Action Items

Action Items

Phase 0 — Data Model

  • Define competency snapshot schema (domain, microskill, level, date, evidence)

  • Define transfer edge format (source_domain, target_domain, pattern, date, example)

  • Audit existing competency partials — extract current levels as baseline snapshot

  • Audit association engine — identify which existing edges represent cross-domain transfer

  • Decide storage: extend association engine YAML, SQLite, or standalone JSON

Phase 1 — Collection

  • Build snapshot capture command (Python CLI or shell function)

  • Build transfer edge capture command — "today I used X from domain A to solve Y in domain B"

  • Integrate with worklog — auto-extract domain tags from session accomplishments

  • Create initial baseline snapshot from current competency state

Phase 2 — Storage

  • Schema for time-series competency data

  • Schema for weighted transfer edges (frequency of cross-domain application)

  • Migration path from association engine flat YAML to graph-capable store

  • Git-trackable format (no binary databases in the repo)

Phase 3 — Visualization

  • Growth curves per domain over time (matplotlib line charts)

  • Transfer map — force-directed graph of domains with edge weights (networkx + matplotlib)

  • Domain heat map — which domains are most active this week/month

  • Compound growth chart — show how multi-domain activity accelerates vs single-domain

  • Camp before/after comparison — radar chart (spider graph) of competency levels pre/post camp

Phase 4 — Camps

  • Define camp format: duration, focus domains, pre-snapshot, daily practice, post-snapshot

  • Design first camp: "Unix + Mathematics" — formal logic, set theory, predicate composition

  • Design cross-domain camp: "Music + Physics + Math" — wave mechanics, harmonic series, logarithms

  • Camp template in domus-captures (STD-001 compliant, phased)

Phase 5 — Cross-Study Mining

  • Parse codex entries for cross-domain references (grep for domain keywords in other domain files)

  • Parse git commit history for multi-domain sessions (commits touching files in different domain directories)

  • Parse worklog session accomplishments for transfer language ("used X to solve Y", "same pattern as Z")

  • Auto-generate transfer edges from mined data — human review before persisting

Structural Patterns to Track

These are the recurring patterns that connect domains. Each should become a node in the graph with edges to every domain where it appears:

  • Formal grammars (Chomsky hierarchy) → language, programming, regex, music notation

  • Wave mechanics → violin, RF, STP, acoustics, electromagnetic spectrum

  • Boolean/predicate logic → find, ISE rules, rhetoric, mathematical proofs

  • Feedback loops → CLI verify-change-verify, metabolic homeostasis, ear training, OODA

  • Graph theory → network topologies, association engine, dependency resolution, social networks

  • Logarithmic scales → musical intervals, decibels, human perception, pH, Richter

  • State machines → protocol FSMs, systemd units, modal editors (vim), musical form (ABA, sonata)

  • Idempotency → Ansible, mathematical functions, REST PUT, functional programming

  • Symmetry → group theory, musical inversion/retrograde, architectural balance, crystallography

  • Compression → zip/gzip, mental model compression (the thing this project measures), musical motif development