Concepts
The irreducible epistemic atoms underlying the curriculum. 4,828 atoms across 8 types
Extract and classify every assumption before committing…
Before committing resources to a plan, extract every assumption it depends on and classify each by importance (would the plan fail if this is wrong?) and vulnerability (how likely is this to be wrong?), then test assumptions marked both high-importance and high-vulnerability first.
Decompose tasks into steps before estimating — holistic…
When unpacking task estimates, decompose complex tasks into component steps before estimating duration—unpacking improves accuracy by forcing visibility of dependencies and transitions that holistic estimation skips.
Place 20% time buffers around your constraint step…
Add 20% time buffers around constraint activities that fail or degrade most frequently under pressure, rather than distributing slack evenly across all workflow steps.
Keep commitments at 70-85% of capacity — the 15-30% slack…
Maintain commitment-to-capacity ratio between 0.70 and 0.85 to preserve system stability, keeping 15-30% of capacity uncommitted as structural buffer for variance absorption.
Rate capacity 1-5 in your first 30 minutes — then select…
Rate your daily capacity on a 1-5 scale within the first 30 minutes of your workday before starting any tasks, using the rating to select which tier of work plan to execute that day.
When commitments exceed capacity, only three fixes work
When commitment-to-capacity ratio exceeds 1.0, intervene only through cut (remove commitment entirely), defer (move to future period with capacity), or delegate (transfer to someone else)—productivity techniques cannot solve overcommitment caused by arithmetic mismatch.
Distribute work unevenly across the week — load…
Allocate weekly focused work hours unevenly based on predicted daily capacity levels (more hours to high-capacity days, fewer to low-capacity days) rather than distributing work uniformly across all days.
Plan from your average capacity, not your best day — five…
After measuring five days of actual focused work time, use the daily average (not the best day or hoped-for number) as your baseline planning capacity for all future scheduling decisions.
Decompose peak-day tasks and pre-load early components…
Decompose tasks due on peak-load days into components that can start independently, then pre-load early components into low-load days to smooth temporal distribution.
Front-load highest-priority work — end-of-week…
Place most important committed work early in the week and early in the day so that end-of-week and end-of-day buffers can absorb delays without threatening highest-priority deliverables.
Calibrate buffers by consumption rate — above 80% means…
Track weekly buffer consumption rate—if consistently consuming more than 80% of buffer, increase buffer size; if consistently consuming less than 20%, buffer can be tightened.
Alternate between cognitive pools — creative, then…
Sequence work blocks across different cognitive pools (creative, then analytical, then social, then administrative) rather than stacking same-type work, to allow depleted pools to recover while fresh pools carry the load.
Measure honest current output over 3 representative days…
When building capacity from a new baseline, measure current honest output over at least three representative days—not best days or aspirational targets—to establish accurate starting point.
Map annual capacity month by month — distribute…
Create annual capacity maps rating each month 1-5 based on historical data, then distribute annual commitments proportionally to predicted monthly capacity rather than dividing by twelve uniformly.
A ratio above 1.0 is mathematical proof of failure — no…
Maintain a commitment-to-capacity ratio below 0.85 to accommodate variance, treating any ratio above 1.0 as mathematical proof that some commitments will fail regardless of willpower or prioritization.
Build support structures before the trough hits…
Pre-load support structures during the phase before a known cyclical trough rather than attempting to maintain behavior through willpower during the trough itself.
Extract your replicable success pattern from 5 fields…
For each genuine success in the past two years, document conditions present, behaviors that differed from defaults, people involved, internal state, and 48-hour setup—then surface elements appearing in three or more instances as your replicable success pattern.
Before starting a project, cross-reference your success…
Cross-reference your success pattern elements against your upcoming project plan before starting work, building in the overlapping conditions deliberately rather than hoping they emerge, as replication requires engineering.
Before waiting on a decision, write tripwires — what…
Write explicit tripwire conditions specifying what observations would resolve ambiguity in each direction before entering any strategic waiting period.
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.
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.
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.
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.
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.