The irreducible epistemic atoms underlying the curriculum. 2,888 atoms across 3 types and 2 molecules
When algorithmic feeds or social media constitute a primary information source, deliberately rotate information inputs across epistemic communities outside your filter bubble on a scheduled basis to counteract echo chamber effects.
Before removing any inherited system, process, or organizational structure, document why it was originally created and what problem it solved—if this context cannot be reconstructed, you lack sufficient information to safely remove it.
When recall of studied material fails, mentally reinstate the original encoding context—room, time of day, task being done, emotional state—before concluding the information wasn't learned or needs re-studying.
Before sending any important communication, apply the SCQA test—verify the message includes Situation (what reader knows), Complication (what changed), Question (what this raises), and Answer (your point)—adding any missing layer before transmission.
When multiple contexts are active simultaneously, identify which one is primary for the current work period and explicitly park all others with specific time and place commitments.
Before evaluating any past decision, reconstruct the information environment that existed at decision time using contemporaneous records rather than memory, then evaluate the decision against that environment only.
When a behavior change fails within one week despite environmental redesign, modify the cue visibility, friction points, or reward structure rather than attributing failure to willpower or abandoning the approach.
For information arriving through multiple transmission steps (forwarded quotes, summarized studies, dashboard metrics), multiply the confidence value at each transmission step rather than treating endpoint confidence as equal to source confidence.
Stack behavioral change interventions by addressing cue visibility first, then friction reduction, then reward design—each layer compounds on the previous rather than operating independently.
Anchor daily externalization to an existing automatic behavior (opening laptop, pouring coffee, post-standup) rather than relying on time-based or motivation-based triggers, because context-stable cues accelerate habit automaticity.
Begin daily externalization with three sentences answering one question ('What am I trying to figure out right now?') for 90 seconds, expanding only after the behavior fires automatically without deliberation.
Document decisions using five fields: what you decided, alternatives considered, information available and missing, optimization criteria, and conditions for revisiting—rather than recording only conclusions.
Before finalizing significant decision records, have an AI argue against your reasoning and append the strongest objection to your record, preserving the full deliberation rather than only your preferred conclusion.
Externalize reasoning chains by writing numbered steps where each step connects to the next through an explicit warrant stating why step N leads to step N+1, marking any transition that relies on unstated assumptions.
When a reasoning chain contains no surprises or pauses during construction—no moments where the next link was weaker than expected—you have transcribed conclusions rather than constructed reasoning and should restart with genuine step-by-step building.
After labeling each emotion, write one sentence identifying what is generating it using causal language ('because'), then check for emotional layers by asking 'What is underneath this?' to surface masking dynamics.
When externalizing emotions, avoid narrative venting ('he did this and then that happened') and instead use structured labeling ('I feel X because Y') to convert fusion into defusion.
Use AI to analyze patterns across multiple emotional externalization entries (recurring emotions, triggers, trends) rather than to label emotions for you, because the regulatory benefit comes from the act of labeling, not from being labeled.
Write implementation intentions beneath each written goal using 'When [specific recurring situation], I will [specific first action]' format, specifying concrete triggers rather than time-based or motivation-based conditions.
Maintain an assumption register with five components for each assumption: the specific testable claim, what would change if false, current evidence for/against it, validation status, and next action to test it—reviewing weekly for active projects.
When a commitment cannot be met, communicate the fact proactively before the deadline and renegotiate terms explicitly, as silent dropping versus explicit renegotiation distinguishes reliable commitment systems from internal intention failures.
Draw mental models as diagrams with boxes for entities and labeled arrows for relationships within ten minutes, because spatial layout forces explicit specification of what connects to what and reveals gaps that prose automatically conceals.
When externalizing mental models, label every arrow with a specific verb describing the relationship mechanism (causes, enables, blocks, amplifies) rather than vague connectors like 'affects' or 'relates to', because unlabeled relationships reveal unexamined assumptions.
After drawing a mental model, audit it for missing feedback loops by tracing whether any effects circle back to influence their own causes, because circular causation governs most complex systems but is invisible to linear thinking.