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Cognitive framework extraction turns a named figure's expertise into a runnable AI skill by distilling their mental models and decision heuristics. You provide a name, and the system generates a self-contained skill package with frontmatter and expression DNA.

Creating a workflow skill from a fuzzy research need requires a diagnostic path that proposes candidates and distills them. The system diagnoses your need, proposes relevant figures or topics, and then generates a dedicated skill with mental models and decision heuristics.

Deep research distillation for building a cognitive framework works by extracting 3-7 mental models and 5-10 decision heuristics from a specified domain. It packages these into a six-phase workflow for quality validation, ensuring the output is a runnable skill.

You can generate a skill package directly from a name or domain topic by passing it to the distillation process. It instantly produces a dedicated skill file ready for installation, complete with references, expression DNA, and honest boundaries.

Limitations of automating cognitive framework extraction include relying on the availability of deep research data for the named figure or topic. The system establishes honest boundaries within the generated skill to indicate where the extracted heuristics and mental models may not apply.

You do not need any dependencies to distill a named figure into a skill. The distillation process operates independently, generating a self-contained skill package that includes references and a templated workflow without requiring external modules.