Concepts
The irreducible epistemic atoms underlying the curriculum. 4,828 atoms across 8 types
Inventory what you actually do — not what you should…
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.
Five-step schema audit
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.
Write your schemas first, then ask AI to extract…
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.
Write belief updates as 'Based on [evidence], I am…
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 gut and analysis disagree, investigate for 30 minutes…
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.
Document consequential schemas with falsification…
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.
Attach scope metadata to every schema — where it was built…
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.
For each category, complete
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.
Irritation at alternative categorizations signals you've…
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 label hides multiple distinct reasons, split…
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 into a binary decision, document the full…
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.
Test every classification for MECE
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).
Match classification architecture to domain
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.
Define each priority level with a one-sentence operational…
Write a one-sentence decision rule for each priority type that defines membership criteria operationally before applying the types to any backlog.
If more than 20% of items are top priority…
Ensure that highest-priority items constitute less than 20% of total backlog; if more items are marked critical, recalibrate threshold definitions to restore differentiation.
Every priority system needs an explicit 'not now' tier…
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.
Priority labels without response protocols are decoration…
Attach specific response protocols (timing, resources, escalation) to each priority type rather than treating them as descriptive labels, making priority actionable.
Assign RACI roles explicitly — every task needs exactly…
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 high-stakes categorization, name the actions…
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.
Classification friction reveals missing categories — map…
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.
Audit categories against your values — missing values…
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.
Zero disconfirmations in a validation log =…
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.
A growing 'Miscellaneous' category signals missing…
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.
Map prerequisite chains backward before learning — ask…
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.