chore: track shared agent skills in repository

2026-10-27 22:01:48 +08:00
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+# Structured Outputs
+
+---
+
+## Structured Output Methods
+
+```
+┌─────────────────────────────────────────────────────────────────────────────┐
+│                    STRUCTURED OUTPUT APPROACHES                             │
+├─────────────────────────────────────────────────────────────────────────────┤
+│                                                                             │
+│  ┌─────────────────┐  ┌─────────────────┐  ┌─────────────────┐            │
+│  │   Prompt-Based  │  │   JSON Mode     │  │ Function Calling│            │
+│  │                 │  │                 │  │ (Tool Use)      │            │
+│  ├─────────────────┤  ├─────────────────┤  ├─────────────────┤            │
+│  │ Reliability: ~  │  │ Reliability: ++ │  │ Reliability: +++│            │
+│  │ Flexibility: +++│  │ Flexibility: ++ │  │ Flexibility: +  │            │
+│  │ Validation: --- │  │ Validation: +   │  │ Validation: +++ │            │
+│  └─────────────────┘  └─────────────────┘  └─────────────────┘            │
+│                                                                             │
+│  Use when:          Use when:             Use when:                        │
+│  - Simple extracts  - Need valid JSON     - Strict schemas required        │
+│  - Flexible schemas - Moderate complexity - Tool orchestration             │
+│  - Quick prototypes - Claude/GPT models   - Type-safe parsing              │
+│                                                                             │
+└─────────────────────────────────────────────────────────────────────────────┘
+```
+
+---
+
+## Prompt-Based Structured Output
+
+### Basic JSON Request
+
+```
+Extract the following information from the text and return as JSON:
+- person_name: string
+- company: string
+- role: string
+- email: string or null
+
+Text: {text}
+
+Return only valid JSON, no other text:
+```
+
+### With Schema Definition
+
+```
+Extract meeting information from the transcript.
+
+Return a JSON object matching this schema:
+{
+  "meeting_title": "string - the main topic discussed",
+  "date": "string - ISO 8601 format (YYYY-MM-DD) or null if not mentioned",
+  "attendees": ["array of strings - names of participants"],
+  "action_items": [
+    {
+      "task": "string - what needs to be done",
+      "assignee": "string - who is responsible",
+      "due_date": "string - ISO 8601 format or null"
+    }
+  ],
+  "decisions": ["array of strings - key decisions made"],
+  "next_meeting": "string - ISO 8601 datetime or null"
+}
+
+Rules:
+- Use null for fields not mentioned in the transcript
+- Use empty arrays [] for list fields with no items
+- Dates must be in ISO 8601 format
+- Return ONLY the JSON object, no explanation
+
+Transcript:
+{transcript}
+```
+
+### Output Wrapping Technique
+
+```
+Analyze the code and identify issues. Return your analysis in this exact format:
+
+<analysis>
+{
+  "summary": "one sentence overview",
+  "issues": [
+    {
+      "severity": "critical|high|medium|low",
+      "type": "bug|security|performance|style",
+      "location": "file:line or function name",
+      "description": "what's wrong",
+      "suggestion": "how to fix"
+    }
+  ],
+  "quality_score": 1-10
+}
+</analysis>
+
+Code:
+{code}
+```
+
+Parsing with tags:
+
+```python
+import re
+import json
+
+def extract_tagged_json(response: str, tag: str = "analysis") -> dict:
+    """Extract JSON from tagged output."""
+    pattern = rf"<{tag}>\s*(.*?)\s*</{tag}>"
+    match = re.search(pattern, response, re.DOTALL)
+
+    if not match:
+        raise ValueError(f"No <{tag}> tags found in response")
+
+    return json.loads(match.group(1))
+```
+
+---
+
+## JSON Mode (Claude & OpenAI)
+
+### Claude JSON Mode
+
+```python
+import anthropic
+
+client = anthropic.Anthropic()
+
+response = client.messages.create(
+    model="claude-opus-4-5-20251101",
+    max_tokens=1024,
+    messages=[
+        {
+            "role": "user",
+            "content": """Extract entities from this text and return as JSON.
+
+            Required fields:
+            - people: array of {name, role}
+            - organizations: array of {name, type}
+            - locations: array of strings
+
+            Text: {text}"""
+        }
+    ],
+    # Claude uses system prompt to enforce JSON
+    system="You are a JSON extraction assistant. Always respond with valid JSON only, no other text."
+)
+
+# Parse the response
+result = json.loads(response.content[0].text)
+```
+
+### OpenAI JSON Mode
+
+```python
+from openai import OpenAI
+
+client = OpenAI()
+
+response = client.chat.completions.create(
+    model="gpt-4-turbo-preview",
+    response_format={"type": "json_object"},  # Enforces JSON output
+    messages=[
+        {
+            "role": "system",
+            "content": "Extract information and return as JSON."
+        },
+        {
+            "role": "user",
+            "content": f"Extract people and companies from: {text}"
+        }
+    ]
+)
+
+result = json.loads(response.choices[0].message.content)
+```
+
+### JSON Mode Best Practices
+
+| Practice | Why |
+|----------|-----|
+| Always describe expected schema | Model needs to know structure |
+| Specify null handling | "Use null for missing fields" |
+| Define array behavior | "Return empty array if none found" |
+| Include field descriptions | Improves extraction accuracy |
+| Add type annotations | "date: string in YYYY-MM-DD format" |
+
+---
+
+## Function Calling / Tool Use
+
+### Claude Tool Use
+
+```python
+import anthropic
+
+client = anthropic.Anthropic()
+
+# Define the tool schema
+tools = [
+    {
+        "name": "extract_contact",
+        "description": "Extract contact information from text",
+        "input_schema": {
+            "type": "object",
+            "properties": {
+                "name": {
+                    "type": "string",
+                    "description": "Full name of the person"
+                },
+                "email": {
+                    "type": "string",
+                    "description": "Email address"
+                },
+                "phone": {
+                    "type": "string",
+                    "description": "Phone number in E.164 format"
+                },
+                "company": {
+                    "type": "string",
+                    "description": "Company or organization name"
+                },
+                "title": {
+                    "type": "string",
+                    "description": "Job title or role"
+                }
+            },
+            "required": ["name"]
+        }
+    }
+]
+
+response = client.messages.create(
+    model="claude-opus-4-5-20251101",
+    max_tokens=1024,
+    tools=tools,
+    tool_choice={"type": "tool", "name": "extract_contact"},  # Force tool use
+    messages=[
+        {
+            "role": "user",
+            "content": f"Extract contact info from: {business_card_text}"
+        }
+    ]
+)
+
+# Get structured output from tool call
+for block in response.content:
+    if block.type == "tool_use":
+        contact = block.input  # Already parsed as dict
+        print(f"Name: {contact['name']}")
+        print(f"Email: {contact.get('email', 'N/A')}")
+```
+
+### OpenAI Function Calling
+
+```python
+from openai import OpenAI
+
+client = OpenAI()
+
+functions = [
+    {
+        "name": "analyze_sentiment",
+        "description": "Analyze sentiment of customer feedback",
+        "parameters": {
+            "type": "object",
+            "properties": {
+                "sentiment": {
+                    "type": "string",
+                    "enum": ["positive", "negative", "neutral", "mixed"]
+                },
+                "confidence": {
+                    "type": "number",
+                    "minimum": 0,
+                    "maximum": 1
+                },
+                "key_phrases": {
+                    "type": "array",
+                    "items": {"type": "string"},
+                    "description": "Phrases that indicate sentiment"
+                },
+                "topics": {
+                    "type": "array",
+                    "items": {"type": "string"},
+                    "description": "Main topics discussed"
+                }
+            },
+            "required": ["sentiment", "confidence"]
+        }
+    }
+]
+
+response = client.chat.completions.create(
+    model="gpt-4-turbo-preview",
+    messages=[
+        {"role": "user", "content": f"Analyze this feedback: {feedback}"}
+    ],
+    functions=functions,
+    function_call={"name": "analyze_sentiment"}  # Force specific function
+)
+
+# Parse function call
+fn_call = response.choices[0].message.function_call
+result = json.loads(fn_call.arguments)
+```
+
+---
+
+## Schema Design Patterns
+
+### Enum Constraints
+
+```json
+{
+  "type": "object",
+  "properties": {
+    "priority": {
+      "type": "string",
+      "enum": ["critical", "high", "medium", "low"],
+      "description": "Issue priority level"
+    },
+    "status": {
+      "type": "string",
+      "enum": ["open", "in_progress", "blocked", "resolved", "closed"]
+    },
+    "category": {
+      "type": "string",
+      "enum": ["bug", "feature", "improvement", "documentation"]
+    }
+  }
+}
+```
+
+### Nested Objects
+
+```json
+{
+  "type": "object",
+  "properties": {
+    "order": {
+      "type": "object",
+      "properties": {
+        "id": {"type": "string"},
+        "total": {"type": "number"},
+        "currency": {"type": "string", "enum": ["USD", "EUR", "GBP"]},
+        "items": {
+          "type": "array",
+          "items": {
+            "type": "object",
+            "properties": {
+              "product_id": {"type": "string"},
+              "name": {"type": "string"},
+              "quantity": {"type": "integer", "minimum": 1},
+              "unit_price": {"type": "number", "minimum": 0}
+            },
+            "required": ["product_id", "quantity", "unit_price"]
+          }
+        },
+        "shipping_address": {
+          "$ref": "#/definitions/address"
+        }
+      },
+      "required": ["id", "items"]
+    }
+  },
+  "definitions": {
+    "address": {
+      "type": "object",
+      "properties": {
+        "street": {"type": "string"},
+        "city": {"type": "string"},
+        "state": {"type": "string"},
+        "postal_code": {"type": "string"},
+        "country": {"type": "string", "pattern": "^[A-Z]{2}$"}
+      },
+      "required": ["street", "city", "country"]
+    }
+  }
+}
+```
+
+### Conditional Fields
+
+```json
+{
+  "type": "object",
+  "properties": {
+    "contact_method": {
+      "type": "string",
+      "enum": ["email", "phone", "mail"]
+    },
+    "email": {"type": "string", "format": "email"},
+    "phone": {"type": "string"},
+    "address": {"$ref": "#/definitions/address"}
+  },
+  "required": ["contact_method"],
+  "allOf": [
+    {
+      "if": {"properties": {"contact_method": {"const": "email"}}},
+      "then": {"required": ["email"]}
+    },
+    {
+      "if": {"properties": {"contact_method": {"const": "phone"}}},
+      "then": {"required": ["phone"]}
+    },
+    {
+      "if": {"properties": {"contact_method": {"const": "mail"}}},
+      "then": {"required": ["address"]}
+    }
+  ]
+}
+```
+
+---
+
+## Validation and Error Handling
+
+### Pydantic Validation (Python)
+
+```python
+from pydantic import BaseModel, Field, validator
+from typing import Optional, List
+from enum import Enum
+
+class Severity(str, Enum):
+    CRITICAL = "critical"
+    HIGH = "high"
+    MEDIUM = "medium"
+    LOW = "low"
+
+class CodeIssue(BaseModel):
+    severity: Severity
+    type: str = Field(..., pattern="^(bug|security|performance|style)$")
+    location: str
+    description: str = Field(..., min_length=10, max_length=500)
+    suggestion: Optional[str] = None
+
+    @validator('location')
+    def validate_location(cls, v):
+        if ':' not in v and '(' not in v:
+            raise ValueError('Location must be file:line or function()')
+        return v
+
+class CodeAnalysis(BaseModel):
+    summary: str = Field(..., max_length=200)
+    issues: List[CodeIssue]
+    quality_score: int = Field(..., ge=1, le=10)
+
+    @validator('issues')
+    def critical_issues_first(cls, v):
+        return sorted(v, key=lambda x: list(Severity).index(x.severity))
+
+# Usage
+def parse_analysis(llm_response: str) -> CodeAnalysis:
+    """Parse and validate LLM response."""
+    try:
+        data = json.loads(llm_response)
+        return CodeAnalysis(**data)
+    except json.JSONDecodeError as e:
+        raise ValueError(f"Invalid JSON: {e}")
+    except ValidationError as e:
+        raise ValueError(f"Schema validation failed: {e}")
+```
+
+### Zod Validation (TypeScript)
+
+```typescript
+import { z } from 'zod';
+
+const SeveritySchema = z.enum(['critical', 'high', 'medium', 'low']);
+
+const CodeIssueSchema = z.object({
+  severity: SeveritySchema,
+  type: z.enum(['bug', 'security', 'performance', 'style']),
+  location: z.string().regex(/[:()]/, 'Must be file:line or function()'),
+  description: z.string().min(10).max(500),
+  suggestion: z.string().optional(),
+});
+
+const CodeAnalysisSchema = z.object({
+  summary: z.string().max(200),
+  issues: z.array(CodeIssueSchema),
+  quality_score: z.number().int().min(1).max(10),
+});
+
+type CodeAnalysis = z.infer<typeof CodeAnalysisSchema>;
+
+function parseAnalysis(llmResponse: string): CodeAnalysis {
+  const data = JSON.parse(llmResponse);
+  return CodeAnalysisSchema.parse(data);
+}
+```
+
+### Retry with Correction
+
+```python
+def get_structured_output(
+    prompt: str,
+    schema: dict,
+    max_retries: int = 3
+) -> dict:
+    """Get structured output with automatic retry on validation failure."""
+
+    for attempt in range(max_retries):
+        response = llm.complete(prompt)
+
+        try:
+            data = json.loads(response)
+            validate(data, schema)  # JSON Schema validation
+            return data
+        except json.JSONDecodeError as e:
+            error_msg = f"Invalid JSON at position {e.pos}: {e.msg}"
+        except ValidationError as e:
+            error_msg = format_validation_error(e)
+
+        # Retry with error feedback
+        if attempt < max_retries - 1:
+            prompt = f"""Your previous response had an error:
+{error_msg}
+
+Please fix and try again. Return only valid JSON matching the schema.
+
+Original request:
+{prompt}"""
+
+    raise ValueError(f"Failed to get valid output after {max_retries} attempts")
+```
+
+---
+
+## Complex Extraction Patterns
+
+### Multi-Entity Extraction
+
+```
+Extract all entities from the following document.
+
+Return JSON with this structure:
+{
+  "people": [
+    {
+      "name": "full name",
+      "aliases": ["nicknames or alternate names"],
+      "role": "their role/position if mentioned",
+      "mentioned_with": ["names of people they're associated with"]
+    }
+  ],
+  "organizations": [
+    {
+      "name": "organization name",
+      "type": "company|nonprofit|government|education|other",
+      "location": "headquarters if mentioned"
+    }
+  ],
+  "events": [
+    {
+      "name": "event name",
+      "date": "YYYY-MM-DD or null",
+      "location": "where it happened",
+      "participants": ["people or organizations involved"]
+    }
+  ],
+  "relationships": [
+    {
+      "entity1": "name",
+      "entity2": "name",
+      "type": "works_at|acquired|partnered|competed|invested",
+      "details": "additional context"
+    }
+  ]
+}
+
+Document:
+{document}
+```
+
+### Hierarchical Data Extraction
+
+```
+Parse this organizational structure and return as JSON:
+
+{
+  "organization": {
+    "name": "company name",
+    "departments": [
+      {
+        "name": "department name",
+        "head": "department head name",
+        "teams": [
+          {
+            "name": "team name",
+            "lead": "team lead name",
+            "members": ["member names"],
+            "responsibilities": ["key responsibilities"]
+          }
+        ]
+      }
+    ]
+  }
+}
+
+Text:
+{org_description}
+```
+
+### Form Data Extraction
+
+```
+Extract form data from this image/document.
+
+Return JSON:
+{
+  "form_type": "detected form type",
+  "fields": {
+    "field_name": {
+      "value": "extracted value",
+      "confidence": 0.0-1.0,
+      "location": "where on form (if applicable)"
+    }
+  },
+  "checkboxes": {
+    "checkbox_label": true/false
+  },
+  "signatures": [
+    {
+      "signer": "name if readable",
+      "date": "date if present",
+      "location": "signature location on form"
+    }
+  ],
+  "missing_fields": ["fields that appear required but are empty"]
+}
+
+Document content:
+{document}
+```
+
+---
+
+## Performance Optimization
+
+### Batch Processing
+
+```python
+async def extract_structured_batch(
+    items: list,
+    schema: dict,
+    batch_size: int = 10
+) -> list:
+    """Process multiple items efficiently."""
+    results = []
+
+    for i in range(0, len(items), batch_size):
+        batch = items[i:i+batch_size]
+
+        # Create batch prompt
+        prompt = f"""Extract information from each item below.
+Return a JSON array with one object per item, matching this schema:
+{json.dumps(schema, indent=2)}
+
+Items:
+{json.dumps([{"index": j, "content": item} for j, item in enumerate(batch)])}
+
+Response (JSON array only):"""
+
+        response = await llm.complete_async(prompt)
+        batch_results = json.loads(response)
+        results.extend(batch_results)
+
+    return results
+```
+
+### Schema Simplification
+
+**Overly complex schema (expensive):**
+```json
+{
+  "analysis": {
+    "sentiment": {
+      "overall": {"score": -1 to 1, "label": "string"},
+      "aspects": [{"aspect": "string", "sentiment": {...}}]
+    },
+    "entities": [...],
+    "topics": [...],
+    "summary": {...}
+  }
+}
+```
+
+**Simplified schema (cost-effective):**
+```json
+{
+  "sentiment": "positive|negative|neutral",
+  "confidence": 0.0-1.0,
+  "key_points": ["string"]
+}
+```
+
+---
+
+## Common Pitfalls
+
+| Pitfall | Problem | Solution |
+|---------|---------|----------|
+| No schema in prompt | Model invents structure | Always specify expected schema |
+| Ambiguous field names | Inconsistent extraction | Use descriptive names with examples |
+| Missing null handling | Errors on optional fields | Explicitly state "null if not found" |
+| Complex nested schemas | Inconsistent output | Flatten when possible |
+| No validation | Silent failures | Always validate with Pydantic/Zod |
+| Large schemas | Token waste, confusion | Split into multiple calls |
+
+---
+
+## Related Skills
+
+- **API Designer** - Schema design for APIs
+- **Data Engineer** - Data validation pipelines
+- **RAG Architect** - Structured extraction for retrieval