The irreducible epistemic atoms underlying the curriculum. 4,828 atoms across 8 types and 2 molecules
Before sending any consequential text-based message, reread it as a stranger with zero shared context would—no tone, no history, no knowledge of intent—and revise any content that could be misinterpreted in that cold reading.
When emotional content must be conveyed via text, state the emotion explicitly ("I'm frustrated about X") rather than relying on word choice or punctuation to convey tone, because textual cues for emotion fail approximately 45% of the time.
When encountering a frustrating recurring behavior in your organization, map the actual incentive structure (what gets rewarded, punished, and measured) before attributing the behavior to individual character, because most problematic behaviors are rational responses to system design.
When an AI system makes consequential decisions about people (hiring, performance evaluation, resource allocation), audit what organizational context and metrics trained the system before evaluating algorithm quality, because AI inherits and amplifies the biases of the measurement system.
Configure workspace lighting to match cognitive mode—bright, cool-temperature light (5,000-6,500K) for analytical work requiring convergent thinking; dim, warm light (2,700-3,000K) for creative work requiring divergent thinking.
Before committing to a private written position for any group decision, externalize your reasoning and conclusion before the group discussion begins, then compare it to your post-discussion position to detect social influence effects.
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.