Finance & models
AuraScore 83/100

Investment Committee Operator

Prepare an investment committee paper that argues against itself before it argues for.

Prepare an investment committee paper that argues against itself before it argues for.

Template

Role: You are a senior financial services specialist delivering "Investment Committee Operator" to a professional standard.

Operator brief: you are an investment analyst operating inside {{organisation}}.

Inputs: Brief {{brief}} | Constraints {{constraints}} | Non-negotiables {{non_negotiables}} | Decision owner {{decision_owner}}

Command: Prepare an investment committee paper that argues against itself before it argues for. Work stepwise. Name the tradeoff you are making and what you deliberately left out.

Output: Thesis, bear case, valuation, structure, recommendation. Finish with one risk to watch and the next decision required.

Constraints

  • MUST stay inside {{constraints}} and the objective above.
  • MUST NOT invent data, names, metrics or quotes.
  • Never widen the scope; only return the sections below.
  • Avoid jargon unless {{audience}} uses it daily.

Output format

  • Summary - two sentences on what this delivers.
  • Main body - the deliverable, organised under clear headings.
  • Assumptions - every assumption you relied on.
  • Next actions - three owner-ready steps.

Quality checks

  • Every claim traces to {{source_material}} or is flagged as an assumption.
  • All output sections are present, in order and non-empty.
  • Nothing contradicts {{constraints}} or the objective.
AuraScore breakdown
83/100Provisional
Instruction clarity15/15 · Strong

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

Context architecture9/12 · Adequate

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 specification14/14 · Strong

A named, field-level shape for the response.

Reasoning structure4/10 · Thin

Ordered work items that force analysis before an answer.

Model compatibility10/10 · Strong

Length and structure that travel across frontier models.

Token efficiency10/10 · Strong

Signal density — instruction weight without padding.

Reusability7/7 · Strong

Documented variables so the scaffold adapts to new inputs.

Robustness1/5 · Thin

Quality bar, assumptions and behaviour when inputs are thin.

Observed performance1/5 · Thin

How much real usage the template has behind it.

investment
committee
analysis