Operations
AuraScore 79/100

Sponsored Research Compliance and Core Facility Allocation Matrix

Construct a multi-sponsor compliance and shared research facility allocation matrix to prevent grant audit flags.

Use this template when research administration must balance federal grant compliance rules, equipment cost recovery, and cross-departmental laboratory scheduling.

Template

Role: Chief Research Operations Officer specializing in sponsored grant administration, laboratory infrastructure management, and federal audit mitigation.

Context

  • Research Institution: {{research_institution}}
  • Active Sponsor Portfolio: {{funding_agency_portfolio}}
  • Grant Project Lifecycle Stages: {{grant_lifecycle_stages}}
  • Audit Vulnerabilities & Known Risks: {{audit_vulnerabilities}}
  • Core Laboratory & Equipment Inventory: {{core_facility_roster}}
  • Operations FTE & Technical Support Limits: {{fte_capacity_limits}}

Task

Produce an operational Sponsored Research Compliance and Core Facility Allocation Matrix that cross-references grant milestone deliverables, equipment recharge rates, and regulatory compliance protocols to prevent cost-allocation audit failures.

Method

  1. Categorize {{funding_agency_portfolio}} by regulatory framework (e.g., Uniform Guidance, NIH guidelines, industry sponsor covenants).
  2. Audit {{core_facility_roster}} against {{grant_lifecycle_stages}} to determine monthly instrument hours needed by principal investigators.
  3. Identify operational pinch points where {{fte_capacity_limits}} limit core instrument uptime or specialized technician supervision.
  4. Map {{audit_vulnerabilities}} against direct vs. indirect cost allocation rules for shared core usage.
  5. Establish priority scoring for instrument reservation conflicts between multi-year federal grants and industry contracts.
  6. Define compliance gates required before data acquisition, sample processing, and grant milestone sign-off.
  7. Build a risk-indexed allocation matrix detailing facility access, chargeback validation, and compliance tracking responsibilities.
  8. Formulate corrective workflows for unallowable cost attempts and non-compliant usage patterns.

Constraints

  • MUST represent the core output as a multi-dimensional matrix linking funding source rules directly to facility usage.
  • MUST explicitly flag unallowable cost allocations under {{funding_agency_portfolio}} rules.
  • MUST NOT suggest shifting unallowable cost overruns onto secondary federal grants.
  • Keep risk definitions strictly calibrated to the provided {{audit_vulnerabilities}}.
  • Ensure all operational recommendations comply with {{research_institution}} standard operating procedures.

Output format

  1. Operational Baseline Assessment (100-150 words)
  2. Research Core Allocation & Compliance Matrix (Markdown table with columns: Core Facility / Tool | Supported Sponsor Types | Allowable Charge Mechanism | Staffing Dependency (FTE) | Compliance Checkpoint | Audit Risk Rating | Scheduling Precedence Tier)
  3. Equipment Maintenance & Time-Allocation Protocol (Tabular schedule showing reserved vs. open access windows)
  4. Risk Mitigation & Corrective Action Playbook (4 prioritized tactical actions)

Self-review

  • Does every entry in {{core_facility_roster}} have a designated chargeback and compliance protocol?
  • Have the FTE capacity limitations from {{fte_capacity_limits}} been accounted for in the facility uptime schedules?
  • Are all flagged risks directly tied to the compliance requirements of {{funding_agency_portfolio}}?
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 engineering10/12 · 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 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.

business-strategy
business-operations
education-research
research-operations
grant-compliance
core-facilities