tripartite-decompositions
GF(3)-balanced structured decompositions for parallel computation. Decomposes problems into MINUS/ERGODIC/PLUS components with sheaf-theoretic gluing. Use for FPT algorithms, skill allocation, or any 3-way parallel workload.
npx skills add majiayu000/claude-skill-registry --skill tripartite-decompositions --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Tripartite Decompositions **Trit**: 0 (ERGODIC - coordinates decomposition) **Foundation**: StructuredDecompositions.jl + GF(3) conservation **Principle**: Every problem decomposes into 3 parts summing to 0 mod 3 ## Core Concept A **tripartite decomposition** is a structured decomposition where: 1. The decomposition shape is a 3-clique (triangle) 2. Each bag is labeled with a trit ∈ {-1, 0, +1} 3. Adhesions preserve GF(3) conservation: Σ trits ≡ 0 (mod 3) ``` MINUS (-1) ╱╲ ╱ ╲ ╱ ╲ ╱ ⊗ ╲ ╱________╲ ERGODIC (0) PLUS (+1) Conservation: (-1) + 0 + (+1) = 0 ✓ ``` ## Mathematical Foundation ### From StructuredDecompositions.jl ```julia # A structured decomposition is a diagram d: ∫G → Span(C) # where G is the decomposition shape and C is the target category abstract type StructuredDecomposition{G, C, D} <: Diagram{id, C, D} end struct StrDecomp{G, C, D} <: StructuredDecomposition{G, C, D} decomp_shape ::G # The shape (for tripartite: K₃) diagram ::D # The actual decomposition functor decomp_type ::DecompType # Decomposition or CoDecomposition domain ::C # Source category end ``` ### Tripartite Extension ```julia using StructuredDecompositions using Catlab # Define the tripartite shape:
- Core Concept
- Mathematical Foundation
- From StructuredDecompositions.jl
- Tripartite Extension
- The 𝐃 Functor (Lifting Problems)
- Random Walk 3-at-a-Time
- Skill Allocation Example
- FPT Algorithms
- Dynamic Programming Connection
- Color Integration
- Gluing via Adhesions
- Validation
- Usage Example
- Canonical Triads
Verify a tripartite decomposition function verify_tripartite(decomp) bags = [bag(decomp, :Minus), bag(decomp, :Ergodic), bag(decomp, :Plus)] trits = [b.trit for b in bags] return true end
What does the tripartite-decompositions skill do?
GF(3)-balanced structured decompositions for parallel computation. Decomposes problems into MINUS/ERGODIC/PLUS components with sheaf-theoretic gluing. Use for FPT algorithms, skill allocation, or any 3-way parallel workload.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill tripartite-decompositions --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.
