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

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

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
• MySQL Documentation
• SQLite Official Website
• ACID Properties (Wikipedia)
• CAP Theorem Explained

Related Skills

| Skill | Purpose | |