compound-learning

SKILL.md

Core transformation: Task(ο) + Knowledge(Κ) → Output(τ) + Knowledge'(Κ') where Κ' ⊃ Κ (knowledge strictly grows)

Traditional workflows treat each task in isolation. Compound learning treats every task as a learning opportunity that improves future performance. Like compound interest, small improvements accumulate exponentially: each unit of work makes subsequent units easier, faster, and higher-quality.

Purpose: Transform intent into structured, actionable plan using accumulated knowledge.

Self-improving holon implementing Plan→Execute→Assess→Compound workflow loops with explicit knowledge crystallization. Generalizes compound engineering beyond coding to any domain where work produces learnable signals: writing, research, learning, problem-solving, design. Use when: (1) task quality matters and can improve over time, (2) patterns emerge from repeated work, (3) institutional knowledge should accumulate, (4) future tasks should benefit from past learnings, (5) "compound", "learn from", "improve process", "capture learnings", or multi-step workflows with review cycles. Orchestrates parallel research agents, multi-lens assessment, and structured knowledge codification into searchable documentation with YAML frontmatter. Implements λο.τ → λ(ο,Κ).τ where Κ is accumulated knowledge that compounds interest-like over time. Source: zpankz/mcp-skillset.

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