Rules
1,053 rules · page 9 of 22
Before changing a team schema, map what it supports…
Before attempting to change a shared team schema, map what the current schema supports—which decisions it enables, what coordination it simplifies, and what would break if it disappeared—to understand its load-bearing function.
Schema change scales with group size
Scale your timeline expectations for schema change with group size—weeks for pairs, quarters for 50-person orgs, years for industries—and measure progress in behavioral change rather than stated agreement.
When your framework can't even formulate the right…
When your schema can no longer formulate the questions you need to ask about a domain, treat this incommensurability as a signal that the framework itself has become a cage requiring replacement.
Log schema changes with 4 fields
Write schema evolution log entries with four mandatory fields - date, schema affected in original language not current interpretation, specific triggering evidence or encounter, and the replacement belief - to defeat hindsight bias through fixed external records.
Keep schema evolution logs minimal (4 fields) and review…
Maintain schema evolution logs with minimum viable entries of four fields - date, schema affected, what changed, and what prompted the change - reviewed weekly, to generate a dataset about cognitive patterns that introspection alone cannot produce.
A schema unreviewed for 6 months is like an unupdated…
Treat any schema that has gone six months without deliberate review the same as a software dependency unupdated for six months - not necessarily broken but requiring verification before continued reliance.
Structural schema change is genuine growth; adding…
Measure personal development quality by asking whether any practice changed a schema's structure or merely added information to existing schemas - structural change is genuine growth, information addition is not.
Document what the old belief was protecting — the lost…
For each evolved schema, document not just old and new versions but also what the old belief was protecting or enabling, because identifying the lost function reveals emotional costs of revision and increases honesty about unrevised beliefs.
An unfalsifiable schema is a belief system, not a model…
When a schema cannot specify any observation that would falsify it, classify it as a belief system rather than a testable model and flag it for replacement or constraint.
Require 3 recent decision examples for each schema — fewer…
In your schema inventory, require behavioral proof by identifying three decisions from the last month that each schema governed—if you cannot find three, reclassify the schema as aspirational rather than operational.
Map schema dependencies in both directions…
For each important schema, map both its prerequisites (what it depends on) and its dependents (what depends on it), then flag schemas appearing most frequently as dependencies for regular review.
When two schemas both hold after evidence review, build…
When schema conflict persists after examining evidence, build a conditional routing rule specifying the exact conditions under which each schema applies rather than attempting to pick a universal winner.
Ask 'how long have similar tasks taken?' not 'how long…
When planning task duration, deliberately switch from inside-view scenario construction to outside-view base-rate consultation by asking 'how long have similar tasks taken?' instead of 'how long will this take?'
Track where each schema came from — prioritize…
For each schema you operate on, document source provenance in a single field—specific person, book, cultural norm, direct experience, or unknown—then prioritize verification effort by source weakness.
Evaluate sources by searching about them, not by reading…
Apply lateral reading by immediately opening new tabs to search for independent information about a source rather than evaluating the source by reading the source itself, because external assessment outperforms internal coherence checking.
When AI suggests a framework, ask for sources, boundary…
When AI assistants suggest frameworks or schemas, respond by asking for original research sources, boundary conditions, and strongest counterarguments rather than accepting or rejecting the claim directly.
Limit metacognitive recursion to two levels during live…
During real-time execution of high-stakes tasks, defer metacognitive recursion beyond two levels to avoid working memory saturation—externalize to enable deeper inspection.
When your self-explanation diverges substantially…
When your explanation of your own behavior differs from an external observer's explanation by more than surface framing, treat the divergence as high-confidence evidence of a metacognitive blind spot requiring investigation.
Limit knowledge graph relationship types to 5-7 — more…
When building knowledge graphs, limit relationship type taxonomies to 5-7 types rather than attempting comprehensive ontological coverage, because classification overhead beyond this threshold produces diminishing informational returns while increasing maintenance cost.
Bridge domains through structural patterns, not surface…
When creating bridge nodes between domains, link to structural patterns (diminishing returns, feedback delays, threshold effects) rather than surface metaphors (companies as bodies), because only structural correspondence enables valid inference transfer across contexts.
Typed edges enable AI reasoning; embeddings only find…
When AI systems traverse your knowledge graph, maintain typed relationship labels with explicit predicates rather than relying on semantic similarity alone, because typed edges enable logical reasoning while embeddings only surface associative proximity.
When opening a hub note, check backlinks for 2 minutes…
When opening a hub note (one you reference frequently), immediately check its backlinks panel and spend two minutes reading the incoming references to surface connections you had forgotten.
If you can't articulate the link in a complete sentence…
When building connections between notes, test each link by asking whether you can articulate the relationship in a complete sentence—if you cannot, delete the link rather than inflating density metrics artificially.
Invest quarterly maintenance in your top 5% most-connected…
Identify your top 5% of notes by connection count and schedule quarterly reviews where you verify each hub note is current, accurate, and well-linked, investing maintenance effort proportional to structural importance.
Distinguish real hubs (genuine cross-domain centrality)…
Distinguish real hubs (concepts earning centrality through genuine cross-domain relationships) from artificial hubs (index pages linking to everything in a category) by testing whether removing the hub would sever meaningful conceptual pathways or merely convenience pathways.
A bridge node must generate insights in BOTH connected…
Test each bridge node by verifying it generates novel predictions or actionable insights in both connected domains—if it only produces a sense of similarity without bidirectional inference, demote it to metaphor status or delete it.
Find your three densest knowledge clusters by internal…
Identify your three densest knowledge clusters by examining which groups of notes link heavily to each other but sparsely to the rest of the graph, then label each cluster based on observed structure rather than imposed categories.
If a cluster only exists because you forced links…
When a cluster appears only after you force-link unrelated notes to create it, delete those artificial connections because imposed clusters destroy the diagnostic value of emergent structure.
When your graph's clusters contradict your self-narrative…
When your graph's cluster structure contradicts your self-narrative about expertise, trust the cluster structure because it reflects actual linking behavior while self-assessment is systematically distorted.
For each pair of clusters with no bridge, identify…
List your major clusters and for each pair ask what connects them; when the answer is 'nothing' or 'one weak edge,' write down the bridge concept that should connect them as your next learning target.
Show your graph to domain experts and ask what's missing…
Show a portion of your knowledge graph to someone with domain expertise and ask what's missing to detect unknown unknowns that your graph's topology cannot reveal through internal inspection alone.
Start your knowledge graph now with five nodes — five…
Start building your knowledge graph with your current five nodes rather than waiting for critical mass, because a graph with five nodes and eight edges already delivers more value than five hundred isolated notes.
Use Shneiderman's overview-first protocol for graph…
Apply Shneiderman's three-phase visualization protocol to graph exploration: overview first without reading labels, zoom and filter to examine specific clusters, then details-on-demand for individual nodes.
Use AI for structural gap detection — ask it to find…
Use AI to identify conspicuously absent concepts that would bridge multiple existing graph nodes rather than generic topic recommendations.
An unmaintained knowledge system is a degraded asset…
When external knowledge structures stop being actively traversed, maintained, and integrated into daily reasoning, treat them as degraded assets rather than cognitive extensions.
Link contradicting ideas together in your graph — spatial…
Deliberately link contradicting ideas in your knowledge graph rather than keeping them in separate domains, because spatial proximity forces the cognitive confrontation that compartmentalization prevents.
When performance is stable with no bottleneck, stop…
When measurement data shows stable satisfactory performance with no identifiable bottleneck, redirect optimization effort to a different system rather than continuing to optimize the current one.
Use the cascade test — 'If I resolved this, what else…
Apply the cascade test to contradictions by asking 'If I resolved this, what else would have to change?' to distinguish surface contradictions (low dependency count) from deep contradictions (high dependency count).
Scale contradiction holding periods by cascade depth — one…
Set holding periods for contradictions based on cascade depth: one week for low cascade, two to four weeks for medium cascade, one month or longer for high cascade.
During a holding period, capture incubation notes on each…
During a contradiction holding period, write brief notes whenever the contradiction surfaces capturing what triggered it and what you noticed, without attempting resolution.
When a holding period expires without clarity, extend…
When holding period ends, extend the hold rather than forcing resolution if you cannot yet articulate a missing variable or synthesis, because premature resolution defeats the purpose of incubation.
Audit your last seven days of behavior against stated…
Audit the last seven days of actual behavior (calendar, screen time, spending, energy allocation) against each stated value to calculate revealed preferences, scoring alignment from 1-10.
When behavior contradicts values, investigate the reward…
When discovering that behavior contradicts stated values, investigate the actual reward structure driving behavior rather than increasing willpower or restating values more emphatically.
Ask what competing value your misaligned behavior reveals…
For each identified values-behavior gap, ask what competing value the behavior actually reveals and what would need to change in environment, habits, or defaults for alignment.
True resolution satisfies both sides fully — if either…
Test whether a resolved contradiction is genuine innovation rather than compromise by verifying that both original requirements are fully satisfied, not partially abandoned.
Design early warning indicators for polarity drift…
Design early warning indicators for polarity drift by identifying the characteristic downsides of each pole, then monitor for those downsides to trigger course-correction before crisis.
Check subgroup relationships before aggregating…
Before aggregating data across subgroups, check whether the relationship holds within each subgroup independently, as aggregate patterns can reverse at the disaggregated level (Simpson's paradox).
Timestamp beliefs with validity windows — convert apparent…
Label each belief in your knowledge system with validity windows specifying the time period during which the belief held, converting apparent contradictions across time into explicit version transitions.