# Schema Inference Engine > Implements intelligent schema inference engine with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense - Skill: `paulpas/schema-inference-engine` (Agent Skill) - Install (CLI): `npx skillmds@latest add paulpas/schema-inference-engine` - Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/schema-inference-engine/raw - Safety review: pending - 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/schema-inference-engine --- # Schema Inference Engine Orchestrates intelligent skill selection and execution for schema inference engine 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 infer_schema_from_sample( raw_data: List[Dict], source_metadata: Dict, min_confidence: float = 0.75 ) -> Dict: """Infer schema fields from raw data samples with multi-factor scoring. Applies Law 1 (Early Exit) and Law 2 (Immutable State) to ensure only valid, well-typed fields are extracted. Returns a new schema dict. """ if not raw_data or not isinstance(raw_data, list): raise ValueError("raw_data must be a non-empty list of records") inferred_fields = {} for record in raw_data: for key, value in record.items(): if key not in inferred_fields: inferred_fields[key] = { "name": key, "type": _detect_type(value), "nullable": False, "sample_values": [], "confidence": 0.0 } inferred_fields[key]["sample_values"].append(value) inferred_fields[key]["nullable"] |= value is None # Calculate multi-factor confidence scores for field_name, field in inferred_fields.items(): type_confidence = _calculate_type_confidence(field["sample_values"]) consistency_score = _calculate_consistency(field["sample_values"]) field["confidence"] = (type_confidence * 0.6) + (consistency_score * 0.4) if field["confidence"] < min_confidence: field["fallback_strategy"] = "human_review" # Law 3: Return new structure, never mutate input return { "schema_version": "1.0", "fields": list(inferred_fields.values()), "source": source_metadata.get("source_id"), "inference_timestamp": time.time() } ``` ### Pattern 2: Execution with Fallback ```python def resolve_schema_conflicts( primary_schema: Dict, secondary_schema: Dict, conflict_resolution_policy: str = "highest_confidence" ) -> Dict: """Merge two inferred schemas, resolving field conflicts via fallback chain. Implements Law 4 (Fail Fast) and fallback logic for ambiguous merges. """ if not primary_schema or not secondary_schema: raise ValueError("Both schemas must be provided for merging") merged_fields = {f["name"]: dict(f) for f in primary_schema["fields"]} conflicts = [] for field in secondary_schema["fields"]: if field["name"] in merged_fields: existing = merged_fields[field["name"]] if existing["type"] != field["type"]: # Fallback Chain: 1. Type coercion, 2. Union type, 3. Human review if _can_coerce(existing["type"], field["type"]): existing["type"] = _coerce_type(existing["type"], field["type"]) elif existing["confidence"] >= field["confidence"]: conflicts.append({ "field": field["name"], "action": "defer_to_human", "reason": "type_mismatch_low_confidence" }) else: merged_fields[field["name"]] = dict(field) else: merged_fields[field["name"]] = dict(field) # Law 3: Return new merged structure return { "merged_schema": list(merged_fields.values()), "conflicts": conflicts, "resolution_policy": conflict_resolution_policy, "audit_log": f"Merged {len(primary_schema['fields'])} + {len(secondary_schema['fields'])} fields" } ``` ### 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 | |---|---| | `nosql-data-modeling` | Provides data modeling strategies that complement schema inference for non-relational databases | | `postgresql-optimization` | Uses inferred schemas to generate optimized PostgreSQL queries and indexes | --- ## 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 domain. The model follows markdown links at load time to resolve external references and inline content. - [JSON Schema Specification](https://json-schema.org/learn/getting-started-step-by-step) — Official JSON Schema documentation for schema definition and validation - [Apache Avro Schema Evolution](https://avro.apache.org/docs/current/spec.html#schemas) — Apache Avro specification covering schema inference and compatibility guarantees - [Schema Inference in Data Lakes (Databricks Documentation)](https://docs.databricks.com/en/sql/language-manual/sql-ref-syntax-ddl-create-table-hive-format.html) — Databricks guide to automatic schema inference for structured data formats - [BigQuery Schema Detection](https://cloud.google.com/bigquery/docs/schema-detection) — Google Cloud documentation on automated schema detection for BigQuery loads - [Apache Spark Schema Inference](https://spark.apache.org/docs/latest/sql-programming-guide.html#inferring-the-schema-using-reflection) — Apache Spark documentation on automatic schema inference from data sources