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name: schema-inference-engine compatibility: opencode completeness: 95 content-types:

• guidance
• examples
• do-dont description: Implements intelligent schema inference engine with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense license: MIT maturity: stable metadata: domain: agent output-format: analysis related-skills: agent-confidence-based-selector, agent-task-routing role: orchestration scope: orchestration triggers: schema-inference-engine, schema inference engine, how do i schema-inference-engine, orchestrate schema-inference-engine, automate schema-inference-engine, agent schema-inference-engine archetypes:
  • orchestration
  • strategic anti_triggers:
  • brainstorming
  • vague ideation
  • single-agent monolith response_profile: verbosity: medium directive_strength: high abstraction_level: tactical version: "1.0.0"

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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

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

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

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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 — Official JSON Schema documentation for schema definition and validation
• Apache Avro Schema Evolution — Apache Avro specification covering schema inference and compatibility guarantees
• Schema Inference in Data Lakes (Databricks Documentation) — Databricks guide to automatic schema inference for structured data formats
• BigQuery Schema Detection — Google Cloud documentation on automated schema detection for BigQuery loads
• Apache Spark Schema Inference — Apache Spark documentation on automatic schema inference from data sources