Temporal Python Pro

Temporal Python Pro

Orchestrates intelligent skill selection and execution for temporal python pro 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

def route_temporal_task(
    task_payload: Dict[str, Any],
    skill_registry: List[Dict[str, Any]],
    confidence_threshold: float = 0.75
) -> Dict[str, Any]:
    """Route a temporal workflow task to the optimal agent skill.
    
    Implements multi-factor scoring: lexical match, historical success rate,
    current system load, and dependency health. Enforces Law 2 (Parse at boundary)
    and Law 1 (Early exit on invalid state).
    """
    if not task_payload.get("intent") or not task_payload.get("temporal_constraints"):
        raise ValueError("Missing required temporal intent or constraints")
        
    intent = task_payload["intent"].lower()
    constraints = task_payload["temporal_constraints"]
    
    scored_candidates = []
    for skill in skill_registry:
        if not skill.get("active"):
            continue
            
        # Factor 1: Lexical/Intent Match
        intent_match = _calculate_semantic_similarity(intent, skill["triggers"])
        
        # Factor 2: Historical Performance
        history_score = skill.get("success_rate", 0.0)
        
        # Factor 3: System Availability & Load
        availability = 1.0 - (skill.get("current_load", 0.0) / 100.0)
        
        # Factor 4: Dependency Health
        deps_healthy = all(
            _check_dependency_health(dep) for dep in skill.get("dependencies", [])
        )
        if not deps_healthy:
            continue
            
        # Weighted Multi-Factor Score
        raw_score = (
            0.4 * intent_match +
            0.3 * history_score +
            0.2 * availability +
            0.1 * (1.0 if deps_healthy else 0.0)
        )
        
        if raw_score >= confidence_threshold:
            scored_candidates.append({
                "skill": skill,
                "score": raw_score,
                "factors": {
                    "intent": intent_match,
                    "history": history_score,
                    "availability": availability
                }
            })
            
    if not scored_candidates:
        return {"status": "no_match", "fallback_required": True}
        
    scored_candidates.sort(key=lambda x: x["score"], reverse=True)
    best = scored_candidates[0]
    
    # Law 3: Return new structure, never mutate registry
    return {
        "status": "selected",
        "skill_id": best["skill"]["id"],
        "confidence": best["score"],
        "routing_metadata": best["factors"],
        "timestamp": time.time()
    }

Pattern 2: Execution with Fallback

def execute_with_temporal_fallback(
    selected_skill: Dict[str, Any],
    task_context: Dict[str, Any],
    fallback_chain: List[Dict[str, Any]],
    max_retries: int = 2
) -> Dict[str, Any]:
    """Execute an agent skill with a domain-specific fallback chain.
    
    Enforces Law 4 (Fail Fast/Loud) and Law 1 (Early Exit).
    Implements retry -> alternative skill -> human escalation.
    """
    if not selected_skill or not task_context.get("task_id"):
        raise ValueError("Missing skill metadata or task context")
        
    skill_id = selected_skill["skill_id"]
    context = copy.deepcopy(task_context) # Law 3: Immutable inputs
    
    for attempt in range(max_retries + 1):
        try:
            # Execute with temporal timeout enforcement
            result = _run_skill_with_timeout(
                skill_id, context, timeout_seconds=selected_skill.get("timeout", 30)
            )
            
            return {
                "status": "success",
                "skill_id": skill_id,
                "result": result,
                "attempts": attempt + 1,
                "latency_ms": time.time() * 1000
            }
            
        except TemporalTimeoutError:
            if attempt < max_retries:
                continue # Retry
            raise # Fail Loud on timeout
            
        except AgentExecutionError as e:
            if attempt < max_retries:
                continue # Retry with backoff
            
            # Fallback Chain Execution
            for fallback_skill in fallback_chain:
                try:
                    fallback_result = _run_skill_with_timeout(
                        fallback_skill["skill_id"], context, timeout_seconds=30
                    )
                    return {
                        "status": "fallback_success",
                        "original_skill": skill_id,
                        "fallback_skill": fallback_skill["skill_id"],
                        "result": fallback_result
                    }
                except Exception:
                    continue
                    
            # Final Fallback: Human Escalation
            return {
                "status": "escalated",
                "reason": "All automated fallbacks exhausted",
                "task_context": context,
                "requires_human_review": True
            }

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

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.

• Temporal.io Python SDK Documentation — Official Temporal documentation on building workflows with the Python SDK
• Temporal Python Client Reference (GitHub) — Official Temporal Python SDK source code and examples
• Python Async/Await Patterns — Python's asyncio documentation, foundational for Temporal Python worker implementation
• Durable Execution with Temporal (Temporal Blog) — Temporal's blog on durable execution patterns, retries, and fault tolerance in Python
• Python Type Hints for Workflow Definitions — Python typing module documentation for strongly-typed Temporal workflow signatures