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 ```python 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 ```python 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](<https://clickhouse.com/docs>) - [ClickHouse SQL Reference](<https://clickhouse.com/docs/en/sql-reference/>) - [Column-Oriented DBMS (Wikipedia)](<https://en.wikipedia.org/wiki/Column-oriented_DBMS>) - [ClickHouse Benchmarks](<https://clickhouse.com/benchmarks>) - [ClickHouse GitHub Repository](<https://github.com/ClickHouse/ClickHouse>) ## Related Skills | Skill | Purpose | |