How It Works

Copy a prompt module from GitHub. Paste it into any capable LLM (Claude, GPT-4, Gemini). Specify your brand. The model produces structured YAML output matching the corresponding template. Run all six modules sequentially for a complete spectral audit.

The pipeline has been validated across five brands (Hermes, Tesla, IKEA, Patagonia, Erewhon) and cross-model replicated with identical structural findings between Claude and Gemini.

Six Modules

01

Brand Decomposition

Decompose any brand into a structured signal inventory across all eight dimensions. Each signal is typed by emission source, tagged as designed or ambient, and assessed for strength and reach.

02

Observer Mapping

Define 3-6 observer cohorts with formal spectral profiles: sensitivity spectrum, dimension weights, tolerances, priors, and identity gate configuration. Perceptual groupings, not demographic segments.

03

Cloud Prediction

Predict how each observer cohort will cluster the brand's signals into perception clouds. Each cloud has a valence (positive, negative, ambivalent), confidence score, and formation mode (standard, mediated, stalled).

04

Coherence Audit

Score the brand on seven structural metrics: coverage, gate permeability, cloud coherence, signal strength, re-collapse resistance, emission efficiency, and designed/ambient ratio. Produces a coherence type diagnosis.

05

Emission Strategy

Design a dimensional emission strategy based on audit findings. Prioritize which dimensions to amplify, reduce, or restructure. Includes action plan with timeline and expected cohort response.

06

Re-collapse Simulation

Stress-test the brand under disruption scenarios: PR crisis, competitor entry, founder controversy, market shift. Model how each cohort's conviction re-collapses and where the structural vulnerabilities lie.

Supporting Resources