Universal Rigorous Synthesis & Analysis Protocol (URSAP): Instruction Prompt, Audit Checklist, Stress-Test Reports, and Daily Deployment Playbook

This deposit contains a complete, model-agnostic operational package for running AI-assisted analysis under explicit epistemic constraints suitable for institutional, governance, and archival contexts.

The package includes:
Universal Rigorous Synthesis & Analysis Instruction Prompt (URSAP)
A constraint-specification that defines a non-conversational analytic role and enforces: prohibition of vibe-checking and sycophancy, prohibition of premature synthesis, strict phase separation (interpretation, weighting, framework construction, emergence, validation/falsification), explicit ambiguity handling, and mandatory falsification gates (single-source reduction, framework dependency testing, and counter-synthesis).
Formal Evaluation Checklist (Third-Party Audit)
A structured compliance checklist designed for external auditors to assess whether AI-assisted analytic work followed the protocol, including instruction presence, role discipline, phase separation, divergence preservation, synthesis validity criteria, falsification completion, contamination review, and longitudinal integrity controls.

Stress-Test Report: Cross-Model Failure Mode Evaluation
A comparative evaluation of how the instruction prompt suppresses or exposes known failure modes across four model families, including vibe-checking, sycophancy, premature synthesis, structural flattening, conversational residue, silent role drift, and evidence theater. Model families discussed include Google Gemini-class models, Meta LLaMA-derived models, OpenAI ChatGPT-class models, and Perplexity AI answer-optimized systems.

Advanced Stress Testing Addendum
An extension of the stress-test suite focused on institutional deployment conditions (consensus pressure, authority substitution, compliance overfitting), adversarial prompt pressure (social manipulation, intimidation, moral urgency, time pressure), and longitudinal degradation in multi-day sessions (memory compression drift, role relaxation, certainty creep), with mitigation clauses and best practices.

Operational Playbook for Daily Deployment
A procedural discipline that treats each work period as a bounded analytic session with mandatory stages: session initialization and instruction lock-in, material ingestion discipline, phase-gated analytic execution, explicit validation and locking, and session closure that preserves provisionality and prevents certainty creep.
Intended use: governance teams, research groups, evaluators, and institutional reviewers who need an explicit, auditable method for preventing AI outputs from drifting into tone-optimized, consensus-seeking, or prematurely synthesized artifacts. The design goal is not “better answers,” but controlled analytic behavior: traceable, falsifiable, phase-separated work products suitable for scrutiny and reuse.
This deposit is an operational stack: a constraint prompt (specification), an auditor checklist (accountability), stress-test reports (failure mode visibility), and a daily playbook (process discipline).