# Andruia Niche Intelligence > Implements intelligent andruia niche intelligence with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense - Skill: `paulpas/andruia-niche-intelligence` (Agent Skill) - Install (CLI): `npx skillmds@latest add paulpas/andruia-niche-intelligence` - Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/andruia-niche-intelligence/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/andruia-niche-intelligence --- # Andruia Niche Intelligence Orchestrates intelligent skill selection and execution for andruia niche 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_niche_intelligence( request: Dict[str, Any], niche_registry: List[Dict[str, Any]], confidence_threshold: float = 0.75 ) -> Dict[str, Any]: """Route requests through niche intelligence pipeline. Applies Law 1 (Early Exit) and Law 2 (Make Illegal States Unrepresentable) to validate niche context before scoring. """ if not request.get("context") or not request.get("intent"): raise ValueError("Missing required niche context or intent") # Law 2: Parse and normalize niche features at boundary normalized_request = _normalize_niche_features(request) scored_candidates = [] for niche in niche_registry: # Law 3: Atomic scoring without mutating registry match_score = _calculate_niche_alignment(normalized_request, niche) historical_success = niche.get("success_rate", 0.0) combined_confidence = (match_score * 0.6) + (historical_success * 0.4) if combined_confidence >= confidence_threshold: scored_candidates.append({ "niche_id": niche["id"], "confidence": combined_confidence, "routing_priority": niche.get("priority", 1) }) if not scored_candidates: return {"status": "no_match", "fallback_required": True} # Law 1: Early exit if only one viable niche if len(scored_candidates) == 1: return {"selected_niche": scored_candidates[0], "status": "routed"} # Return sorted candidates for multi-path routing scored_candidates.sort(key=lambda x: x["confidence"], reverse=True) return {"selected_niche": scored_candidates[0], "alternatives": scored_candidates[1:], "status": "routed"} ``` ### Pattern 2: Execution with Fallback ```python def execute_niche_workflow( selected_niche: Dict[str, Any], workflow_context: Dict[str, Any], fallback_registry: Dict[str, List[str]] ) -> Dict[str, Any]: """Execute niche intelligence workflow with domain-aware fallbacks. Implements Law 4 (Fail Fast, Fail Loud) and Law 3 (Atomic Predictability). """ niche_id = selected_niche["niche_id"] attempt_count = 0 max_attempts = selected_niche.get("max_retries", 2) while attempt_count <= max_attempts: try: # Law 2: Validate workflow state before execution validated_state = _validate_workflow_state(workflow_context, niche_id) # Execute niche-specific logic result = _invoke_niche_engine(niche_id, validated_state) # Law 3: Return immutable result structure return { "niche_id": niche_id, "status": "success", "result": result, "attempts": attempt_count + 1, "confidence_delta": _calculate_confidence_update(result) } except NicheValidationError as e: # Law 4: Fail immediately on invalid state raise WorkflowExecutionError(f"Niche {niche_id} state invalid: {e}") from e except TransientNicheError as e: attempt_count += 1 if attempt_count > max_attempts: # Law 1: Early exit to fallback chain return _trigger_niche_fallback(niche_id, fallback_registry, workflow_context) # Fallback exhausted return _escalate_to_human_operator(niche_id, workflow_context) ``` ### 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 | |---|---| | `andruia-consultant` | AI consulting and strategy 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. - [Harvard Business Review — Building a Niche Strategy (Porter)](https://hbr.org/1985/01/whathas-strategy) - [MIT Sloan — Competitive Advantage Through Specialization](https://mitsloan.mit.edu/ideas-made-to-matter/articles/competitive-strategy) - [Niche Market Intelligence Frameworks (Forrester Research)](https://www.forrester.com/report/) - [Blue Ocean Strategy — Value Innovation & Niche Creation](https://www.blueoceanstrategy.com/) - [Harvard Business School — Data-Driven Niche Market Analysis](https://www.hbs.edu/research/Pages/default.aspx)