The irreducible epistemic atoms underlying the curriculum. 4,828 atoms across 8 types and 2 molecules
When conducting week-long observation audits of high-stakes domains, structure daily entries with physically separated sections for 'what I observed' and 'what my mind wanted to conclude,' keeping both sections visible simultaneously to train recognition of the observation-evaluation gap.
Before using AI for pattern analysis on observational data, ensure your input consists of descriptive observations rather than evaluative conclusions by applying the camera test to each input statement, because AI analyzing your conclusions produces confirmation of your biases rather than structural insights.
When a behavior, reaction, or outcome recurs three or more times across a 30-day period, classify it as a pattern candidate requiring structural analysis rather than treating it as coincidence.
Record each occurrence of a suspected pattern with date and context in a dedicated log before drawing conclusions, because memory-based frequency assessment systematically overestimates recurrence through the frequency illusion.
When the same symptom triad precedes system failures across three independent incidents, document it as a named detection pattern and build an automated alert triggered by that specific combination.
Name behavioral patterns using 2-4 word descriptors that compress the full trigger-response sequence into a recognizable label, enabling real-time pattern recognition under cognitive load.
For each named pattern in your Pattern Dictionary, document three required fields: the pattern name, its observable trigger conditions, and the default behavioral response it produces.
Do not expect pattern recognition alone to eliminate the pattern—track the ratio of pattern-following to pattern-breaking instances over weeks rather than demanding immediate control, because automaticity requires repeated override practice to weaken.
Validate cross-domain pattern candidates by verifying that the relational structure (not surface similarity) matches across domains—two patterns share structure when the causal relationships between elements are preserved even when the elements themselves differ completely.
Before optimizing around a perceived positive pattern, verify through deliberate removal tests whether the pattern persists when suspected causal factors are absent.
When a keystone habit produces cascading benefits, protect its trigger conditions and structural enablers rather than optimizing the habit's internal execution.
Before building optimization systems around a personal correlation, test whether the correlation survives when you control for potential confounding variables through deliberate experimental variation.
When a pattern appears to reverse across subgroups in your data, disaggregate by relevant context variables (sleep, stress, social setting) before drawing conclusions from the aggregate pattern.
When identifying meta-patterns, require each second-order claim to ground in at least three documented first-order pattern instances to distinguish genuine meta-patterns from intellectual speculation.
Allocate pattern-change effort to second-order interventions (changing how patterns form) over first-order fixes (changing individual patterns) when three or more first-order patterns share formation or dissolution characteristics.
Pre-load support structures during the phase before a known cyclical trough rather than attempting to maintain behavior through willpower during the trough itself.
When multiple relationships produce the same tension pattern despite different people, map your own contribution to the dynamic before attributing the pattern to others' behavior.
For each avoided task that persists beyond 48 hours, log the emotion triggered, the substitute activity performed, and the rationalization constructed, as these three elements constitute the replicable structure of personal avoidance patterns.
For each genuine success in the past two years, document conditions present, behaviors that differed from defaults, people involved, internal state, and 48-hour setup—then surface elements appearing in three or more instances as your replicable success pattern.
When a problem persists across multiple attempts, identify exceptions where the problem was absent or reduced rather than analyzing why the problem occurs, as exception conditions are more directly actionable than problem mechanisms.
Cross-reference your success pattern elements against your upcoming project plan before starting work, building in the overlapping conditions deliberately rather than hoping they emerge, as replication requires engineering.
During major life transitions (moves, job changes, context disruptions), deliberately redesign behavioral patterns rather than waiting for them to reform automatically, because environmental cue disruption creates a window where habits are more amenable to conscious redesign.
When reviewing notes for patterns, read through all entries without editing or organizing first, then extract recurring themes on a separate page, to prevent premature categorization from filtering out emergent structures.
Before concluding a pattern is meaningful, verify it survives three independent filters: sample size check (occurrences vs. opportunities), base rate comparison (frequency vs. background rate), and alternative explanation generation (minimum two alternatives).