Workflow chains
AuraScore 81/100

Grant Proposal Intake and Pre-Review Workflow Spec

Develop a multi-agent orchestration specification for research grant compliance checks, triage, and reviewer assignment.

Use this template when setting up an automated triage pipeline for university sponsored programs or grant offices. It formalizes the compliance verification, ethics pre-screening, and faculty reviewer matching sequence.

Template

Role: Director of Research Operations and AI Workflow Engineering.

Context

  • Research administration unit: {{university_research_office}}
  • Funder regulatory guidelines: {{funder_guidelines}}
  • Compliance protocol: {{ethics_board_protocol}}
  • Evaluation rubric: {{scoring_rubric}}
  • Triage turnaround SLA: {{triage_latency_target}}
  • Human escalation condition: {{escalation_threshold}}

Task

Formulate a rigorous workflow chain specification for an automated grant pre-review pipeline that verifies administrative compliance, conducts ethical screening against {{ethics_board_protocol}}, and triages proposals across {{university_research_office}} within {{triage_latency_target}}.

Method

  1. Specify the document ingestion and structural validation agent for proposals submitted under {{funder_guidelines}}.
  2. Design the budgetary and administrative compliance auditing chain to flag disallowed costs, missing sub-awards, or formatting deviations.
  3. Formulate the ethics pre-screening agent evaluating human subject, animal care, and export control risks against {{ethics_board_protocol}}.
  4. Construct the semantic profile matching agent that parses project abstracts to identify appropriate faculty reviewers using institutional bibliographies.
  5. Establish the rubric scoring agent that populates preliminary readiness indices using {{scoring_rubric}}.
  6. Detail the automated triage router directing proposals to either standard fast-track approval or {{escalation_threshold}} escalation paths.
  7. Specify notification and summary packet generation for research deans and principal investigators within {{triage_latency_target}}.

Constraints

  • MUST flag non-compliant grant applications before any qualitative peer review matching occurs.
  • MUST NOT provide subjective scientific merit judgments; agents must restrict evaluation strictly to {{scoring_rubric}} compliance.
  • Reviewer matching must exclude any faculty member with identified institutional or co-authorship conflicts of interest.
  • Pipeline execution must complete end-to-end processing within {{triage_latency_target}}.

Output format

  1. Workflow Architecture & Stage Map (Numbered sequence with stage gates)
  2. Agent Execution Specifications (Agent Name, Task, Input Schema, Output Schema, Validation Rules)
  3. Compliance & Ethics Matrix (Rule mappings for {{funder_guidelines}} and {{ethics_board_protocol}})
  4. Reviewer Matching & Conflict Algorithm Specification (Logic criteria)
  5. Escalation & Triage Policy Document (Structured rules for {{escalation_threshold}})

Self-review

  • Ensure conflict-of-interest checks are explicitly detailed in the matching stage.
  • Verify that administrative compliance verification strictly enforces {{funder_guidelines}}.
  • Confirm that escalation workflows correctly trigger when metrics meet {{escalation_threshold}}.
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.

ai-agents
agents-workflows
education-research
grant-management
compliance-automation
research-admin