Data cleaning
AuraScore 85/100

Axiomatic Entity Consistency and Logical Discrepancy Cleaning Brief

Structure an axiomatic validation and automated contradiction-resolution strategy for relational and knowledge-graph datasets.

Use this template when relational, semantic, or transactional datasets contain logical paradoxes, circular dependencies, or business rule violations. It produces an unambiguous conflict-arbitration brief.

Template

Role: Lead Knowledge Representation Analyst and Formal Verification Specialist

Context

  • Relational Domain: {{relational_schema_scope}}
  • Invariant Axioms: {{deterministic_business_axioms}}
  • Temporal Invariants: {{temporal_sequence_constraints}}
  • Contradiction Log: {{observed_contradiction_log}}
  • Precedence Hierarchy: {{conflict_resolution_precedence}}

Task

Author a formal data cleansing and logic reconciliation brief that resolves contradictory assertions, invariant breaches, and temporal sequence violations across {{relational_schema_scope}} based on {{deterministic_business_axioms}}.

Method

  1. Deconstruct {{deterministic_business_axioms}} into formal first-order logic invariants over the schema.
  2. Ingest {{observed_contradiction_log}} and classify errors into mutually exclusive logic categories (e.g., cardinality breaches, circular state loops, temporal inversions).
  3. Construct a directed dependency graph to detect state transitions violating {{temporal_sequence_constraints}}.
  4. Apply {{conflict_resolution_precedence}} to establish unambiguous arbitration criteria for conflicting facts.
  5. Define deterministic correction operators (e.g., mutation, deletion, tombstoning, node splitting) for each failure mode.
  6. Design a deterministic audit trace requirement to log every automated modification with mathematical justification.
  7. Establish post-repair graph cycle and invariant validation scripts to ensure zero unresolved paradoxes.

Constraints

  • MUST define deterministic resolution rules such that multiple passes over the dataset yield identical states.
  • MUST NOT allow ambiguous fallback states; every contradiction must have a singular deterministic outcome.
  • All temporal repair rules MUST preserve chronological causality.
  • Limit output length to a concise brief format under 750 words.

Output format

Structure the brief using these four distinct sections:

  1. Formal Invariant & Axiom Mapping
  2. Logic Violation Triage & Root Cause Analysis
  3. Precedence-Based Resolution Directives
  4. Verification Queries & Determinism Guarantees

Self-review

  • Verify that every violation cited in {{observed_contradiction_log}} is addressed by an explicit rule.
  • Check that {{conflict_resolution_precedence}} leaves no unresolved ties between conflicting records.
  • Ensure all temporal fixes strictly satisfy {{temporal_sequence_constraints}}.
AuraScore breakdown
85/100Provisional
Instruction clarity15/15 · Strong

Explicit role, a named task, and discrete steps the model can follow.

Context architecture12/12 · Strong

Background, inputs and variables the model needs before it starts.

Constraint engineering12/12 · Strong

Hard boundaries — what the model must and must not do.

Output specification6/14 · Thin

A named, field-level shape for the response.

Reasoning structure10/10 · Strong

Ordered work items that force analysis before an answer.

Model compatibility10/10 · Strong

Length and structure that travel across frontier models.

Token efficiency7/10 · Adequate

Signal density — instruction weight without padding.

Reusability7/7 · Strong

Documented variables so the scaffold adapts to new inputs.

Robustness5/5 · Strong

Quality bar, assumptions and behaviour when inputs are thin.

Observed performance1/5 · Thin

How much real usage the template has behind it.

data-analytics
data-cleaning
complex-reasoning-analysis-math
data cleaning
formal verification
knowledge graph