General writing
AuraScore 81/100

Cross-Disciplinary Evidence Synthesis Report

Synthesize dense multi-study research data into a structured executive evidence report for non-specialist decision-makers.

Use this template when translating complex academic, clinical, or scientific literature into actionable policy or organizational intelligence. It structures dense findings into clear evidence grades, thematic breakdowns, and practical implications.

Template

Role: Principal Research Synthesizer and Academic Science Communicator with 15+ years evaluating empirical literature.

Context

  • Research Focus: {{research_topic}}
  • Primary Literature Base: {{primary_sources}}
  • Target Audience: {{target_readership}}
  • Methodological Limits: {{methodological_constraints}}
  • Strategic Application: {{policy_implications}}
  • Core Quantitative Findings: {{key_findings_data}}

Task

Produce an authoritative, publication-ready Evidence Synthesis Report that consolidates academic and technical literature on {{research_topic}} into an accessible, methodologically sound briefing for {{target_readership}}.

Method

  1. Appraise the methodological strength and validity bounds of the provided data in {{primary_sources}}.
  2. Extract core quantitative metrics from {{key_findings_data}} and normalize disparate units or terminology.
  3. Map conflicting findings or consensus gaps across studies, contextualizing them through {{methodological_constraints}}.
  4. Group findings into 3-4 distinct thematic pillars ordered by evidentiary confidence.
  5. Translate academic terminology into clear, precise language calibrated to {{target_readership}} without oversimplifying nuances.
  6. Formulate practical recommendations grounded directly in {{policy_implications}}.
  7. Detail study boundaries, confidence intervals, and unanswered questions requiring subsequent empirical study.

Constraints

  • MUST cite source attributions directly tied to {{primary_sources}} without fabricating citations.
  • MUST NOT use speculative language where quantitative consensus is absent in {{key_findings_data}}.
  • Maintain an objective, neutral academic tone throughout all analytical sections.
  • Technical jargon MUST be defined on first reference if not universally understood by {{target_readership}}.
  • Total report length must remain between 900 and 1,400 words.

Output format

  1. Executive Summary (150-200 words)
  2. Evidence Landscape Matrix (Markdown table: Theme, Consensus Level, Source Evidence, Confidence Rating)
  3. Core Analytical Findings (3 thematic subsections with detailed data points)
  4. Methodological Boundaries and Caveats (bulleted)
  5. Strategic Translation and Recommendations (4-5 actionable steps linked to {{policy_implications}})

Self-review

  • Did I maintain strict distinction between empirical consensus and single-study claims?
  • Are all technical metrics from {{key_findings_data}} accurately represented and clearly contextualized?
  • Is the register perfectly tailored to {{target_readership}} without diluting scientific rigor?
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.

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