obstruction-learning

plurigrid's avatarfrom plurigrid

Obstruction Learning Skill

⭐1stars🔀2forks📁View on GitHub🕐Updated Jan 9, 2026

When & Why to Use This Skill

The Obstruction Learning Skill is a sophisticated diagnostic and verification tool designed for advanced agentic systems. It utilizes topological methods, specifically Čech cohomology and random walk detection on Ramanujan expanders, to identify 'obstructions'—irreducible elements that prevent local agent behaviors from forming a consistent global state. By enforcing GF(3) conservation laws across skill triads, it ensures mathematical coherence, evaluates system-wide spectral awareness, and optimizes coordination efficiency by minimizing World Extractable Value (WEV).

Use Cases

  • Detecting and resolving logical inconsistencies in complex multi-agent skill compositions where local patches fail to glue into a globally consistent behavior.
  • Evaluating the structural integrity and spectral mixing of AI models through random walk sampling on high-dimensional skill manifolds.
  • Optimizing decentralized coordination by calculating and reducing World Extractable Value (WEV), which represents coordination loss within the system.
  • Verifying the mathematical balance of agent triads (Validator, Coordinator, Generator) to ensure system-wide harmony and functional stability.
  • Mapping and auditing the density of skills within a 23³ grid to identify 'unbalanced cells' that require complementary skill injection.
nameobstruction-learning
descriptionObstruction Learning Skill
version1.0.0

Obstruction Learning Skill

Learn topological ASI via random walk obstruction detection and Čech H⁰ cohomology.

Metadata

Property Value
Name obstruction-learning
Trit -1 (VALIDATOR)
Category Topological Verification
Dependencies sheaf-cohomology, ramanujan-expander, gay-mcp

Core Concept

Obstructions are H⁰ generators - irreducible elements that block global consistency from local patches.

Čech Cohomology: H⁰(U, F) = ker(d₀: F(U) → ∏ᵢⱼ F(Uᵢ ∩ Uⱼ))

Obstruction detected when:
  - GF(3) conservation violated (sum ≢ 0 mod 3)
  - Voice triads don't harmonize
  - Skill compositions conflict
  - Local patches fail to glue globally

Random Walk Reconstruction

The 69-Skill Walk

Sample 69 skills from the 181-skill manifold:

# Execute random walk
just random-walk-69

# Verify GF(3) conservation
just verify-gf3

# Track cumulative obstructions
just random-walk-obstruction 69

Obstruction Detection

-- Find unbalanced cells in 23³ grid
SELECT cell_id, skill_count, trit_sum, gf3_status
FROM cell_density 
WHERE gf3_status = 'UNBALANCED';

-- H⁰ generators by trit class
SELECT trit, COUNT(*) as generators
FROM skills 
GROUP BY trit;

Mathematical Foundations

Čech Cohomology

For a covering U = {Uᵢ} of skill space:

H⁰(U, F) = { s ∈ F(U) | d₀(s) = 0 }

where d₀: F(U) → ∏ F(Uᵢ ∩ Uⱼ)
maps global sections to intersection restrictions

Obstruction = element of H⁰ that prevents gluing.

GF(3) as Cohomology

The GF(3) conservation law is a discrete cohomology:

Trit assignment: skill → {-1, 0, +1}
Coboundary: d(triad) = sum of trits mod 3

H⁰ = { triads | d(triad) = 0 } = balanced triads
Obstruction = triad with d ≠ 0

Ramanujan Mixing

Random walks on Ramanujan expanders mix optimally:

λ₂ ≤ 2√(d-1)     [Alon-Boppana bound]
gap = d - λ₂      [Spectral gap]
τ_mix = O(log n / gap)  [Mixing time]

Workflow

1. Pre-Interaction Sync

just pre-interaction
# Syncs plurigrid/asi arena + hdresearch/duck
# Loads GF(3) skill triad
# Computes spectral awareness

2. Random Walk Sampling

# Sample without replacement (maximal coverage)
just random-walk-69

# Sample with replacement (GF(3) conservation)
just random-walk 23

3. Obstruction Detection

# Find H⁰ generators
just obstruction-h0

# Detect unbalanced cells
just obstruction-detect

# Balance with complementary skill
just obstruction-balance -1  # Find validators to add
just obstruction-balance +1  # Find generators to add

4. Audio Generation

Convert obstruction traces to sound:

just audio-from-trace

Integration Patterns

With Voice Enforcement

# voice-enforcement.toml
[triads.obstruction]
validator = "Milena (Enhanced)"   # -1: detects obstruction
coordinator = "Petra (Premium)"   # 0: mediates resolution
generator = "Federica (Premium)"  # +1: proposes fix
sum = 0

With Dune Orthogonalization

The 23×23×23 grid maps skills to:

Axis Dimensions
DATA chain_indexing → real_time_streaming
INTERFACE sql_query_engine → ai_copilot
INFRASTRUCTURE kubernetes → multi_tenant_isolation

With World Extractable Value

WEV = PoA - 1 = extractable coordination loss

Obstruction → WEV > 0
Resolution → WEV → 0
Global consistency → Optimal coordination

Commands

# Full ASI learning loop
just asi-learn

# Spectral bounds
just spectral-bounds

# World Extractable Value
just wev-compute

# Voice obstruction analysis
just voice-obstructions

Skill Triad

This skill belongs to the topological verification triad:

Role Skill Trit
VALIDATOR obstruction-learning -1
COORDINATOR sheaf-cohomology 0
GENERATOR persistent-homology +1

Sum = 0 ✓ GF(3) conserved

References

  • Bott & Tu, Differential Forms in Algebraic Topology
  • Lurie, Higher Topos Theory
  • Riehl-Shulman, Synthetic ∞-categories
  • QRI, Symmetry Theory of Valence

Scientific Skill Interleaving

This skill connects to the K-Dense-AI/claude-scientific-skills ecosystem:

Graph Theory

  • networkx [○] via bicomodule
    • Universal graph hub

Bibliography References

  • general: 734 citations in bib.duckdb

Cat# Integration

This skill maps to Cat# = Comod(P) as a bicomodule in the equipment structure:

Trit: 0 (ERGODIC)
Home: Prof
Poly Op: ⊗
Kan Role: Adj
Color: #26D826

GF(3) Naturality

The skill participates in triads satisfying:

(-1) + (0) + (+1) ≡ 0 (mod 3)

This ensures compositional coherence in the Cat# equipment structure.