App stores
AuraScore 81/100

Quantitative Analytics Mobile Engine Release Gate Checklist

Verify algorithmic determinism, financial computation disclosures, and store guideline compliance for quantitative mobile apps.

Apply this prompt template when auditing quantitative analysis, statistical charting, or complex algorithmic tool apps for app store release. It produces an exhaustive checklist covering mathematical transparency, disclaimers, and store guideline requirements.

Template

Role: Lead Mobile Quantitative QA Engineer specializing in financial algorithm validation and store distribution compliance.

Context

  • Analytics Suite: {{analytics_suite_name}}
  • Applicable Store Guidelines Version: {{store_guidelines_version}}
  • Model Execution Mode: {{model_execution_mode}}
  • Deterministic Seed Management: {{deterministic_seed_policy}}
  • User Financial Disclosure Level: {{user_disclosure_level}}
  • Supported OS Versions: {{target_os_versions}}

Task

Generate a production-ready QA and compliance release checklist to evaluate {{analytics_suite_name}} against {{store_guidelines_version}}, ensuring that algorithmic simulations, user disclosures, and mathematical models are transparent, compliant, and deterministic across all {{target_os_versions}}.

Method

  1. Review {{store_guidelines_version}} requirements regarding financial modeling tools, disclaimers, and algorithmic claims.
  2. Define deterministic validation checks verifying {{deterministic_seed_policy}} produces consistent outputs across device architectures.
  3. Structure verification points for runtime computation modes under {{model_execution_mode}}.
  4. Draft UI/UX audit items verifying that {{user_disclosure_level}} warnings and risk disclosures are prominently displayed prior to calculation execution.
  5. Establish mathematical boundary validation checkpoints (e.g., divide-by-zero handling, infinity representation, precision truncation).
  6. Formulate store metadata verification points to ensure app descriptions do not make unsubstantiated predictive claims.
  7. Detail platform backward-compatibility checks across the entire range of {{target_os_versions}}.

Constraints

  • Checklist items MUST enforce strict separation between calculation correctness and UI representation.
  • Checklist items MUST NOT allow non-deterministic simulation outputs where seed management is mandated.
  • Disclosures must be validated against both store policy and algorithmic transparency guidelines.
  • Every section must contain actionable verification items with clear pass criteria.

Output format

Provide the release checklist formatted as:

  • Section 1: Algorithmic Correctness and Seed Reproducibility Checklist (4-6 items)
  • Section 2: Store Guideline Disclosure and Claim Verification Checklist (3-5 items)
  • Section 3: Model Execution Runtime and Boundary Handling Checklist (4-6 items)
  • Section 4: Multi-OS Compatibility and Metadata Hygiene Checklist (3-5 items)

Self-review

  • Verify that the requirements of {{store_guidelines_version}} are explicitly referenced in disclosure checks.
  • Ensure all supported versions in {{target_os_versions}} are covered by compatibility checks.
  • Confirm that mathematical boundary conditions are fully testable as binary criteria.
AuraScore breakdown
81/100Provisional
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 efficiency5/10 · Thin

Signal density — instruction weight without padding.

Reusability7/7 · Strong

Documented variables so the scaffold adapts to new inputs.

Robustness3/5 · Adequate

Quality bar, assumptions and behaviour when inputs are thin.

Observed performance1/5 · Thin

How much real usage the template has behind it.

developers
developers-app-stores
complex-reasoning-analysis-math
quantitative-analytics
algorithmic-validation
app-store-guidelines