Rules
1,053 rules · page 21 of 22
Name archived files as [YYYY-MM] [Type]…
Name archived files using the pattern '[YYYY-MM] [Output Type] — [Descriptive Title]' to enable chronological sorting, category filtering, and keyword search through the filename itself, making files findable even if metadata systems fail.
Archive only the final delivered version — drafts go…
Archive only the final delivered version of each output; if you must preserve drafts for legal or regulatory reasons, keep them in a separate 'drafts' subfolder within the archive entry, clearly labeled as non-canonical versions.
Keep directional decisions human…
When using AI to reduce production costs, maintain human control over directional decisions (what to produce, what argument to make, what judgment to render) while delegating mechanical execution (format conversion, draft expansion, repurposing) to preserve sovereignty through augmentation not replacement.
Escalate unresolved patterns from weekly to monthly review…
When a pattern appears in two or more consecutive weekly reviews without resolving, tag it as a monthly review agenda item rather than attempting to solve it at the weekly level.
Limit monthly commitments to 3-5 specific outcomes…
Limit monthly commitments to three to five specific outcomes that would constitute success, as this constraint forces genuine prioritization and prevents the diffusion that comes from maintaining ten or fifteen simultaneous goals.
Design weekly adjustments as structural changes…
Design next week's adjustments as structural changes (moving time blocks, changing environments, creating new defaults) rather than willpower-based intentions, since persistent patterns indicate system problems not motivation problems.
Record actual vs. planned progress as a delivery rate (%)…
For each active goal in monthly reviews, record actual progress against planned progress as a numerical delivery rate (e.g., 50% completion) and track this ratio over time to calibrate future estimation accuracy.
Rate each life area…
During monthly reviews, assign each major life area (health, relationships, career, finances, learning, creativity) a three-level rating (thriving/maintaining/declining) to detect systematic neglect that goal-level tracking misses.
At 40-60% delivery rates, halve commitments or double…
When monthly reviews consistently show 40-60% delivery rates, halve your commitments or double allocated time rather than setting aspirational targets, as realistic targets build self-efficacy while repeated failure erodes it.
Three consecutive months on the "didn't get to" list =…
When a goal appears in your monthly 'didn't get to' list for three consecutive months, either restructure your commitments to create protected capacity or acknowledge the goal is abandoned in practice and remove it from your active list.
Rate each critical assumption…
During quarterly assumption testing, rate each critical assumption as confirmed, uncertain, or falsified based on accumulated evidence, and require immediate strategic response for any falsified assumption.
Formally retire at least one commitment every quarter…
Eliminate at least one active commitment during every quarterly review through formal retirement (not deprioritization), as most people only add commitments and never systematically prune, producing portfolio dilution over time.
Write a one-paragraph quarterly thesis
Write a one-paragraph strategic thesis for the coming quarter that specifies what you are optimizing for and what success looks like in 90 days, providing the frame within which weekly and daily reviews operate.
Annual review phase 1
When conducting an annual review, go through your calendar month-by-month for 90 minutes, marking people, activities, and commitments with + for peak positive experiences and - for peak negative experiences before attempting any analysis.
Apply the Stoic audit in annual reviews
During annual reviews, apply the Stoic audit by asking 'Did I live well this year?' focusing on alignment between stated values and actual behavior rather than productivity or goal achievement.
Retire reflection questions that produce the same answer 3…
When a reflection question produces the same predictable answer three consecutive times, retire it temporarily and rotate in a replacement because stale questions optimize neural search paths without generating insight.
Replace "Why am I such a pushover?"…
Replace characterological reflection questions like 'Why am I such a pushover?' with behavioral ones like 'What did I do when the client pushed back on the deadline?' to generate usable data instead of identity narratives.
Share completed reflective writing with AI and ask…
After completing reflective writing, share it with AI and ask 'What assumptions am I making in this reflection that I have not examined?' to surface premises treated as facts.
Three-pass pattern spotting
Conduct pattern-spotting reviews in three distinct passes: (1) mark anything that recurs without interpretation, (2) cluster codes into emotional, behavioral, situational, outcome, and avoidance patterns, (3) check each against counterexamples before naming.
Read 4-8 weeks of entries in a single sitting for pattern…
Read 4-8 weeks of reflective entries in a single sitting during pattern-spotting reviews rather than individually as written, to enable parallel access to temporally separated experiences.
Weekly
At each weekly review, allocate five minutes to search the archive for one keyword relevant to that week and read 2-3 past entries; at monthly review, read all entries from the same month one year ago to detect how priorities have shifted.
Verify AI-detected patterns with the same rigor as your own
When AI identifies patterns in your reflective archive, treat AI-generated patterns with the same verification rigor as your own: check counterexamples, count instances, demand 3+ independent occurrences, and test for narrative imposition on random variation.
Label self-judgmental thoughts during review…
When self-judgmental thoughts arise during review, label them explicitly ('That is a judgment, not a finding') and return to behavioral description rather than attempting to eliminate them.
Test success principles for survivorship bias
Test success principles against survivorship bias by asking 'If I did exactly this again under slightly different conditions, would I expect the same result?' before treating them as robust patterns.
Three weeks of the same failure = system design problem…
When the same action-level failure recurs weekly for three weeks, treat it as definitive evidence of system design failure rather than execution failure and shift to structural redesign.
Before sharing a reflection, write 3 sentences specifying…
Before sharing a reflection, write three sentences clarifying what type of support you want—not a solution, but a specific form of assistance like 'hearing how this sounds' or 'identifying what I might be missing.'
Map 2-5 thinking partners
Maintain a personal map of 2-5 thinking partners, documenting for each: what topics they're suited for, what topics are inappropriate due to competing interests or power dynamics, and what type of support they naturally provide.
The topic you skip or write safe answers about is the most…
When you skip a reflection prompt or write something safe instead of what you actually got stuck on, treat the skipped topic as the most valuable reflection target for that session.
Resistance audit
Conduct a 'resistance audit' by listing topics from the last five reviews, then separately listing topics that were active in your life but absent from your reflections, then writing for ten minutes on the highest-discomfort absent topic.
Never edit or delete old reflections containing errors…
When reviewing old reflections that contain errors or embarrassments, do not edit or delete them—the archive's value lies in preserving what you actually thought at the time, enabling detection of belief evolution and systematic biases.
When reflection quality plateaus, shift focus
When reflection quality plateaus after months of practice, change the focus of your practice by shifting from events to systems, from behavior to beliefs, from solo to dialogic reflection, or by retiring stale prompts and designing new ones targeting the next depth level.
Scale tool evaluation effort to switching cost…
Match tool evaluation effort to irreversibility, not complexity or anxiety—allocate maximum deliberation to tools where switching costs are high (years of data, muscle memory, workflow dependencies) and minimal deliberation to tools where switching is cheap.
When two tools hold the same data, designate one canonical…
When two tools in your stack both maintain authoritative copies of the same data type, designate one as canonical (single source of truth) and demote the other to either capture-point (temporary holding before transfer) or read-replica (displays data but cannot edit) status to prevent sync drift.
Automate the single highest-frequency manual data transfer…
Automate the single highest-frequency manual data transfer in your tool stack first, verify it works reliably for one week, then iterate to the next highest-frequency transfer—sequential automation prevents building a fragile web of interdependencies that becomes unmaintainable.
Choose open portable formats (Markdown, CSV, JSON) over…
Choose tools that store data in open, portable formats (Markdown, CSV, JSON, plain text) over proprietary formats even if the proprietary tool has more features, because migration cost compounds daily with every new piece of data added to a locked format.
Define the specific job the tool must do in one sentence…
Define the specific job you're hiring a tool to do in one concrete sentence before evaluating candidates—vague category labels ('note-taking app') produce feature-based comparison while specific job definitions ('capture ideas on phone, retrieve on laptop at night') produce fitness-based selection.
Limit tool candidates to 2-3 before evaluating — more…
Limit active tool candidates to two or three options maximum before evaluation—evaluating more options increases decision time logarithmically while decreasing satisfaction regardless of whether you choose objectively better tools.
Invest in mastery proportional to frequency × impact…
Match deep tool mastery investment to usage frequency times behavioral impact—invest months of deliberate practice in tools you use daily for consequential work, limit learning to competence for infrequent or low-stakes tools.
Monthly depth audit
Conduct a monthly depth audit of your primary tool by identifying ten capabilities you've never used, rating their potential value, practicing the top three for one week, then integrating the useful ones—compound learning beats sporadic feature discovery.
One tool as hub for thinking/synthesis, others as spokes…
Designate one tool as hub for all thinking and synthesis work, then connect other specialized tools as spokes that feed into or draw from the hub—hub-and-spoke architecture minimizes integration complexity (N connections not N²) while preserving single source of truth.
Transfer from temporary capture to canonical source within…
Transfer captured information from temporary locations (notebooks, voice memos, quick-capture apps) to canonical sources within 24 hours—longer delays allow the temporary location to become a de facto second source of truth through repeated consultation.
Calculate feature-learning ROI
When a tool's advanced features would save time but require learning investment, calculate break-even: if the feature saves X minutes per use and requires Y hours to master, invest if (frequency × X) > Y within 90 days—shorter payback windows justify learning effort.
For execution (not coordination), choose task tools…
Choose task management tools that support if-then implementation intentions and context tagging over feature-rich project management tools when your primary need is execution not coordination—simpler tools with behavior-triggering structure beat complex tools optimized for planning.
Map tool stack as a graph
Map your tool stack visually with tools as nodes and data transfers as edges, labeling each edge as manual/automated and daily/weekly/monthly—this diagram reveals bottlenecks (high-frequency manual transfers) and redundancies (overlapping functions) that narrative inventory cannot surface.
One-week removal test for tool necessity — if you don't…
Test whether a tool is earning its place in your stack by temporarily removing it for one week—if you don't consciously miss specific capabilities (not just habit), the tool is redundant and should be permanently removed to reduce maintenance surface.
Consolidate overlapping tools by integration quality…
When two tools provide overlapping functionality, consolidate to the tool that integrates better with your stack even if the eliminated tool has superior standalone features—integration quality dominates feature quality in compound workflows.
Run old and new tools in parallel for 2-4 weeks during…
When migrating tools, run both systems in parallel for 2-4 weeks with new data going to the new tool and old data accessed from the old tool, ending only when the parallel period confirms the new tool handles your daily needs.
Test migration before committing
Before migrating any tool, test the destination by importing 20-50 representative items and examining every imported item in detail for formatting survival, metadata preservation, and link integrity.