learn

Recursive self-improving holon λ(ο,Κ,Σ).τ' for knowledge compounding and schema evolution. USE WHEN learning, improving, optimizing, assessing, reflecting, debugging, synthesizing, or refining—whether human, AI, or organizational. Triggers on /learn, /compound, /improve, /refine, /optimize, /assess, /reflect, "lessons learned", "best practices", "continuous improvement". Preserves Κ-monotonicity, η≥4, homoiconicity.

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Install skill "learn" with this command: npx skills add zpankz/mcp-skillset/zpankz-mcp-skillset-learn

<!-- Extended Metadata (non-official, preserved for framework compatibility) --> <!-- ο.class: "occurrent" | ο.mode: "independent" --> <!-- λ.in: lambda-skill | λ.out: 1-parse, INDEX | λ.kin: schema, lambda-compound --> <!-- τ.goal: compound knowledge; preserve η≥4, Κ-monotonicity -->

Learn

λ(ο,Κ,Σ).τ' — Knowledge compounds, schema evolves.

Navigation

INDEX | schema

Concepts: homoiconicity, compound-interest, topology, vertex-sharing, convergence, fixed-point

Phases: 1-parse2-route3-execute4-assess5-refactor6-compound7-renormalize

Domains: learning, coding, research, writing, meta

Related Skills: λ (lambda-skill) — shares compound loop, topology validation, vertex-sharing

Pipeline

ο → PARSE → ROUTE → EXECUTE → ASSESS → REFACTOR → COMPOUND → RENORMALIZE → τ'

PARSEROUTEEXECUTEASSESSREFACTORCOMPOUNDRENORMALIZE

Invariants

InvariantExpressionReference
Κ-monotonicitylen(Κ') ≥ len(Κ)knowledge-monotonicity
Topologyη ≥ 4topology-invariants
HomoiconicityΣ.can_process(Σ)homoiconicity
Integrationshared_vertices ≠ ∅vertex-sharing

Workflow Routing

WorkflowTriggerFile
Parse"extract intent", "understand request"phases/1-parse.md
Route"classify complexity", "select pipeline"phases/2-route.md
Execute"apply skills", "run pipeline"phases/3-execute.md
Assess"evaluate outcome", "measure quality"phases/4-assess.md
Refactor"improve structure", "optimize"phases/5-refactor.md
Compound"extract learnings", "crystallize"phases/6-compound.md
Renormalize"prune noise", "compress"phases/7-renormalize.md

Examples

Example 1: After debugging session

User: "That fixed the auth bug. Let's capture what we learned."
→ Invokes Compound phase
→ Extracts: symptom, root cause, solution, prevention
→ Crystallizes learning with vertex-sharing to PKM
→ Returns: Learning artifact saved to K

Example 2: Skill improvement

User: "/learn improve the grounding-router skill"
→ Invokes full pipeline: Parse → Route (R2) → Execute → Assess → Refactor
→ Applies topology validation (η≥4)
→ Returns: Improved skill with preserved invariants

Example 3: Reflection on session

User: "/reflect on this coding session"
→ Invokes Assess → Compound → Renormalize
→ Extracts patterns, antipatterns, principles
→ Returns: Session learnings integrated into K

Integration with λ (lambda-skill)

This skill extends lambda-skill with:

  • Additional phases: Assess, Refactor, Renormalize (beyond λ's 6 stages)
  • Schema evolution: Σ→Σ' (λ only evolves K)
  • Shared invariants: η≥4, KROG, vertex-sharing
-- λ (lambda) core
λ(ο,K).τ = emit ∘ validate ∘ compose ∘ execute(K) ∘ route ∘ parse

-- Learn extends with schema evolution
λ(ο,Κ,Σ).τ' = renormalize ∘ compound ∘ refactor ∘ assess ∘ execute ∘ route ∘ parse

Quick Reference

λ(ο,Κ,Σ).τ'    Parse→Route→Execute→Assess→Refactor→Compound→Renormalize
Κ grows        Σ evolves        η≥4 preserved        vertex-sharing enforced

Source Transparency

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