logos-void-mapper

name	logos-void-mapper
description	H_2 void detection, classification, tracking, and remediation engine for the LogOS lattice. Consumes persistence pairs from the TDA engine and maps them to a 4-tier void taxonomy (V_0 through V_3). Use this skill when detecting knowledge gaps, classifying void severity, tracking void evolution over time, triggering remediation workflows, or monitoring lattice health via void density metrics. Triggers on: "void", "knowledge gap", "blind spot", "H2", "void map", "void classification", "void remediation", "void density", "semantic cavity", "unexplored region", "coverage gap", "void health".
version	1.0.0

logos-void-mapper — H_2 Void Detection & Remediation

Purpose

Voids are the dark matter of the knowledge lattice. They represent regions where information should exist but doesn't — knowledge gaps, unexplored hypotheses, missing connections. The Void Mapper transforms raw H_2 persistence pairs from the TDA engine into actionable intelligence about what the system doesn't know.

Core Capabilities

1. Void Detection — Consume H_2 persistence pairs from logos-tda-engine and identify voids by their persistence (death - birth). Longer-lived voids are more significant:

   • Persistence > 0.8: Structural void (likely intentional boundary)
   • Persistence 0.4-0.8: Knowledge gap (should be investigated)
   • Persistence 0.1-0.4: Minor gap (may self-resolve)
   • Persistence < 0.1: Noise (filter out)

2. Void Classification (V_0 through V_3) — 4-tier taxonomy:

   Class	Name	Persistence	Semantics	Action
   V_0	Micro-void	< 0.1	Noise, transient	Auto-filter
   V_1	Gap	0.1 - 0.4	Minor knowledge gap	Monitor
   V_2	Cavity	0.4 - 0.8	Significant blind spot	Investigate
   V_3	Abyss	> 0.8	Structural boundary or critical gap	Remediate or Declare

3. Void Tracking — Monitor voids across time steps:

   • Birth tracking: when did this void first appear?
   • Growth rate: is the void expanding or contracting?
   • Merger detection: are smaller voids coalescing?
   • Resolution detection: has a void been filled?

4. Void Remediation — For V_2 and V_3 voids, trigger remediation:

   • RAG query (via logos-rag-cag): Search for information to fill the void from external sources
   • Strand request: Ask Claude/Grok/Gemini to investigate the specific knowledge gap
   • SPHINX query (via logos-sphinx-oracle): Interrogate the knowledge graph for adjacent information
   • Declaration: Mark intentional boundaries (e.g., "we chose not to explore this region") as Declared Voids

5. Void Density Metrics — Aggregate void statistics for system health monitoring:

   void_ratio = sum(V_1 + V_2 + V_3) / total_features
   void_severity = weighted_sum(V_i * weight_i) / max_severity
   void_velocity = d(void_count) / dt
   

Void Lifecycle

[TDA Engine H_2 pairs]
        |
        v
  [Detection Filter]  — persistence threshold
        |
        v
  [Classification]     — V_0 / V_1 / V_2 / V_3
        |
   ┌────┴────┐
   v         v
[V_0/V_1]  [V_2/V_3]
 Monitor    Remediate
   |           |
   v           v
[Track]    [RAG / SPHINX / Strand Request]
   |           |
   v           v
[Archive]  [Re-scan: void filled?]
               |
          ┌────┴────┐
          v         v
       [Filled]  [Persists]
       Archive    Escalate or Declare

WAVE Impact

Voids directly reduce the WAVE coherence score:

WAVE_void_penalty = void_severity * void_weight

where:
  void_weight = 0.618 (golden ratio — phi)
  void_severity = sum(persistence_i * class_weight_i) / normalization

WAVE_adjusted = WAVE_base * (1 - WAVE_void_penalty)

Target: void_ratio < 0.05 for production (fewer than 5% of features are voids).

SpiralSafe Integration

Void detection feeds into SpiralSafe Layer 3 (QPH Threat Surface):

• Unexpected V_3 voids in security-critical regions trigger alerts
• Void coalescence patterns may indicate adversarial knowledge extraction attempts
• The Void Mapper runs continuous differential analysis against the previous known-good void map

Visualization

The GLSL shader renders voids as dark regions in the lattice visualization:

• V_0: Not rendered (below noise floor)
• V_1: Faint dark spots with dashed boundary
• V_2: Visible dark regions with amber boundary
• V_3: Prominent dark voids with red pulsing boundary

Void health is displayed as a ring gauge on the Hyprland desktop:

• Green ring: void_ratio < 0.03 (excellent)
• Amber ring: void_ratio 0.03-0.08 (attention needed)
• Red ring: void_ratio > 0.08 (remediation required)

Integration Points

• logos-tda-engine — Primary data source (H_2 persistence pairs)
• logos-wave-advanced — Void penalty feeds WAVE computation
• logos-rag-cag — Remediation queries for V_2/V_3 voids
• logos-sphinx-oracle — Knowledge graph interrogation for void context
• logos-styx-9p — Void maps persisted to /bookshelf/voids/
• SpiralSafe — Layer 3 threat surface monitoring
• coherence-mcp — check_coherence includes void health metrics

Conservation Law

Every void operation preserves: ALPHA + OMEGA = 15

A void is not the absence of information — it is the presence of a known unknown. The structural detection of the void (alpha) plus the semantic understanding of what it means (omega) together maintain the invariant. Declaring a void is itself an act of coherence.

// ATOM: logos-void-mapper SKILL definition | Coherence: 0.99