--- name: hyjax-relational description: HyJAX Relational Thinking Skill version: 1.0.0 --- # HyJAX Relational Thinking Skill Apply relational thinking (ACSets/C-Sets) to Amp thread analysis using HyJAX patterns. ## When to Use - Analyzing thread relationships and concept networks - Extracting patterns from conversation history - Building relational databases from unstructured thread data - Generating Colored S-expressions for visualization ## Core Concepts ### ACSet Schema for Threads ``` Objects: Thread, Message, Concept, File Morphisms: thread_msg, mentions, discusses, related Attributes: content, timestamp, info_gain ``` ### Colored S-expressions ```lisp (acset-gold (threads-red (thread T-001 "Title" 42)) (concepts-green (concept skill 5) (concept MCP 3)) (relations-purple (edge skill co-occurs subagent))) ``` ## Key Files | File | Purpose | |------|---------| | `/Users/bob/ies/music-topos/lib/thread_relational_hyjax.hy` | Main HyJAX analyzer | | `/Users/bob/ies/music-topos/lib/unified_thread_lake.duckdb` | Persistent database | | `/Users/bob/ies/music-topos/lib/analyze_threads_relational.py` | Python analyzer | ## Quick Start ### 1. Query the Thread Lake ```bash duckdb /Users/bob/ies/music-topos/lib/unified_thread_lake.duckdb -c " SELECT name, hub_score FROM concepts ORDER BY hub_score DESC LIMIT 10 " ``` ### 2. Find 2-Hop Concept Paths ```bash duckdb /Users/bob/ies/music-topos/lib/unified_thread_lake.duckdb -c " SELECT r1.from_concept || ' → ' || r1.to_concept || ' → ' || r2.to_concept as path FROM concept_relations r1 JOIN concept_relations r2 ON r1.to_concept = r2.from_concept WHERE r1.from_concept = 'skill' " ``` ### 3. Run Full Analysis ```bash cd /Users/bob/ies && source .venv/bin/activate python3 music-topos/lib/full_thread_analysis.py ``` ## Relational Patterns ### Hub Concepts (Most Connected) | Concept | Hub Score | |---------|-----------| | skill | 8 | | GF3 | 5 | | MCP | 4 | | subagent | 3 | ### Strongest Relations - skill ↔ subagent (weight 2) - skill → MCP → alife - skill → ACSet → discohy - HyJAX ↔ relational ## Integration with Other Skills ### With `acsets-algebraic-databases` ```julia @present SchThread(FreeSchema) begin Thread::Ob; Message::Ob; Concept::Ob thread_msg::Hom(Message, Thread) discusses::Hom(Message, Concept) related::Hom(Concept, Concept) end ``` ### With `gay-mcp` Each concept gets a deterministic color via Gay.jl seed: ```julia using Gay concept_color = gay_color(hash("skill")) # Reproducible color ``` ### With `entropy` patterns ```python H(concepts) = 4.55 bits # Shannon entropy of concept distribution efficiency = 95.6% # vs max entropy ``` ## DuckDB Schema ```sql CREATE TABLE threads (thread_id VARCHAR PRIMARY KEY, title VARCHAR, message_count INT); CREATE TABLE concepts (concept_id VARCHAR PRIMARY KEY, name VARCHAR, frequency INT, hub_score INT); CREATE TABLE concept_relations (from_concept VARCHAR, to_concept VARCHAR, weight INT); CREATE TABLE colored_sexprs (sexpr_id VARCHAR PRIMARY KEY, root_color VARCHAR, tree_json JSON); ``` ## Workflow 1. **Ingest**: Use `find_thread` to get thread data 2. **Extract**: Apply concept patterns to titles/content 3. **Build**: Create ACSet with objects and morphisms 4. **Query**: Run relational queries (pullbacks, 2-hop paths) 5. **Output**: Generate Colored S-expressions ## Example Output ``` THREAD RELATIONAL ANALYSIS - 30 THREADS ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Threads: 30 Messages: 2,951 Concepts: 27 Relations: 48 Entropy: 4.55 bits (95.6% efficiency) TOP CONCEPTS: skill 5 █████ subagent 3 ███ MCP 3 ███ GF3 3 ███ COLORED S-EXPRESSION: (acset-gold (threads-red ...) (concepts-green ...) (relations-purple ...)) ``` ## Scientific Skill Interleaving This skill connects to the K-Dense-AI/claude-scientific-skills ecosystem: ### Autodiff - **jax** [○] via bicomodule ### 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: Span 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.