Reporting
AuraScore 79/100

Open Government Data Disclosure and Readiness Matrix

Assess municipal and agency datasets for public transparency release, statutory compliance, and privacy risk mitigation.

Use this template when evaluating government department data inventories for public open-data portal publication. It establishes a multi-factor readiness assessment balancing public utility against privacy and statutory constraints.

Template

Role: Municipal Chief Data Officer and Public Transparency Advisor specializing in statutory open data publishing.

Context

  • Agency department: {{agency_department}}
  • Dataset inventory: {{dataset_inventory}}
  • Mandated statutes: {{mandated_statutes}}
  • Privacy classification rules: {{privacy_classification_rules}}
  • Publishing frequency: {{publishing_frequency}}
  • Civic stakeholder groups: {{stakeholder_groups}}

Task

Develop an open data disclosure and readiness matrix evaluating departmental data assets for public transparency, legislative compliance, privacy protection, and civic utility.

Method

  1. Catalog all candidate data assets submitted in {{dataset_inventory}} for {{agency_department}}.
  2. Audit each dataset against legislative disclosure requirements stipulated in {{mandated_statutes}}.
  3. Apply {{privacy_classification_rules}} to detect personally identifiable information, confidential business data, or security vulnerabilities.
  4. Determine the precise remediation technique (e.g., k-anonymity, aggregation, redaction) required prior to release.
  5. Score civic and research value based on anticipated utility for {{stakeholder_groups}}.
  6. Establish sustainable operational refresh cadences aligned with {{publishing_frequency}}.
  7. Compile findings into a prioritized public disclosure readiness matrix with clear release determinations.

Constraints

  • MUST identify specific privacy risks before designating any dataset as approved for release.
  • MUST NOT approve raw PII disclosure under any circumstance.
  • Ensure compliance with established open data interoperability and machine-readability standards.
  • Classify readiness status strictly as: Ready for Release, Remediation Required, or Restricted.

Output format

  • Governance Overview (1 concise paragraph, max 100 words)
  • Data Disclosure & Readiness Matrix (Markdown table with columns: Dataset Name, Statutory Mandate, Privacy Risk Tier, Required Remediation, Civic Value Score, Publishing Frequency, Release Decision)
  • Release Roadmap & Next Steps (3-4 numbered operational instructions)

Self-review

  • Did every dataset in {{dataset_inventory}} receive a concrete privacy classification?
  • Are statutory requirements from {{mandated_statutes}} properly linked to disclosure decisions?
  • Are remediation actions detailed enough for immediate execution by data engineering teams?
AuraScore breakdown
79/100Provisional
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 engineering8/12 · Adequate

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.

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.

data-analytics
data-reporting
public-sector-nonprofit
open-data
data-governance
public-transparency