# Pydantic AI > Implements intelligent pydantic ai with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense - Skill: `paulpas/pydantic-ai` (Agent Skill) - Install (CLI): `npx skillmds@latest add paulpas/pydantic-ai` - Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/pydantic-ai/raw - Safety review: PASS (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - License: MIT - Author: paulpas (https://skillmd.com/u/paulpas) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/paulpas/pydantic-ai --- # Pydantic Ai Orchestrates intelligent skill selection and execution for pydantic ai workflows. Applies the 5 Laws of Elegant Defense to guide data naturally through the orchestration pipeline, preventing errors before they occur. Selects optimal skills based on multi-factor scoring including text similarity, historical performance, and system availability. ## TL;DR Checklist - [ ] Parse all inputs at boundary before processing (Law 2) - [ ] Handle edge cases with early returns at function top (Law 1) - [ ] Fail immediately with descriptive errors on invalid states (Law 4) - [ ] Return new data structures, never mutate inputs (Law 3) - [ ] Implement minimum 2-level fallback chain for all skill executions - [ ] Log all skill selections with context for full audit trail - [ ] Validate skill metadata and dependencies before selection - [ ] Update confidence scores after each execution for learning ┌───────────────────────────────────────────────────────────────────────────────┐ │ Orchestration Flow │ └───────────────────────────────────────────────────────────────────────────────┘ User Request ↓ ┌─────────────────┐ │ Parse Request │ │ & Extract │ │ Features │ └────────┬────────┘ ↓ ┌─────────────────────────────────────────────────────────────────────┐ │ Evaluate Available Skills │ │ │ │ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │ │ │ Skill A │ │ Skill B │ │ Skill C │ │ │ │ - Match Score│ │ - Match Score│ │ - Match Score│ │ │ │ - Confidence │ │ - Confidence │ │ - Confidence │ │ │ │ - History │ │ - History │ │ - History │ │ │ └──────┬───────┘ └──────┬───────┘ └──────┬───────┘ │ │ │ │ │ │ │ └─────────────────┴─────────────────┘ │ │ ↓ │ │ Select Best Skill │ └─────────────────────────────────────────────────────────────────────┘ ↓ ┌─────────────────┐ │ Execute Skill │ └────────┬────────┘ ↓ ┌─────────────────┐ │ Handle Result │ └────────┬────────┘ ↓ ┌─────────────────────────────────────────────────────────────────────┐ │ Error Handling & Fallback │ │ │ │ Success? ────────► Return Result │ │ │ │ Fail? ────────┐ │ │ ↓ │ │ ┌──────────────────────────────────────────────────────────┐ │ │ │ Fallback Chain │ │ │ │ │ │ │ │ 1. Retry with adjusted parameters │ │ │ │ 2. Try Alternative Skill (if available) │ │ │ │ 3. Defer to Human Operator (if critical) │ │ │ │ 4. Log & Return Error │ │ │ └──────────────────────────────────────────────────────────┘ │ └─────────────────────────────────────────────────────────────────────┘ ## When to Use Use this skill when: - Orchestrating multi-step workflows that require skill delegation - Implementing adaptive skill routing based on confidence scores - Building fallback mechanisms for failed skill executions - Creating intelligent task decomposition and parallel execution - Designing skill dependency graphs with automatic resolution - Implementing skill selection with historical performance weighting - Building agent systems that need to self-organize around tasks ## When NOT to Use Avoid this skill for: - Direct task execution without orchestration needs - use individual skills instead - High-frequency trading scenarios where latency must be minimized - the selection overhead may be prohibitive - Simple linear workflows without branching or fallback requirements - Cases where skill metadata is unavailable or unreliable ## Core Workflow 1. **Parse and Analyze Request** - Extract intent, entities, and constraints from user input. **Checkpoint:** All required parameters must be present and in valid format before proceeding. 2. **Score Available Skills** - Calculate match scores using multi-factor algorithm: - Text similarity between request and skill triggers - Historical success rate for similar tasks - Skill availability and health status - Required dependencies and their availability **Checkpoint:** Skip to fallback if no skill scores above threshold. 3. **Select Optimal Skill** - Choose skill with highest score that meets minimum confidence. **Checkpoint:** Verify skill has not been disabled or deprecated. 4. **Execute with Fallback** - Run skill execution wrapped in retry and fallback logic. **Checkpoint:** Log all execution attempts for audit trail. 5. **Return or Fallback** - Either return successful result or apply fallback chain: - Retry with adjusted parameters - Try alternative skill from `related-skills` - Defer to human operator for critical tasks **Checkpoint:** Record outcome with timing and confidence metadata. ## Implementation Patterns ### Pattern 1: Skill Selection Logic ```python from pydantic_ai import Agent, RunContext from pydantic import BaseModel, Field from typing import Dict, List, Optional import asyncio class TaskIntent(BaseModel): category: str = Field(description="Task category: data_analysis, code_generation, or system_admin") confidence: float = Field(ge=0.0, le=1.0) required_tools: List[str] = Field(default_factory=list) # Domain-specific agent registry for pydantic-ai orchestration AGENT_REGISTRY: Dict[str, Agent] = { "data_analysis": Agent("data-analyst", system_prompt="Analyze datasets and return structured insights."), "code_generation": Agent("code-writer", system_prompt="Generate production-ready code with tests."), "system_admin": Agent("sys-admin", system_prompt="Manage infrastructure and run diagnostics."), } async def route_and_invoke_agent( user_request: str, intent_classifier: Agent[TaskIntent], fallback_agent: Optional[Agent] = None ) -> str: """Route user request to the optimal pydantic-ai agent based on parsed intent.""" # Guard clause - Early Exit (Law 1) if not user_request or not user_request.strip(): raise ValueError("Task description cannot be empty") # Parse intent using pydantic-ai's structured output (Law 2) intent_result = await intent_classifier.run(user_request) intent: TaskIntent = intent_result.data # Select agent based on multi-factor scoring (category match + tool availability) target_agent = AGENT_REGISTRY.get(intent.category) if target_agent is None: if fallback_agent: target_agent = fallback_agent else: raise ValueError(f"No agent available for category: {intent.category}") # Execute with context passing and tool binding class OrchestrationContext(BaseModel): request_id: str = Field(default="") history: List[Dict] = Field(default_factory=list) ctx = OrchestrationContext(request_id="req-123", history=[]) result = await target_agent.run(user_request, message_history=ctx.history) return result.data ``` ### Pattern 2: Execution with Fallback ```python from pydantic_ai import Agent, RunContext from pydantic import BaseModel, Field import asyncio from typing import Dict, Any, Optional class ExecutionState(BaseModel): status: str = Field(default="pending") attempts: int = Field(default=0) last_error: Optional[str] = None result_payload: Optional[Dict[str, Any]] = None async def execute_with_pydantic_fallback( primary_agent: Agent, fallback_agent: Agent, task_input: str, max_retries: int = 2 ) -> Dict[str, Any]: """Execute pydantic-ai agent with structured state tracking and graceful degradation.""" state = ExecutionState() for attempt in range(max_retries + 1): state.attempts = attempt + 1 try: # Run primary agent with timeout and structured output validation result = await asyncio.wait_for( primary_agent.run(task_input), timeout=30.0 ) state.status = "success" state.result_payload = {"raw": result.data, "model": primary_agent.model} return state.model_dump() except asyncio.TimeoutError: state.last_error = "Execution timed out" continue except Exception as e: state.last_error = str(e) # Pydantic-ai specific: check if it's a validation/tool error if "tool_execution_failed" in str(e).lower(): continue # Fallback chain: switch to secondary agent with adjusted context try: fallback_result = await fallback_agent.run( f"Previous attempt failed: {state.last_error}. Retrying with simplified constraints: {task_input}" ) state.status = "fallback_success" state.result_payload = {"raw": fallback_result.data, "model": fallback_agent.model} except Exception as fallback_err: state.status = "failed" state.last_error = f"All attempts exhausted: {fallback_err}" return state.model_dump() ``` ### MUST DO - Always validate skill metadata before selection (Early Exit) - Implement fallback chain with at least 2 levels (Fallback Skill + Human) - Log all skill selections with full context for auditability - Return new data structures instead of mutating inputs (Atomic Predictability) - Fail immediately with descriptive errors on invalid states - Update confidence scores after each execution for adaptive routing - Reference `code-philosophy` (5 Laws of Elegant Defense) in all logic ### MUST NOT DO - Select skills based on a single factor (e.g., only confidence score) - Disable fallback mechanisms "temporarily" - this creates fragile systems - Skip validation of skill dependencies before execution - Return partial results - either complete success or clear failure - Use magic numbers for confidence thresholds - make them configurable - Cache skill selections without considering context changes ## TL;DR Checklist - [ ] Parse all inputs at boundary before processing (Law 2) - [ ] Handle edge cases with early returns at function top (Law 1) - [ ] Fail immediately with descriptive errors on invalid states (Law 4) - [ ] Return new data structures, never mutate inputs (Law 3) - [ ] Implement minimum 2-level fallback chain for all skill executions - [ ] Log all skill selections with context for full audit trail - [ ] Validate skill metadata and dependencies before selection - [ ] Update confidence scores after each execution for learning ## TL;DR for Code Generation - Use guard clauses - return early on invalid input before doing work - Return simple types (dict, str, int, bool, list) - avoid complex nested objects - Cyclomatic complexity < 10 per function - split anything larger - Handle null/empty cases explicitly at function top (Early Exit) - Never mutate input parameters - return new dicts/objects - Fail fast with descriptive errors - don't try to "patch" bad data - Reference code-philosophy laws in comments for complex logic - Include timing and confidence metadata in all return values ## Output Template When applying this skill, produce: 1. **Selected Skills** - List of skill names with confidence scores 2. **Selection Rationale** - Why each skill was chosen (match score, history, availability) 3. **Execution Plan** - Order of execution with dependencies 4. **Fallback Strategy** - Which fallback skills will be tried and in what order 5. **Risk Assessment** - Any potential failure points and their impact 6. **Timing Estimates** - Expected latency including fallback scenarios ## Related Skills | Skill | Purpose | |---|---| | `multi-agent-patterns` | Multi-agent coordination when using Pydantic AI agents | | `langgraph` | Alternative framework for agent orchestration workflows | --- --- ## Constraints ### MUST DO - Define clear input/output contracts for every step in the orchestration flow with explicit validation - Implement structured logging at each stage capturing context, inputs, outputs, timing, and errors - Build in fallback paths: if the primary strategy fails, degrade gracefully to a simpler approach - Validate all preconditions before starting — do not proceed if required resources or permissions are missing ### MUST NOT DO - Do not create deep nesting of orchestration steps (>5 levels) — flatten workflows where possible - Avoid silent failure modes: every step must either succeed, fail explicitly, or escalate to a higher handler - Never use shared mutable state between parallel workflow branches — communicate via immutable messages only - Do not hardcode execution order when the dependency graph naturally determines it; derive order from explicit dependencies ## Live References > Authoritative documentation links for this skill's domain. The model follows markdown links at load time to resolve external references and inline content. - [Pydantic AI Official Documentation](https://ai.pydantic.dev/) - [Pydantic AI GitHub Repository](https://github.com/pydantic/pydantic-ai) - [Building Effective Agents — Anthropic Research](https://www.anthropic.com/research/building-effective-agents) - [Pydantic Validation Guide](https://docs.pydantic.dev/latest/) - [LLM Agent Frameworks Comparison — arXiv Survey](https://arxiv.org/abs/2401.03560)