```\n\n### Pattern 4: Multi-Entity Extraction\n\n```python\nclass Entity(BaseModel):\n    name: str\n    type: Literal[\"PERSON\", \"ORGANIZATION\", \"LOCATION\"]\n\nclass DocumentEntities(BaseModel):\n    entities: list[Entity]\n\nmodel = outlines.models.transformers(\"microsoft\u002FPhi-3-mini-4k-instruct\")\ngenerator = outlines.generate.json(model, DocumentEntities)\n\ntext = \"Tim Cook met with Satya Nadella at Microsoft headquarters in Redmond.\"\nprompt = f\"Extract entities from: {text}\"\n\nresult = generator(prompt)\nfor entity in result.entities:\n    print(f\"{entity.name} ({entity.type})\")\n```\n\n### Pattern 5: Code Generation\n\n```python\nclass PythonFunction(BaseModel):\n    function_name: str\n    parameters: list[str]\n    docstring: str\n    body: str\n\nmodel = outlines.models.transformers(\"microsoft\u002FPhi-3-mini-4k-instruct\")\ngenerator = outlines.generate.json(model, PythonFunction)\n\nprompt = \"Generate a Python function to calculate factorial\"\nfunc = generator(prompt)\n\nprint(f\"def {func.function_name}({', '.join(func.parameters)}):\")\nprint(f'    \"\"\"{func.docstring}\"\"\"')\nprint(f\"    {func.body}\")\n```\n\n### Pattern 6: Batch Processing\n\n```python\ndef batch_extract(texts: list[str], schema: type[BaseModel]):\n    \"\"\"Extract structured data from multiple texts.\"\"\"\n    model = outlines.models.transformers(\"microsoft\u002FPhi-3-mini-4k-instruct\")\n    generator = outlines.generate.json(model, schema)\n\n    results = []\n    for text in texts:\n        result = generator(f\"Extract from: {text}\")\n        results.append(result)\n\n    return results\n\nclass Person(BaseModel):\n    name: str\n    age: int\n\ntexts = [\n    \"John is 30 years old\",\n    \"Alice is 25 years old\",\n    \"Bob is 40 years old\"\n]\n\npeople = batch_extract(texts, Person)\nfor person in people:\n    print(f\"{person.name}: {person.age}\")\n```\n\n## Backend Configuration\n\n### Transformers\n\n```python\nimport outlines\n\n# Basic usage\nmodel = outlines.models.transformers(\"microsoft\u002FPhi-3-mini-4k-instruct\")\n\n# GPU configuration\nmodel = outlines.models.transformers(\n    \"microsoft\u002FPhi-3-mini-4k-instruct\",\n    device=\"cuda\",\n    model_kwargs={\"torch_dtype\": \"float16\"}\n)\n\n# Popular models\nmodel = outlines.models.transformers(\"meta-llama\u002FLlama-3.1-8B-Instruct\")\nmodel = outlines.models.transformers(\"mistralai\u002FMistral-7B-Instruct-v0.3\")\nmodel = outlines.models.transformers(\"Qwen\u002FQwen2.5-7B-Instruct\")\n```\n\n### llama.cpp\n\n```python\n# Load GGUF model\nmodel = outlines.models.llamacpp(\n    \".\u002Fmodels\u002Fllama-3.1-8b.Q4_K_M.gguf\",\n    n_ctx=4096,         # Context window\n    n_gpu_layers=35,    # GPU layers\n    n_threads=8         # CPU threads\n)\n\n# Full GPU offload\nmodel = outlines.models.llamacpp(\n    \".\u002Fmodels\u002Fmodel.gguf\",\n    n_gpu_layers=-1  # All layers on GPU\n)\n```\n\n### vLLM (Production)\n\n```python\n# Single GPU\nmodel = outlines.models.vllm(\"meta-llama\u002FLlama-3.1-8B-Instruct\")\n\n# Multi-GPU\nmodel = outlines.models.vllm(\n    \"meta-llama\u002FLlama-3.1-70B-Instruct\",\n    tensor_parallel_size=4  # 4 GPUs\n)\n\n# With quantization\nmodel = outlines.models.vllm(\n    \"meta-llama\u002FLlama-3.1-8B-Instruct\",\n    quantization=\"awq\"  # Or \"gptq\"\n)\n```\n\n## Best Practices\n\n### 1. Use Specific Types\n\n```python\n# ✅ Good: Specific types\nclass Product(BaseModel):\n    name: str\n    price: float  # Not str\n    quantity: int  # Not str\n    in_stock: bool  # Not str\n\n# ❌ Bad: Everything as string\nclass Product(BaseModel):\n    name: str\n    price: str  # Should be float\n    quantity: str  # Should be int\n```\n\n### 2. Add Constraints\n\n```python\nfrom pydantic import Field\n\n# ✅ Good: With constraints\nclass User(BaseModel):\n    name: str = Field(min_length=1, max_length=100)\n    age: int = Field(ge=0, le=120)\n    email: str = Field(pattern=r\"^[\\w\\.-]+@[\\w\\.-]+\\.\\w+$\")\n\n# ❌ Bad: No constraints\nclass User(BaseModel):\n    name: str\n    age: int\n    email: str\n```\n\n### 3. Use Enums for Categories\n\n```python\n# ✅ Good: Enum for fixed set\nclass Priority(str, Enum):\n    LOW = \"low\"\n    MEDIUM = \"medium\"\n    HIGH = \"high\"\n\nclass Task(BaseModel):\n    title: str\n    priority: Priority\n\n# ❌ Bad: Free-form string\nclass Task(BaseModel):\n    title: str\n    priority: str  # Can be anything\n```\n\n### 4. Provide Context in Prompts\n\n```python\n# ✅ Good: Clear context\nprompt = \"\"\"\nExtract product information from the following text.\nText: iPhone 15 Pro costs $999 and is currently in stock.\nProduct:\n\"\"\"\n\n# ❌ Bad: Minimal context\nprompt = \"iPhone 15 Pro costs $999 and is currently in stock.\"\n```\n\n### 5. Handle Optional Fields\n\n```python\nfrom typing import Optional\n\n# ✅ Good: Optional fields for incomplete data\nclass Article(BaseModel):\n    title: str  # Required\n    author: Optional[str] = None  # Optional\n    date: Optional[str] = None  # Optional\n    tags: list[str] = []  # Default empty list\n\n# Can succeed even if author\u002Fdate missing\n```\n\n## Comparison to Alternatives\n\n| Feature | Outlines | Instructor | Guidance | LMQL |\n|---------|----------|------------|----------|------|\n| Pydantic Support | ✅ Native | ✅ Native | ❌ No | ❌ No |\n| JSON Schema | ✅ Yes | ✅ Yes | ⚠️ Limited | ✅ Yes |\n| Regex Constraints | ✅ Yes | ❌ No | ✅ Yes | ✅ Yes |\n| Local Models | ✅ Full | ⚠️ Limited | ✅ Full | ✅ Full |\n| API Models | ⚠️ Limited | ✅ Full | ✅ Full | ✅ Full |\n| Zero Overhead | ✅ Yes | ❌ No | ⚠️ Partial | ✅ Yes |\n| Automatic Retrying | ❌ No | ✅ Yes | ❌ No | ❌ No |\n| Learning Curve | Low | Low | Low | High |\n\n**When to choose Outlines:**\n- Using local models (Transformers, llama.cpp, vLLM)\n- Need maximum inference speed\n- Want Pydantic model support\n- Require zero-overhead structured generation\n- Control token sampling process\n\n**When to choose alternatives:**\n- Instructor: Need API models with automatic retrying\n- Guidance: Need token healing and complex workflows\n- LMQL: Prefer declarative query syntax\n\n## Performance Characteristics\n\n**Speed:**\n- **Zero overhead**: Structured generation as fast as unconstrained\n- **Fast-forward optimization**: Skips deterministic tokens\n- **1.2-2x faster** than post-generation validation approaches\n\n**Memory:**\n- FSM compiled once per schema (cached)\n- Minimal runtime overhead\n- Efficient with vLLM for high throughput\n\n**Accuracy:**\n- **100% valid outputs** (guaranteed by FSM)\n- No retry loops needed\n- Deterministic token filtering\n\n## Resources\n\n- **Documentation**: https:\u002F\u002Foutlines-dev.github.io\u002Foutlines\n- **GitHub**: https:\u002F\u002Fgithub.com\u002Foutlines-dev\u002Foutlines (8k+ stars)\n- **Discord**: https:\u002F\u002Fdiscord.gg\u002FR9DSu34mGd\n- **Blog**: https:\u002F\u002Fblog.dottxt.co\n\n## See Also\n\n- `references\u002Fjson_generation.md` - Comprehensive JSON and Pydantic patterns\n- `references\u002Fbackends.md` - Backend-specific configuration\n- `references\u002Fexamples.md` - Production-ready examples\n\n\n",{"name":5,"tags":60,"author":63,"license":15,"version":64,"description":7,"dependencies":65},[61,44,46,62,43,42,41,45,28,30],"Prompt Engineering","JSON Schema","Orchestra Research","1.0.0",[66,67,68,69],"outlines","transformers","vllm","pydantic","main","2b749c82b5d11fac39d9a6e021ce3f5b8a541bd1","cli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fprompt-engineering-outlines",3931,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates","https:\u002F\u002Faitmpl.com","https:\u002F\u002Fgithub.com\u002Fdavila7","https:\u002F\u002Fdanielavila.me","Building AI dev tools with LLMs","2026-11-14 06:14:46",{"source":8,"skillName":5,"commands":81,"prompt":91,"zipAvailable":92},[82,85,88],{"tool":83,"command":84},"npx","npx skills add davila7\u002Fclaude-code-templates --skill prompt-engineering-outlines",{"tool":86,"command":87},"bunx","bunx skills add davila7\u002Fclaude-code-templates --skill prompt-engineering-outlines",{"tool":89,"command":90},"pnpm","pnpm dlx skills add davila7\u002Fclaude-code-templates --skill prompt-engineering-outlines","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fprompt-engineering-outlines\u002FSKILL.md",true,[94,100,104],{"path":95,"resourceType":96,"resourceTypeDesc":97,"sizeBytes":98,"sourceHtmlUrl":99},"references\u002Fbackends.md","REFERENCE","参考文档",14407,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fprompt-engineering-outlines\u002Freferences\u002Fbackends.md",{"path":101,"resourceType":96,"resourceTypeDesc":97,"sizeBytes":102,"sourceHtmlUrl":103},"references\u002Fexamples.md",21231,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fprompt-engineering-outlines\u002Freferences\u002Fexamples.md",{"path":105,"resourceType":96,"resourceTypeDesc":97,"sizeBytes":106,"sourceHtmlUrl":107},"references\u002Fjson_generation.md",14927,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fprompt-engineering-outlines\u002Freferences\u002Fjson_generation.md",[109,135,161,194,229,252],{"skillNo":110,"name":111,"nameZh":6,"description":112,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":113,"categories":114,"keywords":116,"publishedAt":55},"01M70NHBTQ8BV0A2SCAZXEPHCX","wealthville-pool-scoring","Score DeFi liquidity pools before providing liquidity. Returns a 0-100 pool score and an Enter\u002FHold\u002FExit\u002FReduce\u002FAvoid verdict with calibrated confidence for ~68,800 Solana pools and 575 EVM pools, plus a public miss-inclusive track record. Use when asked whether a pool is worth LPing into, which pools look best right now, or how accurate the scores have actually been.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fweb-data\u002Fwealthville-pool-scoring\u002FSKILL.md",[115],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[117,118,119,120,121,38,122,123,33,124,125,126,127,128,129,130,131,132,133,134],"scoring","pool","wealthville pool scoring","liquidity","which","exit","now","when","reduce","with","record","track","verdict","evm","solana","how","public","look",{"skillNo":136,"name":137,"nameZh":6,"description":138,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":139,"categories":140,"keywords":142,"publishedAt":55},"01M70MB6VR8ERJ4EM8D5KHBMEV","technical-debt-auditor","Scans a codebase and produces a quantified technical debt report with a prioritized remediation plan. Analyzes dead code, TODO\u002FFIXME\u002FHACK markers, dependency staleness, complexity hotspots, code duplication signals, test coverage gaps, unused exports, and outdated patterns. Use this skill when the user asks to audit their codebase for tech debt, wants a health check of their code quality, says things like \"where should I focus refactoring\", \"find code smells\", \"audit my codebase\", \"what technical debt do we have\", \"prioritize cleanup work\", or asks about code quality metrics. Also trigger when the user mentions \"code rot\", \"legacy cleanup\", \"refactoring priorities\", or wants to understand the overall health of a project before a major effort. Trigger even if the user doesn't say \"technical debt\" explicitly — any request to systematically assess code quality and identify improvement priorities belongs here.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdevelopment\u002Ftechnical-debt-auditor\u002FSKILL.md",[141],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[143,144,145,146,147,148,149,150,151,152,153,124,49,154,155,156,157,158,159,160],"technical debt","debt","technical","technical debt auditor","auditor","coverage","complexity","markers","project","cleanup","unused","todo","work","plan","focus","signals","audit","dependency",{"skillNo":162,"name":163,"nameZh":6,"description":164,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":165,"categories":166,"keywords":176,"publishedAt":55},"01M70KPYH9QNHCNME5GJQGE22K","readonly-sql-guard","Make an agent's database access provably read-only. Use when an agent runs caller-supplied SQL against a production\u002FERP\u002Fanalytics database (a \"query\" MCP tool, text-to-SQL, an admin console), or when reviewing DB-access code for write\u002FDDL\u002Fside-effect\u002Ftransaction-escape risks. Checks what a statement CALLS, not just its shape.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdatabase\u002Freadonly-sql-guard\u002FSKILL.md",[167,170,171],{"categoryNo":168,"categoryName":169,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":172,"categoryName":173,"taxonomyType":174,"taxonomyTypeDesc":175},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[177,178,179,180,181,182,183,38,184,185,186,187,188,189,190,33,191,192,124,193],"readonly","guard","sql guard","sql","readonly sql guard","erp","effect","read-only","db","just","tool","calls","only","MCP","access","database","query",{"skillNo":195,"name":196,"nameZh":6,"description":197,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":198,"licenseFileUrl":199,"sourceHtmlUrl":200,"categories":201,"keywords":205,"publishedAt":55},"01M6AFY6F7CC2BBZP1QD6VTNVC","web-artifacts-builder","Suite of tools for creating elaborate, multi-component claude.ai HTML artifacts using modern frontend web technologies (React, Tailwind CSS, shadcn\u002Fui). Use for complex artifacts requiring state management, routing, or shadcn\u002Fui components - not for simple single-file HTML\u002FJSX artifacts.","Apache-2.0","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FSKILL.md",[202],{"categoryNo":203,"categoryName":204,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000009","前端开发",[206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,38,228],"artifacts builder","web","web artifacts builder","artifacts","builder","state","routing","state management","component","css","frontend","ui","Tools","file","components","shadcn","react","Tailwind","suite","jsx","html","single-file","ui components",{"skillNo":230,"name":231,"nameZh":6,"description":232,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":198,"licenseFileUrl":233,"sourceHtmlUrl":234,"categories":235,"keywords":240,"publishedAt":55},"01M6AFWF33SFV9Z3SF4PEEE4BF","webapp-testing","Toolkit for interacting with and testing local web applications using Playwright. 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