Results for “kol-research”

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.

Universal transformation λ(ο,K).τ with recursive self-improvement. USE WHEN routing reasoning, validating knowledge graphs, preparing CICM/ANZCA examinations, or when self-improvement of reasoning/architecture/context is required. Routes queries through R0-R3 complexity pipelines, validates topology (η≥target) and governance (KROG), emits per style (Φ), and compounds learnings into knowledge K. Triggers on complexity assessment, multi-step reasoning, examination mode, or /λ invocation.

Analyze auto-memory for promotion candidates, stale entries, consolidation opportunities, and health metrics.

Build or modify a RAG retrieval pipeline with vector store search, corpus routing, citation rendering, and book fidelity enforcement.

Record, search, and prune per-project learnings in .claude/learnings.jsonl — typed, confidence-scored, searchable across sessions

Analyze auto-memory for promotion candidates, stale entries, consolidation opportunities, and health metrics. Use when the user runs /si:review or asks what has been learned and what should be promoted or pruned.

Evaluate scientific claims and evidence quality by assessing experimental design, identifying biases and confounders, and applying evidence grading frameworks like GRADE and Cochrane Risk of Bias.

COCO: Microsoft COCO: Common Objects in Context

Adds strategic color to monochromatic or dull interfaces using oklch for perceptually uniform palettes, generating harmonious color systems with automatic light/dark variants and accessible contrast ratios.