fractional dynamical model estimation with unknown unknowns
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Updated
Sep 5, 2020 - Jupyter Notebook
fractional dynamical model estimation with unknown unknowns
Verified, defensible investigation of high-stakes, complex, contested, and wicked problems — a Claude Code & Claude Cowork plugin: source-tiered web verification, a disconfirmation pass, grade-locked decision briefs, and audience rendering, architected around the ICD-203 analytic tradecraft standards.
Agent skill that finds hidden assumptions, risks, edge cases, and cheap validation probes before you build
Theorem of the Unnameable [⧉/⧉ₛ] — Epistemological framework for binary information classification (Fixed Point/Fluctuating Point). Application to LLMs via 3-6-9 anti-loop matrix. Empirical validation: 5 models, 73% savings, zero hallucination on marked zones.
A portable AI-agent skill for finding project blind spots, unknown unknowns, hidden risks, and missing decisions, with a durable ledger for repeat audits across Claude Code, Claude desktop/Cowork, Codex, and OpenCode.
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