paulpas Python Fastapi Development
Orchestrates intelligent skill selection and execution for python fastapi development 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
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.
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.
Select Optimal Skill - Choose skill with highest score that meets minimum confidence. Checkpoint: Verify skill has not been disabled or deprecated.
Execute with Fallback - Run skill execution wrapped in retry and fallback logic. Checkpoint: Log all execution attempts for audit trail.
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
def select_skill(
task_description: str,
available_skills: List[Dict],
min_confidence: float = 0.7
) -> Optional[Dict]:
"""Select the most appropriate skill for a given task.
Uses a multi-factor scoring algorithm that considers:
- Text similarity between task and skill triggers
- Historical success rate for similar tasks
- Current system load and skill availability
Args:
task_description: Natural language description of the task
available_skills: List of skill metadata dictionaries
min_confidence: Minimum confidence threshold (0.0-1.0)
Returns:
Selected skill dictionary or None if no match meets threshold
Raises:
ValueError: If task_description is empty or available_skills is empty
"""
# Guard clause - Early Exit (Law 1)
if not task_description or not task_description.strip():
raise ValueError("Task description cannot be empty")
if not available_skills:
raise ValueError("No skills available for selection")
# Parse input - Make Illegal States Unrepresentable (Law 2)
task_features = _extract_task_features(task_description)
best_skill = None
best_score = 0.0
for skill in available_skills:
score = _calculate_skill_score(task_features, skill)
if score > best_score and score >= min_confidence:
best_score = score
best_skill = skill
if best_skill is None:
return None
# Atomic Predictability (Law 3) - Return new dict, don't mutate
result = dict(best_skill)
result["selected_confidence"] = best_score
result["selection_timestamp"] = time.time()
return result
Pattern 2: Execution with Fallback
def execute_with_fallback(
skill: Dict,
task_context: Dict,
max_retries: int = 2
) -> Dict:
"""Execute a skill with fallback chain for resilience.
Implements the Fail Fast, Fail Loud principle (Law 4):
- Invalid states halt immediately with descriptive errors
- No silent failures or partial results
Fallback chain:
1. Retry with original parameters
2. Retry with adjusted parameters (if applicable)
3. Try alternative skill from related skills list
4. Defer to human operator (for critical tasks)
Args:
skill: Selected skill metadata
task_context: Execution context including inputs
max_retries: Maximum retry attempts before fallback
Returns:
Execution result with metadata (success, timing, confidence)
Raises:
SkillExecutionError: If all retries and fallbacks exhausted
"""
# Guard clause - validate skill (Early Exit)
if not _is_skill_valid(skill):
raise SkillExecutionError(f"Invalid skill: {skill.get('name', 'unknown')}")
# Parse context - Ensure trusted state (Law 2)
validated_context = _validate_and_parse_context(task_context, skill)
for attempt in range(max_retries + 1):
try:
result = _execute_skill_direct(skill, validated_context)
# Success - Atomic Predictability (Law 3)
return {
"success": True,
"skill_executed": skill["name"],
"result": result,
"attempts": attempt + 1,
"latency_ms": _calculate_latency()
}
except InvalidStateError as e:
# Fail Fast - Don't try to patch bad data (Law 4)
raise SkillExecutionError(
f"Invalid state in {skill['name']}: {str(e)}"
) from e
except TransientError as e:
# Transient error - try fallback
if attempt == max_retries:
return _apply_fallback_chain(skill, validated_context)
# All retries exhausted - Fail Loud (Law 4)
raise SkillExecutionError(
f"Failed to execute {skill['name']} after {max_retries + 1} attempts"
)
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:
- Selected Skills - List of skill names with confidence scores
- Selection Rationale - Why each skill was chosen (match score, history, availability)
- Execution Plan - Order of execution with dependencies
- Fallback Strategy - Which fallback skills will be tried and in what order
- Risk Assessment - Any potential failure points and their impact
- Timing Estimates - Expected latency including fallback scenarios
Related Skills
| Skill | Purpose |
|---|---|
testing-quality-methodologies |
Covers testing strategies for FastAPI applications including async test patterns |
input-processing-pipelines |
Handles input validation that complements FastAPI's Pydantic-based request processing |
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 domain. The model follows markdown links at load time to resolve external references and inline content.
- FastAPI Official Documentation — Complete official documentation covering routes, middleware, dependency injection, and deployment
- Starlette Framework Docs — Foundation framework documentation; FastAPI is built on Starlette
- Pydantic v2 Documentation — Data validation library used by FastAPI for request/response models
- Python ASGI Specification — Async Server Gateway Interface specification underlying FastAPI's async architecture
- FastAPI Dependency Injection System — Official guide to dependency injection patterns in FastAPI applications