Rules
1,053 rules · page 7 of 22
Attach 'When [situation], I will [action]' to every…
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.
Track assumptions in a five-field register
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 will be missed, renegotiate proactively…
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 boxes and labeled arrows within 10…
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.
Label diagram arrows with specific verbs — 'causes'…
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.
Audit every model diagram for missing feedback loops…
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.
Write blockers as 'I cannot [action] because [obstacle]'…
Write blockers in the form 'I cannot [specific action] because [specific obstacle]' immediately upon noticing friction to convert ill-structured problems into solvable ones.
Split compound blockers into separate obstacles…
Decompose compound blockers into separate obstacles with independent owners and solutions before attempting resolution, because monolithic blockers resist action through perceived complexity.
Structure learning notes as: claim, evidence, connection…
Write learning in the structure: claim (one sentence, your words), evidence (why believe it), connection (how it relates), question (what's unresolved) to force generation rather than transcription.
Write your explanation before asking AI — generation…
When using AI for learning, write your own explanation first, then use AI interrogation to find gaps, then revise—never let AI write the initial explanation because reading AI output does not produce the generation effect.
Capture feedback within 60 minutes in five structured…
Capture feedback within 60 minutes of receiving it using structured fields (date, source, verbatim content, emotional reaction, specific behavior) before memory reconstruction distorts the signal.
Classify failures into three types before analysis…
Categorize each failure as preventable (process deviation), complex (novel factor interaction), or intelligent (frontier experiment) before analysis, because different failure types require different questions.
Weekly
Audit thinking environments weekly by comparing actual conditions against documented specifications to detect entropy, because environmental decay through accumulated objects, browser tabs, and permission drift is constant and unnoticed without structured review.
Document your knowledge system in 5 parts
Document system operations in five components—capture rules, processing workflow, retrieval method, review protocol, and evolution history—because each component addresses a distinct failure mode in knowledge system sustainability.
Cross-reference domains in weekly review
During weekly reviews, cross-reference externalized domains to detect contradictions—compare stated priorities against time allocation, goals against commitments, assumptions against failure analyses—because isolated review of each domain misses the conflicts that degrade decision quality.
Test: can you reconstruct the reasoning without the AI?
Test AI integration by verifying whether interactions increase your independent understanding—if you cannot reconstruct the reasoning without the AI, the tool is replacing cognition rather than extending it.
Give AI your full knowledge system, not isolated questions
Feed complete externalized system context to AI assistants rather than isolated queries, because AI reasoning quality scales with the completeness and structure of the personal knowledge base it can traverse.
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.
If you can only say 'they go together' but not HOW one…
For each candidate enabling relationship, articulate the specific mechanism through which one condition creates another; if you can only state correlation ('they go together') rather than mechanism, treat it as association not enabling.
Steel-man both sides of a contradiction before resolving…
Before attempting to resolve any contradiction between beliefs, construct the strongest possible version of each side that the most informed advocate would recognize and endorse.
Count only independent evidence lines for confidence — ten…
When evaluating confidence in a belief, count only genuinely independent lines of evidence—sources that do not share origins, methods, or assumptions—rather than total source count, because correlated sources compound confidence on a single foundation.
Ground every abstraction with 3+ examples from different…
Connect each abstract concept in your knowledge system to at least three concrete examples from different domains, because single examples invite surface-feature overgeneralization while multiple examples force attention to shared structural patterns.
Fix problems at the deepest causal link you can reach…
Intervene in causal chains at the deepest link where you have both ability and authority to act, as fixes at deep links prevent the entire chain from firing while surface fixes only address symptoms.
After completing a relationship map, write what…
After drawing a complete relationship map, write three to five sentences describing the structural story—focusing specifically on what was invisible before you drew the map rather than summarizing what you already knew.
Check transitivity before inferring through chains…
When a relationship type is mathematically transitive (like 'is greater than' or 'is ancestor of'), you can safely infer the endpoint connection from a chain; when it is not transitive (like 'is friend of' or 'is close to'), you must verify endpoint relationships directly rather than inferring them.