Rules
1,053 rules · page 6 of 22
Use 5-minute cyclic sighing before stressful situations…
Deploy 5-minute cyclic sighing (prolonged exhalations at ~6 breaths per minute) before difficult meetings or after stressful news to reverse stress-induced perceptual narrowing.
Under stress, ask AI what you are not seeing…
When using AI during high stress, prompt with 'I am stressed and may be experiencing tunnel vision—what am I likely not seeing?' rather than 'prove my interpretation is right.'
Check whether you are estimating frequency or vividness
When making frequency or probability estimates, pause and ask: 'Am I estimating actual frequency, or how easily I can picture this?' then look up the base rate before deciding.
Before big decisions, separate the recent trigger…
Before any consequential decision, populate two mandatory fields: the recent event driving current feeling, and the base rate/historical trend across the full relevant time window.
When a vivid case drives certainty, ask for the base rate…
When a vivid individual case makes you feel certain about probability, explicitly ask 'what is the actual frequency of this event in the relevant population?' before forming any judgment.
Frame pre-mortems as explanations of failure…
Frame pre-mortem prompts as 'It is [future date]. This has failed completely. Write why.' rather than 'What could go wrong?' to shift cognition from speculation to explanation.
If your search for disconfirming evidence cannot change…
When searching for disconfirming evidence, if your search could not have actually changed your mind, you performed a ritual not genuine disconfirmation—redesign the search until failure is possible.
Ask for behavioral feedback, not character evaluations
Frame feedback requests as specific behavioral questions ('What do I consistently do that I probably don't realize?') rather than character evaluations to keep feedback at task level instead of identity level.
Classify evidence by diagnostic value before updating…
When new evidence arrives, classify it by diagnostic value before updating—ask whether you'd see this evidence regardless of belief truth versus only if belief were true/false.
After 15-20 judgments, check if your errors are bias…
After accumulating 15-20 judgments in the same domain, analyze whether errors cluster directionally (bias requiring correction factor) or scatter randomly (noise requiring aggregation).
Keep a two-week bias journal to build your personal error…
Conduct a two-week bias journal recording significant judgments with confidence levels, then categorize errors by direction and type to build your personal bias profile.
Write a specific pre-correction procedure for each known…
For each identified bias in your profile, write a specific pre-correction question or procedure to execute before acting on judgments in that domain.
Apply your documented error factor to timeline estimates…
After identifying that you are systematically overconfident on timelines by X%, multiply your initial timeline estimates by (1 + X/100) before stating them publicly.
After four weeks, check if your beliefs under-react…
After four weeks of belief tracking, examine whether beliefs barely moved despite evidence (conservatism) or swung dramatically on single data points (base rate neglect) to identify domain-specific updating patterns.
Run a five-question context scan before interpreting…
Before interpreting any piece of information—a message, a metric, a statement, a data point—run a five-question context scan: What environment am I in? What role am I occupying? What just happened that might color my perception? What are the goals (mine and others')? What assumptions am I importing from a different context?
Use a 3-step context switch
When switching between cognitive contexts, implement a three-step loading protocol: (1) close the current context by writing a one-sentence summary and noting open loops (30 seconds), (2) create a transition gap of deliberate non-engagement (60 seconds), (3) load the new context by reviewing relevant notes and orienting before producing (60 seconds).
Record five elements at the moment of every significant…
Record decision context at the moment of commitment using five elements: (1) decision statement, (2) forces/constraints/emotions active at choice point, (3) expected consequences with timeline, (4) confidence level 1-10, (5) review trigger date—before hindsight bias can rewrite your reasoning.
Read the original decision context before evaluating…
When reviewing a past decision, read the original context record before evaluating the outcome, because evaluating outcome first allows hindsight bias to contaminate your assessment of whether the reasoning was sound.
Document expectation vs. reality in cross-cultural…
When you experience confusion, friction, or judgment in a cross-cultural interaction, document three elements before reacting: (1) what you expected, (2) what actually happened, (3) what cultural assumption might explain the gap—treating the collision as diagnostic data about invisible defaults.
If you deliberated for 60+ seconds, the reasoning is worth…
Set the threshold for decision context documentation at any choice where you deliberated between options for more than sixty seconds, because if you considered alternatives consciously, the reasoning is worth preserving against memory reconstruction.
Spend 60-90 seconds orienting before producing…
During context loading for complex cognitive work (coding, writing, design), spend the first 60-90 seconds orienting through review of previous state before attempting to produce output, as the cognitive system requires initialization time before it can operate at full capacity in that domain.
Domain facts need aggressive updating; structural…
Distinguish domain-specific facts (treatment protocols, software frameworks, market conditions) requiring aggressive temporal updating from structural principles (logic, mathematics, core psychological mechanisms) where age indicates Lindy-tested robustness, applying opposite update strategies to each type.
Before adopting a practice from another era or scale…
When importing best practices or frameworks from another era or scale, explicitly verify that the contextual conditions (organizational size, technological infrastructure, market maturity) that made the practice optimal still hold before adopting it.
Require independent operational definitions of key terms…
For each high-stakes word in decisions or commitments (quality, ownership, alignment, done, strategy), require independent operational definitions from each stakeholder before proceeding, then compare and reconcile the definitions explicitly.
Reread consequential messages as a contextless stranger…
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.
State emotions explicitly in text — 'I'm frustrated about…
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.
Map the incentive structure before blaming individuals…
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.
Audit the training data before trusting AI decisions about…
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.
Bright cool light for analytical work, dim warm light…
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.
Write your position before group discussion — compare…
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.
Deliberately rotate information sources outside…
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.
Chesterton's Fence
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 fails, mentally reinstate the encoding context…
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.
Check every important message for SCQA
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 compete, designate one as primary…
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.
Reconstruct the information environment from records…
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 behavior change fails, adjust the cue/friction/reward…
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.
Multiply confidence at each transmission hop — multi-step…
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 behavior change
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.
Attach daily writing to an existing automatic behavior…
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.
Start daily writing at 3 sentences, 90 seconds
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.
Record decisions with five fields
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.
Have AI argue against your decision before finalizing…
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.
Write reasoning as numbered steps with explicit warrants…
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.
If writing your reasoning produced zero surprises…
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 an emotion, trace the cause then ask what…
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.
Use structured labeling instead of narrative venting…
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.
Let AI find patterns across journal entries — label…
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.