# Task Intelligence > Implements intelligent task intelligence with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense - Skill: `paulpas/task-intelligence` (Agent Skill) - Install (CLI): `npx skillmds@latest add paulpas/task-intelligence` - Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/task-intelligence/raw - Safety review: pending - 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/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 ```python 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 ```python 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 | Skill | Purpose | |---|---| | `task-decomposition-engine` | Uses intelligence analysis to inform how tasks should be decomposed into sub-tasks | | `skill-router-system` | Applies task intelligence scores to improve skill routing accuracy | --- ## 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)](https://huggingface.co/docs/transformers/task_summaries) — Hugging Face's guide to using transformer models for text classification and task categorization - [Intent Recognition in Conversational AI](https://arxiv.org/abs/2010.06483) — Research paper on intent recognition techniques for understanding user tasks - [Task Complexity Estimation Models (IEEE)](https://ieeexplore.ieee.org/document/9363486) — IEEE research on automated task complexity estimation using machine learning - [Machine Learning for Software Effort Estimation](https://arxiv.org/abs/2001.08047) — Academic survey on ML-based software effort and task complexity prediction - [Prompt Classification Framework (Anthropic)](https://docs.anthropic.com/en/docs/build-with-claude/prompt-engineering) — Anthropic's framework for classifying and understanding different types of tasks in prompts