database

# Database Orchestrates intelligent skill selection and execution for database 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_database_operation( query: str, db_cluster: Dict[str, Any], operation_type: str = "auto" ) -> Dict[str, Any]: """Route database operations to optimal nodes based on query type and cluster health. Implements multi-factor routing for database workflows: - Parses query intent (SELECT, INSERT, UPDATE, DELETE, DDL) - Evaluates node health, replication lag, and connection pool status - Applies read/write splitting with automatic failover routing """ # Guard clause - validate query and cluster state if not query or not query.strip(): raise ValueError("Query cannot be empty") if not db_cluster.get("nodes"): raise ValueError("No database nodes available") # Parse query intent (Law 2 - Make illegal states unrepresentable) intent = _parse_query_intent(query) is_read = intent in ("SELECT", "SHOW", "DESCRIBE") # Evaluate nodes for routing candidates = [] for node in db_cluster["nodes"]: if node["status"] != "healthy": continue lag = node.get("replication_lag_ms", 0) if is_read and lag > db_cluster.get("max_read_lag_ms", 500): continue score = _calculate_node_score(node, is_read, db_cluster["load"]) candidates.append({"node": node, "score": score}) if not candidates: return {"fallback": "maintenance_mode", "reason": "no_healthy_nodes"} # Sort by score and select optimal node candidates.sort(key=lambda x: x["score"], reverse=True) selected = candidates[0]["node"] # Return routing decision with metadata (Law 3 - Atomic Predictability) return { "target_node": selected["id"], "operation_type": "read" if is_read else "write", "confidence": candidates[0]["score"], "routing_timestamp": time.time(), "query_intent": intent } ``` ### Pattern 2: Execution with Fallback ```python def execute_db_operation( routing_decision: Dict[str, Any], query: str, params: tuple = (), max_retries: int = 2 ) -> Dict[str, Any]: """Execute database operation with resilient connection handling and fallback routing. Implements database-specific fallback chain: 1. Retry with exponential backoff on transient connection errors 2. Failover to read replica or standby node on timeout/lock 3. Route to maintenance pool if primary is degraded 4. Return structured error with query context for debugging """ target_node = routing_decision["target_node"] operation = routing_decision["operation_type"] for attempt in range(max_retries + 1): try: # Establish connection with timeout (Law 4 - Fail Fast) conn = _acquire_connection(target_node, timeout=5.0) cursor = conn.cursor() # Execute with query timeout protection cursor.execute(query, params) result = cursor.fetchall() if operation == "read" else {"rows_affected": cursor.rowcount} # Close connection and return result (Law 3) cursor.close() conn.close() return { "success": True, "node": target_node, "result": result, "attempts": attempt + 1, "latency_ms": _measure_latency() } except ConnectionError as e: if attempt == max_retries: return _failover_to_standby(target_node, query, params) time.sleep(0.5 * (2 ** attempt)) except QueryTimeoutError as e: # Lock contention or slow query - trigger fallback routing return _reroute_with_backoff(target_node, query, params) return { "success": False, "error": "max_retries_exceeded", "query": query, "node": target_node } ``` ### 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 --- --- ## 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. - [PostgreSQL Documentation](<https://www.postgresql.org/docs/>) - [MySQL Documentation](<https://dev.mysql.com/doc/>) - [SQLite Official Website](<https://www.sqlite.org/index.html>) - [ACID Properties (Wikipedia)](<https://en.wikipedia.org/wiki/ACID>) - [CAP Theorem Explained](<https://en.wikipedia.org/wiki/CAP_theorem>) ## Related Skills | Skill | Purpose | |