The irreducible epistemic atoms underlying the curriculum. 4,828 atoms across 8 types and 2 molecules
Before attempting to design better schemas, inventory your current operating schemas by writing what you actually do (not what you should do) across professional, relational, and self-concept domains.
Perform schema inspection through a five-step audit: (1) list actual operating rules in a domain, (2) source each rule's origin, (3) identify one success and one failure case per rule, (4) rate confidence in each rule, (5) compare confidence to evidence quality.
When using AI to assist schema inspection, first externalize your thinking in writing, then request assumption extraction, pattern detection across entries, or adversarial questioning—because AI can only inspect articulated schemas, not internal ones.
When you update a belief, write an explicit update statement in the format 'Based on [specific evidence], I am updating my model from [old version] to [new version]' to reframe revision as calibration rather than defeat.
When formal and intuitive schemas disagree on a decision, investigate the disagreement for thirty minutes rather than defaulting to either—write what your gut is reacting to and test whether it reveals a pattern your formal criteria missed or a bias you haven't examined.
For each schema driving consequential decisions, document: (1) the schema as a sentence, (2) when you adopted it, (3) supporting evidence, and (4) what would falsify it — if you cannot articulate falsification conditions, treat the schema as dogma requiring immediate audit.
Store each schema with explicit scope documentation specifying the domain where it was built and the structural conditions it assumes, treating scope as mandatory metadata rather than optional annotation.
Document the purpose each category serves by completing the sentence 'this category exists to [do what] for [whom]' to distinguish functional infrastructure from inherited furniture.
When someone proposes a different categorization and your first reaction is irritation that they are 'wrong,' treat this as a signal that you have mistaken a constructed category for an objective feature of reality.
When a binary classification hides multiple distinct failure modes or reasons within a single bucket, decompose it into separate dimensions that can be evaluated independently.
When forced to make a binary decision after spectrum-based deliberation, document the richer multi-dimensional signal alongside the binary outcome so future analysis can recover what the compression discarded.
For every pair of categories in a classification system, verify that no item can legitimately belong to both (mutual exclusivity test), and verify that no domain item falls outside all categories (collective exhaustiveness test).
Design multi-class classification systems with mutually exclusive categories when items can only be one type, and multi-label systems when items can legitimately belong to multiple categories simultaneously.
Write a one-sentence decision rule for each priority type that defines membership criteria operationally before applying the types to any backlog.
Ensure that highest-priority items constitute less than 20% of total backlog; if more items are marked critical, recalibrate threshold definitions to restore differentiation.
Include an explicit 'not now' or lowest-tier priority type to prevent deferred items from inflating middle categories and to create visible records of deliberate exclusion.
Attach specific response protocols (timing, resources, escalation) to each priority type rather than treating them as descriptive labels, making priority actionable.
For each agent-task pair in collaborative work, assign an explicit role type (Responsible, Accountable, Consulted, Informed) and verify that every task has exactly one Accountable party.
Before committing to a category assignment in high-stakes decisions, explicitly name what actions that category triggers and what you would do differently if the item belonged to an adjacent category.
When items consistently resist classification in your system (you hesitate, force-fit, or leave uncategorized), map what those resistant items have in common to diagnose missing categories that represent dimensions you care about but haven't encoded.
For each top-level category in your knowledge system, write one sentence explaining what value that category protects or promotes, then identify missing categories that would operationalize values you hold but aren't currently encoding.
Treat a validation log with no disconfirmations as a warning signal of selective documentation rather than validation success, because unbiased testing inevitably produces some surprises.
When a 'Miscellaneous' or 'Other' category grows faster than named categories, it signals that your classification dimensions are missing a meaningful distinction that reality contains.
Before attempting to learn a target skill, map its prerequisite chain backward by repeatedly asking 'what must I be able to do first?' until reaching skills you can perform reliably, then start at the lowest-rated prerequisite rather than the target.