Cc Skill Clickhouse Io

Cc Skill Clickhouse Io

Orchestrates intelligent skill selection and execution for cc skill clickhouse io 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_clickhouse_query(
    query: str,
    schema_registry: Dict[str, TableSchema],
    cluster_topology: List[Dict],
    min_query_score: float = 0.8
) -> Dict:
    """Route and validate ClickHouse queries against schema and cluster topology.
    
    Applies Law 2 (Make Illegal States Unrepresentable) by validating
    table existence, column types, and cluster health before execution.
    
    Args:
        query: Raw SQL query string
        schema_registry: Mapping of table_name -> TableSchema
        cluster_topology: List of available ClickHouse nodes with health/status
        min_query_score: Minimum routing confidence threshold
        
    Returns:
        Routing decision dict with target_node, execution_mode, and validation_status
    """
    # Law 1: Early exit on invalid input
    if not query or not query.strip().upper().startswith(("SELECT", "INSERT", "SYSTEM")):
        raise ValueError("Unsupported query type for ClickHouse IO pipeline")
        
    # Law 2: Parse and validate schema constraints
    parsed_tables = _extract_target_tables(query)
    for table in parsed_tables:
        if table not in schema_registry:
            raise ValueError(f"Table '{table}' not found in schema registry")
        _validate_query_against_schema(query, schema_registry[table])
        
    # Law 3: Atomic routing decision (no mutation of topology)
    healthy_nodes = [
        node for node in cluster_topology 
        if node["status"] == "online" and node["load_factor"] < 0.85
    ]
    
    if not healthy_nodes:
        return {"status": "degraded", "fallback": "read_replica_pool"}
        
    # Score nodes based on query type and load
    target_node = max(healthy_nodes, key=lambda n: _calculate_node_score(n, query))
    
    return {
        "target_node": target_node["address"],
        "execution_mode": "async_insert" if "INSERT" in query.upper() else "sync",
        "validation_passed": True,
        "routing_confidence": 0.95
    }

Pattern 2: Execution with Fallback

def execute_clickhouse_pipeline(
    routing_decision: Dict,
    query: str,
    clickhouse_client: ClickHouseClient,
    fallback_replicas: List[str] = None
) -> Dict:
    """Execute ClickHouse query with domain-specific fallback and error handling.
    
    Implements Law 4 (Fail Fast, Fail Loud) by catching ClickHouse-specific
    exception codes and routing to fallback replicas or retry queues.
    
    Args:
        routing_decision: Output from route_clickhouse_query
        query: Validated SQL query
        clickhouse_client: Initialized ClickHouse client instance
        fallback_replicas: List of replica addresses for failover
        
    Returns:
        Execution result with row counts, latency, and status metadata
    """
    target = routing_decision["target_node"]
    mode = routing_decision["execution_mode"]
    attempts = 0
    max_attempts = 3
    
    while attempts < max_attempts:
        try:
            # Law 3: Return new result structure, never mutate client state
            if mode == "async_insert":
                result = clickhouse_client.execute_async(query, target)
            else:
                result = clickhouse_client.execute_sync(query, target)
                
            return {
                "success": True,
                "rows_affected": result.row_count,
                "latency_ms": result.elapsed_ms,
                "node_used": target,
                "mode": mode
            }
            
        except ClickHouseError as e:
            attempts += 1
            # Law 4: Fail fast on schema/data errors, retry on transient
            if e.code in (117, 241, 279):  # TOO_SLOW, NETWORK_ERROR, TIMEOUT
                if attempts >= max_attempts:
                    return _failover_to_replicas(query, fallback_replicas, clickhouse_client)
                continue
            elif e.code in (47, 62):  # BAD_ARGUMENTS, UNKNOWN_TABLE
                raise ValueError(f"Query validation failed: {e.message}") from e
            else:
                raise ClickHousePipelineError(f"Unexpected CH error {e.code}: {e.message}") from e
                
    return {"success": False, "error": "Max retries exhausted", "attempts": attempts}

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.

• ClickHouse Documentation
• ClickHouse SQL Reference
• Column-Oriented DBMS (Wikipedia)
• ClickHouse Benchmarks
• ClickHouse GitHub Repository

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