Career BoardSubstrateNSRG
PATTERN ENGINE

Cross-session coherence and drift detection.

229 patterns scored

Narrative Signal Resonance Graph. Detects skill-demand shifts, technology convergence, and gap clusters across improvement cycles.

CAPABILITIES

What it does, line by line.

  • 01Skill-demand shift detection
  • 02Technology-convergence detection
  • 03Gap-cluster detection
  • 04Confidence-scored evidence per pattern
  • 05Cycle-versioned pattern history
  • 06Feeds the weekly Nexus pulse and the Chameleon archetype router
SPEC

How NSRG actually works.

NSRG is the pattern layer that sits on top of UCW. Where the wallet captures everything, NSRG decides what matters. It correlates completed user stories with ResearchGravity learnings, surfaces skill-demand shifts before they hit the resume layer, and flags gap clusters that compound across cycles.

Three pattern types are tracked: skill-demand-shift (rising or falling market signals against the operator profile), technology-convergence (when adjacent tools collapse into a single workflow), and gap-cluster (multiple gaps that share the same root). Each pattern carries evidence, a confidence score, and the recommendation that closes it.

NSRG is what makes the weekly Nexus pulse non-trivial. Without it, the pulse would be a numeric score with no narrative. With it, the operator gets the pattern, the proof, and the next move, ranked by leverage rather than urgency.

Currently scoring 229 patterns across the operator graph. Patterns are versioned, dated, and survive cycles. A pattern that triggered three cycles ago and resolved is still queryable as a receipt the next time the same shape recurs.

HOW NSRG POWERS THE CHAMELEON

How NSRG fits the Chameleon engine.

NSRG scores cross-session coherence so Chameleon learns which archetype rewrites converted into interviews.

BUILT ON NSRG

The substrate is the moat. The product is the proof.

Career Board ships NSRG as a working surface. Try the Chameleon, see the receipts, then decide.