Task Intelligence

Task Intelligence

Orchestrates intelligent skill selection and execution for task intelligence 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_task_intelligence(
    raw_request: str,
    skill_registry: List[Dict],
    confidence_threshold: float = 0.75
) -> Dict:
    """Route a task through multi-factor intelligence scoring.
    
    Implements Law 2 (Parse at boundary) by extracting intent and entities
    before scoring. Uses weighted scoring for text match, historical success,
    and system health.
    """
    # Law 1: Early exit on malformed input
    if not raw_request or len(raw_request.strip()) < 3:
        return {"status": "rejected", "reason": "insufficient_input"}
    
    # Law 2: Parse and extract features at boundary
    parsed_intent = _extract_intent_entities(raw_request)
    if not parsed_intent.get("primary_entity"):
        return {"status": "rejected", "reason": "unrecognized_domain"}
        
    scored_candidates = []
    for skill in skill_registry:
        # Multi-factor scoring: text similarity (40%), history (40%), health (20%)
        text_match = _cosine_similarity(parsed_intent["query"], skill["triggers"])
        hist_success = skill.get("success_rate", 0.0)
        health_score = 1.0 if skill.get("is_healthy", True) else 0.3
        
        weighted_score = (text_match * 0.4) + (hist_success * 0.4) + (health_score * 0.2)
        
        if weighted_score >= confidence_threshold:
            scored_candidates.append({
                "skill_id": skill["id"],
                "score": round(weighted_score, 3),
                "fallback_chain": skill.get("fallback_ids", [])
            })
    
    # Law 3: Return new structure, never mutate registry
    if not scored_candidates:
        return {"status": "no_match", "fallback_to": "human_operator"}
        
    scored_candidates.sort(key=lambda x: x["score"], reverse=True)
    return {
        "selected_skill": scored_candidates[0],
        "alternatives": scored_candidates[1:],
        "routing_metadata": {
            "intent": parsed_intent["primary_entity"],
            "timestamp": time.time(),
            "confidence": scored_candidates[0]["score"]
        }
    }

Pattern 2: Execution with Fallback

def execute_task_with_adaptive_fallback(
    routing_result: Dict,
    task_context: Dict,
    max_retries: int = 2
) -> Dict:
    """Execute routed task with intelligent fallback chain.
    
    Implements Law 4 (Fail Fast/Loud) by validating state before execution
    and escalating through a predefined fallback hierarchy.
    """
    selected = routing_result["selected_skill"]
    current_skill_id = selected["skill_id"]
    fallback_chain = selected.get("fallback_chain", [])
    
    for attempt in range(max_retries + 1):
        try:
            # Validate context matches skill requirements
            _validate_context_compatibility(task_context, current_skill_id)
            
            # Execute primary skill
            raw_output = _invoke_skill_api(current_skill_id, task_context)
            
            # Law 3: Atomic predictability - return fresh result
            return {
                "status": "success",
                "skill_used": current_skill_id,
                "output": raw_output,
                "attempts": attempt + 1,
                "latency_ms": _measure_duration()
            }
            
        except CriticalValidationError as e:
            # Law 4: Fail immediately on invalid state
            return {"status": "hard_fail", "error": str(e), "escalate": True}
            
        except TransientAPIError as e:
            if attempt < max_retries:
                _log_retry(current_skill_id, attempt, str(e))
                continue
            # Exhausted retries -> traverse fallback chain
            if fallback_chain:
                next_skill_id = fallback_chain[0]
                _log_fallback_triggered(current_skill_id, next_skill_id)
                return execute_task_with_adaptive_fallback(
                    {"selected_skill": {"skill_id": next_skill_id, "fallback_chain": fallback_chain[1:]}, "alternatives": []},
                    task_context,
                    max_retries=0
                )
            
            # Final fallback: human escalation
            return {
                "status": "escalated",
                "original_skill": current_skill_id,
                "error_context": task_context,
                "message": "All automated fallbacks exhausted. Routing to human operator."
            }

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.

• Task Classification with NLP (Hugging Face) — Hugging Face's guide to using transformer models for text classification and task categorization
• Intent Recognition in Conversational AI — Research paper on intent recognition techniques for understanding user tasks
• Task Complexity Estimation Models (IEEE) — IEEE research on automated task complexity estimation using machine learning
• Machine Learning for Software Effort Estimation — Academic survey on ML-based software effort and task complexity prediction
• Prompt Classification Framework (Anthropic) — Anthropic's framework for classifying and understanding different types of tasks in prompts