[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6ADXW0K0KMVCS3VHCTTDGMB":3,"skill-same:01M6ADXW0K0KMVCS3VHCTTDGMB":131},{"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":28,"publishedAt":51,"linkOnly":52,"markdownContent":53,"markdownBody":54,"frontMatter":55,"refName":73,"commitSha":74,"skillDirectory":75,"forksCount":76,"repositoryHtmlUrl":77,"repositoryHomepageUrl":78,"ownerHtmlUrl":79,"ownerBlogUrl":80,"ownerDescription":81,"updatedAt":82,"install":83,"resources":96},"01M6ADXW0K0KMVCS3VHCTTDGMB","prompt-engineering-instructor",null,"Extract structured data from LLM responses with Pydantic validation, retry failed extractions automatically, parse complex JSON with type safety, and stream partial results with Instructor - battle-tested structured output library","davila7\u002Fclaude-code-templates","davila7","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F6216945?v=4","01M6ADEWWFPS191NFM0Y1EX1JH","Daniel Avila","01M6ADJHGM78PHQRD3XZTFDVKK",33418,"MIT","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fprompt-engineering-instructor\u002FSKILL.md",[18,23],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":26,"taxonomyTypeDesc":27},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],"prompt engineering","instructor","engineering","prompt engineering instructor","prompt","parse","structured data","stream","retry","structured output","type","results","json","Safety","Pydantic","data","library","extraction","streaming","openai","validation","research","2026-11-10 10:12:30",false,"---\nname: prompt-engineering-instructor\ndescription: Extract structured data from LLM responses with Pydantic validation, retry failed extractions automatically, parse complex JSON with type safety, and stream partial results with Instructor - battle-tested structured output library\nlicense: MIT\nmetadata:\n  version: 1.0.0\n  author: Orchestra Research\n  tags: [Prompt Engineering, Instructor, Structured Output, Pydantic, Data Extraction, JSON Parsing, Type Safety, Validation, Streaming, OpenAI, Anthropic]\n  dependencies: [instructor, pydantic, openai, anthropic]\n---\n\n# Instructor: Structured LLM Outputs\n\n## When to Use This Skill\n\nUse Instructor when you need to:\n- **Extract structured data** from LLM responses reliably\n- **Validate outputs** against Pydantic schemas automatically\n- **Retry failed extractions** with automatic error handling\n- **Parse complex JSON** with type safety and validation\n- **Stream partial results** for real-time processing\n- **Support multiple LLM providers** with consistent API\n\n**GitHub Stars**: 15,000+ | **Battle-tested**: 100,000+ developers\n\n## Installation\n\n```bash\n# Base installation\npip install instructor\n\n# With specific providers\npip install \"instructor[anthropic]\"  # Anthropic Claude\npip install \"instructor[openai]\"     # OpenAI\npip install \"instructor[all]\"        # All providers\n```\n\n## Quick Start\n\n### Basic Example: Extract User Data\n\n```python\nimport instructor\nfrom pydantic import BaseModel\nfrom anthropic import Anthropic\n\n# Define output structure\nclass User(BaseModel):\n    name: str\n    age: int\n    email: str\n\n# Create instructor client\nclient = instructor.from_anthropic(Anthropic())\n\n# Extract structured data\nuser = client.messages.create(\n    model=\"claude-sonnet-4-5-20250929\",\n    max_tokens=1024,\n    messages=[{\n        \"role\": \"user\",\n        \"content\": \"John Doe is 30 years old. His email is john@example.com\"\n    }],\n    response_model=User\n)\n\nprint(user.name)   # \"John Doe\"\nprint(user.age)    # 30\nprint(user.email)  # \"john@example.com\"\n```\n\n### With OpenAI\n\n```python\nfrom openai import OpenAI\n\nclient = instructor.from_openai(OpenAI())\n\nuser = client.chat.completions.create(\n    model=\"gpt-4o-mini\",\n    response_model=User,\n    messages=[{\"role\": \"user\", \"content\": \"Extract: Alice, 25, alice@email.com\"}]\n)\n```\n\n## Core Concepts\n\n### 1. Response Models (Pydantic)\n\nResponse models define the structure and validation rules for LLM outputs.\n\n#### Basic Model\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 relevant tags\")\n\narticle = client.messages.create(\n    model=\"claude-sonnet-4-5-20250929\",\n    max_tokens=1024,\n    messages=[{\n        \"role\": \"user\",\n        \"content\": \"Analyze this article: [article text]\"\n    }],\n    response_model=Article\n)\n```\n\n**Benefits:**\n- Type safety with Python type hints\n- Automatic validation (word_count > 0)\n- Self-documenting with Field descriptions\n- IDE autocomplete support\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\nperson = client.messages.create(\n    model=\"claude-sonnet-4-5-20250929\",\n    max_tokens=1024,\n    messages=[{\n        \"role\": \"user\",\n        \"content\": \"John lives at 123 Main St, Boston, USA\"\n    }],\n    response_model=Person\n)\n\nprint(person.address.city)  # \"Boston\"\n```\n\n#### Optional Fields\n\n```python\nfrom typing import Optional\n\nclass Product(BaseModel):\n    name: str\n    price: float\n    discount: Optional[float] = None  # Optional\n    description: str = Field(default=\"No description\")  # Default value\n\n# LLM doesn't need to provide discount or description\n```\n\n#### Enums for Constraints\n\n```python\nfrom enum import Enum\n\nclass Sentiment(str, Enum):\n    POSITIVE = \"positive\"\n    NEGATIVE = \"negative\"\n    NEUTRAL = \"neutral\"\n\nclass Review(BaseModel):\n    text: str\n    sentiment: Sentiment  # Only these 3 values allowed\n\nreview = client.messages.create(\n    model=\"claude-sonnet-4-5-20250929\",\n    max_tokens=1024,\n    messages=[{\n        \"role\": \"user\",\n        \"content\": \"This product is amazing!\"\n    }],\n    response_model=Review\n)\n\nprint(review.sentiment)  # Sentiment.POSITIVE\n```\n\n### 2. Validation\n\nPydantic validates LLM outputs automatically. If validation fails, Instructor retries.\n\n#### Built-in Validators\n\n```python\nfrom pydantic import Field, EmailStr, HttpUrl\n\nclass Contact(BaseModel):\n    name: str = Field(min_length=2, max_length=100)\n    age: int = Field(ge=0, le=120)  # 0 \u003C= age \u003C= 120\n    email: EmailStr  # Validates email format\n    website: HttpUrl  # Validates URL format\n\n# If LLM provides invalid data, Instructor retries automatically\n```\n\n#### Custom Validators\n\n```python\nfrom pydantic import field_validator\n\nclass Event(BaseModel):\n    name: str\n    date: str\n    attendees: int\n\n    @field_validator('date')\n    def validate_date(cls, v):\n        \"\"\"Ensure date is in YYYY-MM-DD format.\"\"\"\n        import re\n        if not re.match(r'\\d{4}-\\d{2}-\\d{2}', v):\n            raise ValueError('Date must be YYYY-MM-DD format')\n        return v\n\n    @field_validator('attendees')\n    def validate_attendees(cls, v):\n        \"\"\"Ensure positive attendees.\"\"\"\n        if v \u003C 1:\n            raise ValueError('Must have at least 1 attendee')\n        return v\n```\n\n#### Model-Level Validation\n\n```python\nfrom pydantic import model_validator\n\nclass DateRange(BaseModel):\n    start_date: str\n    end_date: str\n\n    @model_validator(mode='after')\n    def check_dates(self):\n        \"\"\"Ensure end_date is after start_date.\"\"\"\n        from datetime import datetime\n        start = datetime.strptime(self.start_date, '%Y-%m-%d')\n        end = datetime.strptime(self.end_date, '%Y-%m-%d')\n\n        if end \u003C start:\n            raise ValueError('end_date must be after start_date')\n        return self\n```\n\n### 3. Automatic Retrying\n\nInstructor retries automatically when validation fails, providing error feedback to the LLM.\n\n```python\n# Retries up to 3 times if validation fails\nuser = client.messages.create(\n    model=\"claude-sonnet-4-5-20250929\",\n    max_tokens=1024,\n    messages=[{\n        \"role\": \"user\",\n        \"content\": \"Extract user from: John, age unknown\"\n    }],\n    response_model=User,\n    max_retries=3  # Default is 3\n)\n\n# If age can't be extracted, Instructor tells the LLM:\n# \"Validation error: age - field required\"\n# LLM tries again with better extraction\n```\n\n**How it works:**\n1. LLM generates output\n2. Pydantic validates\n3. If invalid: Error message sent back to LLM\n4. LLM tries again with error feedback\n5. Repeats up to max_retries\n\n### 4. Streaming\n\nStream partial results for real-time processing.\n\n#### Streaming Partial Objects\n\n```python\nfrom instructor import Partial\n\nclass Story(BaseModel):\n    title: str\n    content: str\n    tags: list[str]\n\n# Stream partial updates as LLM generates\nfor partial_story in client.messages.create_partial(\n    model=\"claude-sonnet-4-5-20250929\",\n    max_tokens=1024,\n    messages=[{\n        \"role\": \"user\",\n        \"content\": \"Write a short sci-fi story\"\n    }],\n    response_model=Story\n):\n    print(f\"Title: {partial_story.title}\")\n    print(f\"Content so far: {partial_story.content[:100]}...\")\n    # Update UI in real-time\n```\n\n#### Streaming Iterables\n\n```python\nclass Task(BaseModel):\n    title: str\n    priority: str\n\n# Stream list items as they're generated\ntasks = client.messages.create_iterable(\n    model=\"claude-sonnet-4-5-20250929\",\n    max_tokens=1024,\n    messages=[{\n        \"role\": \"user\",\n        \"content\": \"Generate 10 project tasks\"\n    }],\n    response_model=Task\n)\n\nfor task in tasks:\n    print(f\"- {task.title} ({task.priority})\")\n    # Process each task as it arrives\n```\n\n## Provider Configuration\n\n### Anthropic Claude\n\n```python\nimport instructor\nfrom anthropic import Anthropic\n\nclient = instructor.from_anthropic(\n    Anthropic(api_key=\"your-api-key\")\n)\n\n# Use with Claude models\nresponse = client.messages.create(\n    model=\"claude-sonnet-4-5-20250929\",\n    max_tokens=1024,\n    messages=[...],\n    response_model=YourModel\n)\n```\n\n### OpenAI\n\n```python\nfrom openai import OpenAI\n\nclient = instructor.from_openai(\n    OpenAI(api_key=\"your-api-key\")\n)\n\nresponse = client.chat.completions.create(\n    model=\"gpt-4o-mini\",\n    response_model=YourModel,\n    messages=[...]\n)\n```\n\n### Local Models (Ollama)\n\n```python\nfrom openai import OpenAI\n\n# Point to local Ollama server\nclient = instructor.from_openai(\n    OpenAI(\n        base_url=\"http:\u002F\u002Flocalhost:11434\u002Fv1\",\n        api_key=\"ollama\"  # Required but ignored\n    ),\n    mode=instructor.Mode.JSON\n)\n\nresponse = client.chat.completions.create(\n    model=\"llama3.1\",\n    response_model=YourModel,\n    messages=[...]\n)\n```\n\n## Common Patterns\n\n### Pattern 1: Data Extraction from Text\n\n```python\nclass CompanyInfo(BaseModel):\n    name: str\n    founded_year: int\n    industry: str\n    employees: int\n    headquarters: str\n\ntext = \"\"\"\nTesla, Inc. was founded in 2003. It operates in the automotive and energy\nindustry with approximately 140,000 employees. The company is headquartered\nin Austin, Texas.\n\"\"\"\n\ncompany = client.messages.create(\n    model=\"claude-sonnet-4-5-20250929\",\n    max_tokens=1024,\n    messages=[{\n        \"role\": \"user\",\n        \"content\": f\"Extract company information from: {text}\"\n    }],\n    response_model=CompanyInfo\n)\n```\n\n### Pattern 2: Classification\n\n```python\nclass Category(str, Enum):\n    TECHNOLOGY = \"technology\"\n    FINANCE = \"finance\"\n    HEALTHCARE = \"healthcare\"\n    EDUCATION = \"education\"\n    OTHER = \"other\"\n\nclass ArticleClassification(BaseModel):\n    category: Category\n    confidence: float = Field(ge=0.0, le=1.0)\n    keywords: list[str]\n\nclassification = client.messages.create(\n    model=\"claude-sonnet-4-5-20250929\",\n    max_tokens=1024,\n    messages=[{\n        \"role\": \"user\",\n        \"content\": \"Classify this article: [article text]\"\n    }],\n    response_model=ArticleClassification\n)\n```\n\n### Pattern 3: Multi-Entity Extraction\n\n```python\nclass Person(BaseModel):\n    name: str\n    role: str\n\nclass Organization(BaseModel):\n    name: str\n    industry: str\n\nclass Entities(BaseModel):\n    people: list[Person]\n    organizations: list[Organization]\n    locations: list[str]\n\ntext = \"Tim Cook, CEO of Apple, announced at the event in Cupertino...\"\n\nentities = client.messages.create(\n    model=\"claude-sonnet-4-5-20250929\",\n    max_tokens=1024,\n    messages=[{\n        \"role\": \"user\",\n        \"content\": f\"Extract all entities from: {text}\"\n    }],\n    response_model=Entities\n)\n\nfor person in entities.people:\n    print(f\"{person.name} - {person.role}\")\n```\n\n### Pattern 4: Structured Analysis\n\n```python\nclass SentimentAnalysis(BaseModel):\n    overall_sentiment: Sentiment\n    positive_aspects: list[str]\n    negative_aspects: list[str]\n    suggestions: list[str]\n    score: float = Field(ge=-1.0, le=1.0)\n\nreview = \"The product works well but setup was confusing...\"\n\nanalysis = client.messages.create(\n    model=\"claude-sonnet-4-5-20250929\",\n    max_tokens=1024,\n    messages=[{\n        \"role\": \"user\",\n        \"content\": f\"Analyze this review: {review}\"\n    }],\n    response_model=SentimentAnalysis\n)\n```\n\n### Pattern 5: Batch Processing\n\n```python\ndef extract_person(text: str) -> Person:\n    return client.messages.create(\n        model=\"claude-sonnet-4-5-20250929\",\n        max_tokens=1024,\n        messages=[{\n            \"role\": \"user\",\n            \"content\": f\"Extract person from: {text}\"\n        }],\n        response_model=Person\n    )\n\ntexts = [\n    \"John Doe is a 30-year-old engineer\",\n    \"Jane Smith, 25, works in marketing\",\n    \"Bob Johnson, age 40, software developer\"\n]\n\npeople = [extract_person(text) for text in texts]\n```\n\n## Advanced Features\n\n### Union Types\n\n```python\nfrom typing import Union\n\nclass TextContent(BaseModel):\n    type: str = \"text\"\n    content: str\n\nclass ImageContent(BaseModel):\n    type: str = \"image\"\n    url: HttpUrl\n    caption: str\n\nclass Post(BaseModel):\n    title: str\n    content: Union[TextContent, ImageContent]  # Either type\n\n# LLM chooses appropriate type based on content\n```\n\n### Dynamic Models\n\n```python\nfrom pydantic import create_model\n\n# Create model at runtime\nDynamicUser = create_model(\n    'User',\n    name=(str, ...),\n    age=(int, Field(ge=0)),\n    email=(EmailStr, ...)\n)\n\nuser = client.messages.create(\n    model=\"claude-sonnet-4-5-20250929\",\n    max_tokens=1024,\n    messages=[...],\n    response_model=DynamicUser\n)\n```\n\n### Custom Modes\n\n```python\n# For providers without native structured outputs\nclient = instructor.from_anthropic(\n    Anthropic(),\n    mode=instructor.Mode.JSON  # JSON mode\n)\n\n# Available modes:\n# - Mode.ANTHROPIC_TOOLS (recommended for Claude)\n# - Mode.JSON (fallback)\n# - Mode.TOOLS (OpenAI tools)\n```\n\n### Context Management\n\n```python\n# Single-use client\nwith instructor.from_anthropic(Anthropic()) as client:\n    result = client.messages.create(\n        model=\"claude-sonnet-4-5-20250929\",\n        max_tokens=1024,\n        messages=[...],\n        response_model=YourModel\n    )\n    # Client closed automatically\n```\n\n## Error Handling\n\n### Handling Validation Errors\n\n```python\nfrom pydantic import ValidationError\n\ntry:\n    user = client.messages.create(\n        model=\"claude-sonnet-4-5-20250929\",\n        max_tokens=1024,\n        messages=[...],\n        response_model=User,\n        max_retries=3\n    )\nexcept ValidationError as e:\n    print(f\"Failed after retries: {e}\")\n    # Handle gracefully\n\nexcept Exception as e:\n    print(f\"API error: {e}\")\n```\n\n### Custom Error Messages\n\n```python\nclass ValidatedUser(BaseModel):\n    name: str = Field(description=\"Full name, 2-100 characters\")\n    age: int = Field(description=\"Age between 0 and 120\", ge=0, le=120)\n    email: EmailStr = Field(description=\"Valid email address\")\n\n    class Config:\n        # Custom error messages\n        json_schema_extra = {\n            \"examples\": [\n                {\n                    \"name\": \"John Doe\",\n                    \"age\": 30,\n                    \"email\": \"john@example.com\"\n                }\n            ]\n        }\n```\n\n## Best Practices\n\n### 1. Clear Field Descriptions\n\n```python\n# ❌ Bad: Vague\nclass Product(BaseModel):\n    name: str\n    price: float\n\n# ✅ Good: Descriptive\nclass Product(BaseModel):\n    name: str = Field(description=\"Product name from the text\")\n    price: float = Field(description=\"Price in USD, without currency symbol\")\n```\n\n### 2. Use Appropriate Validation\n\n```python\n# ✅ Good: Constrain values\nclass Rating(BaseModel):\n    score: int = Field(ge=1, le=5, description=\"Rating from 1 to 5 stars\")\n    review: str = Field(min_length=10, description=\"Review text, at least 10 chars\")\n```\n\n### 3. Provide Examples in Prompts\n\n```python\nmessages = [{\n    \"role\": \"user\",\n    \"content\": \"\"\"Extract person info from: \"John, 30, engineer\"\n\nExample format:\n{\n  \"name\": \"John Doe\",\n  \"age\": 30,\n  \"occupation\": \"engineer\"\n}\"\"\"\n}]\n```\n\n### 4. Use Enums for Fixed Categories\n\n```python\n# ✅ Good: Enum ensures valid values\nclass Status(str, Enum):\n    PENDING = \"pending\"\n    APPROVED = \"approved\"\n    REJECTED = \"rejected\"\n\nclass Application(BaseModel):\n    status: Status  # LLM must choose from enum\n```\n\n### 5. Handle Missing Data Gracefully\n\n```python\nclass PartialData(BaseModel):\n    required_field: str\n    optional_field: Optional[str] = None\n    default_field: str = \"default_value\"\n\n# LLM only needs to provide required_field\n```\n\n## Comparison to Alternatives\n\n| Feature | Instructor | Manual JSON | LangChain | DSPy |\n|---------|------------|-------------|-----------|------|\n| Type Safety | ✅ Yes | ❌ No | ⚠️ Partial | ✅ Yes |\n| Auto Validation | ✅ Yes | ❌ No | ❌ No | ⚠️ Limited |\n| Auto Retry | ✅ Yes | ❌ No | ❌ No | ✅ Yes |\n| Streaming | ✅ Yes | ❌ No | ✅ Yes | ❌ No |\n| Multi-Provider | ✅ Yes | ⚠️ Manual | ✅ Yes | ✅ Yes |\n| Learning Curve | Low | Low | Medium | High |\n\n**When to choose Instructor:**\n- Need structured, validated outputs\n- Want type safety and IDE support\n- Require automatic retries\n- Building data extraction systems\n\n**When to choose alternatives:**\n- DSPy: Need prompt optimization\n- LangChain: Building complex chains\n- Manual: Simple, one-off extractions\n\n## Resources\n\n- **Documentation**: https:\u002F\u002Fpython.useinstructor.com\n- **GitHub**: https:\u002F\u002Fgithub.com\u002Fjxnl\u002Finstructor (15k+ stars)\n- **Cookbook**: https:\u002F\u002Fpython.useinstructor.com\u002Fexamples\n- **Discord**: Community support available\n\n## See Also\n\n- `references\u002Fvalidation.md` - Advanced validation patterns\n- `references\u002Fproviders.md` - Provider-specific configuration\n- `references\u002Fexamples.md` - Real-world use cases\n\n\n","# Instructor: Structured LLM Outputs\n\n## When to Use This Skill\n\nUse Instructor when you need to:\n- **Extract structured data** from LLM responses reliably\n- **Validate outputs** against Pydantic schemas automatically\n- **Retry failed extractions** with automatic error handling\n- **Parse complex JSON** with type safety and validation\n- **Stream partial results** for real-time processing\n- **Support multiple LLM providers** with consistent API\n\n**GitHub Stars**: 15,000+ | **Battle-tested**: 100,000+ developers\n\n## Installation\n\n```bash\n# Base installation\npip install instructor\n\n# With specific providers\npip install \"instructor[anthropic]\"  # Anthropic Claude\npip install \"instructor[openai]\"     # OpenAI\npip install \"instructor[all]\"        # All providers\n```\n\n## Quick Start\n\n### Basic Example: Extract User Data\n\n```python\nimport instructor\nfrom pydantic import BaseModel\nfrom anthropic import Anthropic\n\n# Define output structure\nclass User(BaseModel):\n    name: str\n    age: int\n    email: str\n\n# Create instructor client\nclient = instructor.from_anthropic(Anthropic())\n\n# Extract structured data\nuser = client.messages.create(\n    model=\"claude-sonnet-4-5-20250929\",\n    max_tokens=1024,\n    messages=[{\n        \"role\": \"user\",\n        \"content\": \"John Doe is 30 years old. His email is john@example.com\"\n    }],\n    response_model=User\n)\n\nprint(user.name)   # \"John Doe\"\nprint(user.age)    # 30\nprint(user.email)  # \"john@example.com\"\n```\n\n### With OpenAI\n\n```python\nfrom openai import OpenAI\n\nclient = instructor.from_openai(OpenAI())\n\nuser = client.chat.completions.create(\n    model=\"gpt-4o-mini\",\n    response_model=User,\n    messages=[{\"role\": \"user\", \"content\": \"Extract: Alice, 25, alice@email.com\"}]\n)\n```\n\n## Core Concepts\n\n### 1. Response Models (Pydantic)\n\nResponse models define the structure and validation rules for LLM outputs.\n\n#### Basic Model\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 relevant tags\")\n\narticle = client.messages.create(\n    model=\"claude-sonnet-4-5-20250929\",\n    max_tokens=1024,\n    messages=[{\n        \"role\": \"user\",\n        \"content\": \"Analyze this article: [article text]\"\n    }],\n    response_model=Article\n)\n```\n\n**Benefits:**\n- Type safety with Python type hints\n- Automatic validation (word_count > 0)\n- Self-documenting with Field descriptions\n- IDE autocomplete support\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\nperson = client.messages.create(\n    model=\"claude-sonnet-4-5-20250929\",\n    max_tokens=1024,\n    messages=[{\n        \"role\": \"user\",\n        \"content\": \"John lives at 123 Main St, Boston, USA\"\n    }],\n    response_model=Person\n)\n\nprint(person.address.city)  # \"Boston\"\n```\n\n#### Optional Fields\n\n```python\nfrom typing import Optional\n\nclass Product(BaseModel):\n    name: str\n    price: float\n    discount: Optional[float] = None  # Optional\n    description: str = Field(default=\"No description\")  # Default value\n\n# LLM doesn't need to provide discount or description\n```\n\n#### Enums for Constraints\n\n```python\nfrom enum import Enum\n\nclass Sentiment(str, Enum):\n    POSITIVE = \"positive\"\n    NEGATIVE = \"negative\"\n    NEUTRAL = \"neutral\"\n\nclass Review(BaseModel):\n    text: str\n    sentiment: Sentiment  # Only these 3 values allowed\n\nreview = client.messages.create(\n    model=\"claude-sonnet-4-5-20250929\",\n    max_tokens=1024,\n    messages=[{\n        \"role\": \"user\",\n        \"content\": \"This product is amazing!\"\n    }],\n    response_model=Review\n)\n\nprint(review.sentiment)  # Sentiment.POSITIVE\n```\n\n### 2. Validation\n\nPydantic validates LLM outputs automatically. If validation fails, Instructor retries.\n\n#### Built-in Validators\n\n```python\nfrom pydantic import Field, EmailStr, HttpUrl\n\nclass Contact(BaseModel):\n    name: str = Field(min_length=2, max_length=100)\n    age: int = Field(ge=0, le=120)  # 0 \u003C= age \u003C= 120\n    email: EmailStr  # Validates email format\n    website: HttpUrl  # Validates URL format\n\n# If LLM provides invalid data, Instructor retries automatically\n```\n\n#### Custom Validators\n\n```python\nfrom pydantic import field_validator\n\nclass Event(BaseModel):\n    name: str\n    date: str\n    attendees: int\n\n    @field_validator('date')\n    def validate_date(cls, v):\n        \"\"\"Ensure date is in YYYY-MM-DD format.\"\"\"\n        import re\n        if not re.match(r'\\d{4}-\\d{2}-\\d{2}', v):\n            raise ValueError('Date must be YYYY-MM-DD format')\n        return v\n\n    @field_validator('attendees')\n    def validate_attendees(cls, v):\n        \"\"\"Ensure positive attendees.\"\"\"\n        if v \u003C 1:\n            raise ValueError('Must have at least 1 attendee')\n        return v\n```\n\n#### Model-Level Validation\n\n```python\nfrom pydantic import model_validator\n\nclass DateRange(BaseModel):\n    start_date: str\n    end_date: str\n\n    @model_validator(mode='after')\n    def check_dates(self):\n        \"\"\"Ensure end_date is after start_date.\"\"\"\n        from datetime import datetime\n        start = datetime.strptime(self.start_date, '%Y-%m-%d')\n        end = datetime.strptime(self.end_date, '%Y-%m-%d')\n\n        if end \u003C start:\n            raise ValueError('end_date must be after start_date')\n        return self\n```\n\n### 3. Automatic Retrying\n\nInstructor retries automatically when validation fails, providing error feedback to the LLM.\n\n```python\n# Retries up to 3 times if validation fails\nuser = client.messages.create(\n    model=\"claude-sonnet-4-5-20250929\",\n    max_tokens=1024,\n    messages=[{\n        \"role\": \"user\",\n        \"content\": \"Extract user from: John, age unknown\"\n    }],\n    response_model=User,\n    max_retries=3  # Default is 3\n)\n\n# If age can't be extracted, Instructor tells the LLM:\n# \"Validation error: age - field required\"\n# LLM tries again with better extraction\n```\n\n**How it works:**\n1. LLM generates output\n2. Pydantic validates\n3. If invalid: Error message sent back to LLM\n4. LLM tries again with error feedback\n5. Repeats up to max_retries\n\n### 4. Streaming\n\nStream partial results for real-time processing.\n\n#### Streaming Partial Objects\n\n```python\nfrom instructor import Partial\n\nclass Story(BaseModel):\n    title: str\n    content: str\n    tags: list[str]\n\n# Stream partial updates as LLM generates\nfor partial_story in client.messages.create_partial(\n    model=\"claude-sonnet-4-5-20250929\",\n    max_tokens=1024,\n    messages=[{\n        \"role\": \"user\",\n        \"content\": \"Write a short sci-fi story\"\n    }],\n    response_model=Story\n):\n    print(f\"Title: {partial_story.title}\")\n    print(f\"Content so far: {partial_story.content[:100]}...\")\n    # Update UI in real-time\n```\n\n#### Streaming Iterables\n\n```python\nclass Task(BaseModel):\n    title: str\n    priority: str\n\n# Stream list items as they're generated\ntasks = client.messages.create_iterable(\n    model=\"claude-sonnet-4-5-20250929\",\n    max_tokens=1024,\n    messages=[{\n        \"role\": \"user\",\n        \"content\": \"Generate 10 project tasks\"\n    }],\n    response_model=Task\n)\n\nfor task in tasks:\n    print(f\"- {task.title} ({task.priority})\")\n    # Process each task as it arrives\n```\n\n## Provider Configuration\n\n### Anthropic Claude\n\n```python\nimport instructor\nfrom anthropic import Anthropic\n\nclient = instructor.from_anthropic(\n    Anthropic(api_key=\"your-api-key\")\n)\n\n# Use with Claude models\nresponse = client.messages.create(\n    model=\"claude-sonnet-4-5-20250929\",\n    max_tokens=1024,\n    messages=[...],\n    response_model=YourModel\n)\n```\n\n### OpenAI\n\n```python\nfrom openai import OpenAI\n\nclient = instructor.from_openai(\n    OpenAI(api_key=\"your-api-key\")\n)\n\nresponse = client.chat.completions.create(\n    model=\"gpt-4o-mini\",\n    response_model=YourModel,\n    messages=[...]\n)\n```\n\n### Local Models (Ollama)\n\n```python\nfrom openai import OpenAI\n\n# Point to local Ollama server\nclient = instructor.from_openai(\n    OpenAI(\n        base_url=\"http:\u002F\u002Flocalhost:11434\u002Fv1\",\n        api_key=\"ollama\"  # Required but ignored\n    ),\n    mode=instructor.Mode.JSON\n)\n\nresponse = client.chat.completions.create(\n    model=\"llama3.1\",\n    response_model=YourModel,\n    messages=[...]\n)\n```\n\n## Common Patterns\n\n### Pattern 1: Data Extraction from Text\n\n```python\nclass CompanyInfo(BaseModel):\n    name: str\n    founded_year: int\n    industry: str\n    employees: int\n    headquarters: str\n\ntext = \"\"\"\nTesla, Inc. was founded in 2003. It operates in the automotive and energy\nindustry with approximately 140,000 employees. The company is headquartered\nin Austin, Texas.\n\"\"\"\n\ncompany = client.messages.create(\n    model=\"claude-sonnet-4-5-20250929\",\n    max_tokens=1024,\n    messages=[{\n        \"role\": \"user\",\n        \"content\": f\"Extract company information from: {text}\"\n    }],\n    response_model=CompanyInfo\n)\n```\n\n### Pattern 2: Classification\n\n```python\nclass Category(str, Enum):\n    TECHNOLOGY = \"technology\"\n    FINANCE = \"finance\"\n    HEALTHCARE = \"healthcare\"\n    EDUCATION = \"education\"\n    OTHER = \"other\"\n\nclass ArticleClassification(BaseModel):\n    category: Category\n    confidence: float = Field(ge=0.0, le=1.0)\n    keywords: list[str]\n\nclassification = client.messages.create(\n    model=\"claude-sonnet-4-5-20250929\",\n    max_tokens=1024,\n    messages=[{\n        \"role\": \"user\",\n        \"content\": \"Classify this article: [article text]\"\n    }],\n    response_model=ArticleClassification\n)\n```\n\n### Pattern 3: Multi-Entity Extraction\n\n```python\nclass Person(BaseModel):\n    name: str\n    role: str\n\nclass Organization(BaseModel):\n    name: str\n    industry: str\n\nclass Entities(BaseModel):\n    people: list[Person]\n    organizations: list[Organization]\n    locations: list[str]\n\ntext = \"Tim Cook, CEO of Apple, announced at the event in Cupertino...\"\n\nentities = client.messages.create(\n    model=\"claude-sonnet-4-5-20250929\",\n    max_tokens=1024,\n    messages=[{\n        \"role\": \"user\",\n        \"content\": f\"Extract all entities from: {text}\"\n    }],\n    response_model=Entities\n)\n\nfor person in entities.people:\n    print(f\"{person.name} - {person.role}\")\n```\n\n### Pattern 4: Structured Analysis\n\n```python\nclass SentimentAnalysis(BaseModel):\n    overall_sentiment: Sentiment\n    positive_aspects: list[str]\n    negative_aspects: list[str]\n    suggestions: list[str]\n    score: float = Field(ge=-1.0, le=1.0)\n\nreview = \"The product works well but setup was confusing...\"\n\nanalysis = client.messages.create(\n    model=\"claude-sonnet-4-5-20250929\",\n    max_tokens=1024,\n    messages=[{\n        \"role\": \"user\",\n        \"content\": f\"Analyze this review: {review}\"\n    }],\n    response_model=SentimentAnalysis\n)\n```\n\n### Pattern 5: Batch Processing\n\n```python\ndef extract_person(text: str) -> Person:\n    return client.messages.create(\n        model=\"claude-sonnet-4-5-20250929\",\n        max_tokens=1024,\n        messages=[{\n            \"role\": \"user\",\n            \"content\": f\"Extract person from: {text}\"\n        }],\n        response_model=Person\n    )\n\ntexts = [\n    \"John Doe is a 30-year-old engineer\",\n    \"Jane Smith, 25, works in marketing\",\n    \"Bob Johnson, age 40, software developer\"\n]\n\npeople = [extract_person(text) for text in texts]\n```\n\n## Advanced Features\n\n### Union Types\n\n```python\nfrom typing import Union\n\nclass TextContent(BaseModel):\n    type: str = \"text\"\n    content: str\n\nclass ImageContent(BaseModel):\n    type: str = \"image\"\n    url: HttpUrl\n    caption: str\n\nclass Post(BaseModel):\n    title: str\n    content: Union[TextContent, ImageContent]  # Either type\n\n# LLM chooses appropriate type based on content\n```\n\n### Dynamic Models\n\n```python\nfrom pydantic import create_model\n\n# Create model at runtime\nDynamicUser = create_model(\n    'User',\n    name=(str, ...),\n    age=(int, Field(ge=0)),\n    email=(EmailStr, ...)\n)\n\nuser = client.messages.create(\n    model=\"claude-sonnet-4-5-20250929\",\n    max_tokens=1024,\n    messages=[...],\n    response_model=DynamicUser\n)\n```\n\n### Custom Modes\n\n```python\n# For providers without native structured outputs\nclient = instructor.from_anthropic(\n    Anthropic(),\n    mode=instructor.Mode.JSON  # JSON mode\n)\n\n# Available modes:\n# - Mode.ANTHROPIC_TOOLS (recommended for Claude)\n# - Mode.JSON (fallback)\n# - Mode.TOOLS (OpenAI tools)\n```\n\n### Context Management\n\n```python\n# Single-use client\nwith instructor.from_anthropic(Anthropic()) as client:\n    result = client.messages.create(\n        model=\"claude-sonnet-4-5-20250929\",\n        max_tokens=1024,\n        messages=[...],\n        response_model=YourModel\n    )\n    # Client closed automatically\n```\n\n## Error Handling\n\n### Handling Validation Errors\n\n```python\nfrom pydantic import ValidationError\n\ntry:\n    user = client.messages.create(\n        model=\"claude-sonnet-4-5-20250929\",\n        max_tokens=1024,\n        messages=[...],\n        response_model=User,\n        max_retries=3\n    )\nexcept ValidationError as e:\n    print(f\"Failed after retries: {e}\")\n    # Handle gracefully\n\nexcept Exception as e:\n    print(f\"API error: {e}\")\n```\n\n### Custom Error Messages\n\n```python\nclass ValidatedUser(BaseModel):\n    name: str = Field(description=\"Full name, 2-100 characters\")\n    age: int = Field(description=\"Age between 0 and 120\", ge=0, le=120)\n    email: EmailStr = Field(description=\"Valid email address\")\n\n    class Config:\n        # Custom error messages\n        json_schema_extra = {\n            \"examples\": [\n                {\n                    \"name\": \"John Doe\",\n                    \"age\": 30,\n                    \"email\": \"john@example.com\"\n                }\n            ]\n        }\n```\n\n## Best Practices\n\n### 1. Clear Field Descriptions\n\n```python\n# ❌ Bad: Vague\nclass Product(BaseModel):\n    name: str\n    price: float\n\n# ✅ Good: Descriptive\nclass Product(BaseModel):\n    name: str = Field(description=\"Product name from the text\")\n    price: float = Field(description=\"Price in USD, without currency symbol\")\n```\n\n### 2. Use Appropriate Validation\n\n```python\n# ✅ Good: Constrain values\nclass Rating(BaseModel):\n    score: int = Field(ge=1, le=5, description=\"Rating from 1 to 5 stars\")\n    review: str = Field(min_length=10, description=\"Review text, at least 10 chars\")\n```\n\n### 3. Provide Examples in Prompts\n\n```python\nmessages = [{\n    \"role\": \"user\",\n    \"content\": \"\"\"Extract person info from: \"John, 30, engineer\"\n\nExample format:\n{\n  \"name\": \"John Doe\",\n  \"age\": 30,\n  \"occupation\": \"engineer\"\n}\"\"\"\n}]\n```\n\n### 4. Use Enums for Fixed Categories\n\n```python\n# ✅ Good: Enum ensures valid values\nclass Status(str, Enum):\n    PENDING = \"pending\"\n    APPROVED = \"approved\"\n    REJECTED = \"rejected\"\n\nclass Application(BaseModel):\n    status: Status  # LLM must choose from enum\n```\n\n### 5. Handle Missing Data Gracefully\n\n```python\nclass PartialData(BaseModel):\n    required_field: str\n    optional_field: Optional[str] = None\n    default_field: str = \"default_value\"\n\n# LLM only needs to provide required_field\n```\n\n## Comparison to Alternatives\n\n| Feature | Instructor | Manual JSON | LangChain | DSPy |\n|---------|------------|-------------|-----------|------|\n| Type Safety | ✅ Yes | ❌ No | ⚠️ Partial | ✅ Yes |\n| Auto Validation | ✅ Yes | ❌ No | ❌ No | ⚠️ Limited |\n| Auto Retry | ✅ Yes | ❌ No | ❌ No | ✅ Yes |\n| Streaming | ✅ Yes | ❌ No | ✅ Yes | ❌ No |\n| Multi-Provider | ✅ Yes | ⚠️ Manual | ✅ Yes | ✅ Yes |\n| Learning Curve | Low | Low | Medium | High |\n\n**When to choose Instructor:**\n- Need structured, validated outputs\n- Want type safety and IDE support\n- Require automatic retries\n- Building data extraction systems\n\n**When to choose alternatives:**\n- DSPy: Need prompt optimization\n- LangChain: Building complex chains\n- Manual: Simple, one-off extractions\n\n## Resources\n\n- **Documentation**: https:\u002F\u002Fpython.useinstructor.com\n- **GitHub**: https:\u002F\u002Fgithub.com\u002Fjxnl\u002Finstructor (15k+ stars)\n- **Cookbook**: https:\u002F\u002Fpython.useinstructor.com\u002Fexamples\n- **Discord**: Community support available\n\n## See Also\n\n- `references\u002Fvalidation.md` - Advanced validation patterns\n- `references\u002Fproviders.md` - Provider-specific configuration\n- `references\u002Fexamples.md` - Real-world use cases\n\n\n",{"name":5,"license":15,"metadata":56,"description":7},{"tags":57,"author":68,"version":69,"dependencies":70},[58,59,60,43,61,62,63,64,65,66,67],"Prompt Engineering","Instructor","Structured Output","Data Extraction","JSON Parsing","Type Safety","Validation","Streaming","OpenAI","Anthropic","Orchestra Research","1.0.0",[30,71,48,72],"pydantic","anthropic","main","2b749c82b5d11fac39d9a6e021ce3f5b8a541bd1","cli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fprompt-engineering-instructor",3926,"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","2026-11-12 14:45:56",{"source":8,"skillName":5,"commands":84,"prompt":94,"zipAvailable":95},[85,88,91],{"tool":86,"command":87},"npx","npx skills add davila7\u002Fclaude-code-templates --skill prompt-engineering-instructor",{"tool":89,"command":90},"bunx","bunx skills add davila7\u002Fclaude-code-templates --skill prompt-engineering-instructor",{"tool":92,"command":93},"pnpm","pnpm dlx skills add davila7\u002Fclaude-code-templates --skill prompt-engineering-instructor","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-instructor\u002FSKILL.md",true,[97,103,107,111,115,119,123,127],{"path":98,"resourceType":99,"resourceTypeDesc":100,"sizeBytes":101,"sourceHtmlUrl":102},"README.md","REFERENCE","参考文档",9260,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fenterprise-communication\u002Fdifficult-workplace-conversations\u002FREADME.md",{"path":104,"resourceType":99,"resourceTypeDesc":100,"sizeBytes":105,"sourceHtmlUrl":106},"references\u002Fconversation-framework.md",9460,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fenterprise-communication\u002Fdifficult-workplace-conversations\u002Freferences\u002Fconversation-framework.md",{"path":108,"resourceType":99,"resourceTypeDesc":100,"sizeBytes":109,"sourceHtmlUrl":110},"references\u002Fdelivery-scripts.md",8854,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fenterprise-communication\u002Fdifficult-workplace-conversations\u002Freferences\u002Fdelivery-scripts.md",{"path":112,"resourceType":99,"resourceTypeDesc":100,"sizeBytes":113,"sourceHtmlUrl":114},"references\u002Femotional-regulation.md",8812,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fenterprise-communication\u002Fdifficult-workplace-conversations\u002Freferences\u002Femotional-regulation.md",{"path":116,"resourceType":99,"resourceTypeDesc":100,"sizeBytes":117,"sourceHtmlUrl":118},"references\u002Fexamples.md",2320,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fprompt-engineering-instructor\u002Freferences\u002Fexamples.md",{"path":120,"resourceType":99,"resourceTypeDesc":100,"sizeBytes":121,"sourceHtmlUrl":122},"references\u002Fpreparation-template.md",7357,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fenterprise-communication\u002Fdifficult-workplace-conversations\u002Freferences\u002Fpreparation-template.md",{"path":124,"resourceType":99,"resourceTypeDesc":100,"sizeBytes":125,"sourceHtmlUrl":126},"references\u002Fproviders.md",1329,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fprompt-engineering-instructor\u002Freferences\u002Fproviders.md",{"path":128,"resourceType":99,"resourceTypeDesc":100,"sizeBytes":129,"sourceHtmlUrl":130},"references\u002Fvalidation.md",15306,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fprompt-engineering-instructor\u002Freferences\u002Fvalidation.md",[132,160,187,216,251,275],{"skillNo":133,"name":134,"nameZh":6,"description":135,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":136,"categories":137,"keywords":139,"publishedAt":51},"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",[138],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159],"scoring","pool","wealthville pool scoring","liquidity","which","for","exit","now","use","when","reduce","with","record","track","verdict","evm","solana","how","public","look",{"skillNo":161,"name":162,"nameZh":6,"description":163,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":164,"categories":165,"keywords":167,"publishedAt":51},"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",[166],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[168,169,170,171,172,173,174,175,176,177,178,149,179,180,181,182,183,184,185,186],"technical debt","debt","technical","technical debt auditor","auditor","coverage","complexity","markers","project","cleanup","unused","code","todo","work","plan","focus","signals","audit","dependency",{"skillNo":188,"name":189,"nameZh":6,"description":190,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":191,"categories":192,"keywords":198,"publishedAt":51},"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",[193,196,197],{"categoryNo":194,"categoryName":195,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":24,"categoryName":25,"taxonomyType":26,"taxonomyTypeDesc":27},[199,200,201,202,203,204,205,145,206,207,208,209,210,211,212,148,213,214,149,215],"readonly","guard","sql guard","sql","readonly sql guard","erp","effect","read-only","db","just","tool","calls","only","MCP","access","database","query",{"skillNo":217,"name":218,"nameZh":6,"description":219,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":220,"licenseFileUrl":221,"sourceHtmlUrl":222,"categories":223,"keywords":227,"publishedAt":51},"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",[224],{"categoryNo":225,"categoryName":226,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000009","前端开发",[228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,145,250],"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":252,"name":253,"nameZh":6,"description":254,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":220,"licenseFileUrl":255,"sourceHtmlUrl":256,"categories":257,"keywords":262,"publishedAt":51},"01M6AFWF33SFV9Z3SF4PEEE4BF","webapp-testing","Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FSKILL.md",[258,261],{"categoryNo":259,"categoryName":260,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":225,"categoryName":226,"taxonomyType":21,"taxonomyTypeDesc":22},[263,264,265,266,267,229,268,238,239,269,270,271,272,145,273,151,274],"webapp testing","testing","webapp","browser","playwright","applications","debugging","toolkit","logs","screenshots","local","behavior",{"skillNo":276,"name":277,"nameZh":6,"description":278,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":220,"licenseFileUrl":279,"sourceHtmlUrl":280,"categories":281,"keywords":289,"publishedAt":51},"01M6AF67Q67JJJ5V51JR2P52KA","discernment-nudge","After you give a substantive answer or draft that the user may act on — advice or recommendations, drafted artifacts such as goals, plans, pitches, proposals, or emails, estimates or projections, analysis or interpretation of data, factual claims they may rely on, or a multi-step argument — invoke this skill BEFORE finalizing your reply and then, if it applies, append 2-3 short follow-up questions, each tied to something specific in what you just produced, that help the user check key facts, probe the reasoning or assumptions, and notice missing context. Do this at most once per conversation. Skip it when the user asked a trivial how-to or simple lookup, wants a purely educational explanation, asked you only to format, convert, or assemble a file from content they provided, is writing code they will run, is doing creative writing or casual chat, or already asked you to double-check, cite, or review — the skill file explains these boundaries and the exact output format.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fdiscernment-nudge\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fdiscernment-nudge\u002FSKILL.md",[282,285,286],{"categoryNo":283,"categoryName":284,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":287,"categoryName":288,"taxonomyType":26,"taxonomyTypeDesc":27},"01JBSEEDCAT000000000000036","内容创作者",[290,291,292,293,294,295,44,296,231,297,241,179,298,299,300,301,302,211,303,149,304,305,306,307,308,157,309],"discernment nudge","nudge","interpretation","help","run","reasoning","writing","lookup","content","review","conversation","short","up","invoke","you","analysis","claims","argument","assemble","recommendations"]