The full physical exam. Not "am I sick?" but "how fit am I, and where should I train next?"
What Self-Actualization Is (and Isn't)
It IS
It ISN'T
Honest assessment of current state
Cheerful report that everything is fine
Identification of growth opportunities
List of capabilities
Prioritized improvement plan
Unprioritized wish list
Architecture optimization
Code refactoring
Deep meditation + action plan
Quick health check (that's dream/health)
Assessment Dimensions
1. Structural Integrity (Architecture Health)
Metric
How to Measure
Healthy
Concern
Connection validity
All applyTo targets exist
100% valid
Any broken links
Schema compliance
All frontmatter matches spec
Full compliance
Any format violations
Version alignment
Version string consistent across files
All match
Any drift
File organization
Files in correct directories
All correct
Orphaned files
2. Memory Balance (P:E:D Ratio)
The architecture has three memory types. Healthy balance varies by maturity:
Architecture Maturity
Procedural (P)
Episodic (E)
Domain/Skills (D)
New (< 3 months)
~30%
~20%
~50%
Maturing (3-12 months)
~20%
~15%
~65%
Mature (> 1 year)
~15%
~10%
~75%
Current formula: P = instructions count, E = prompts + episodic, D = skills count.
Imbalance signals:
Too many instructions, few skills → "Knows how but not what" (procedural heavy)
Too many skills, few instructions → "Knows what but not how" (domain heavy)
Too many episodic, few skills → "Remembers sessions but never synthesized" (consolidation debt)
3. Knowledge Depth (Skill Quality)
Quality Level
Signals
Action
Deep
Tables with thresholds, real examples, anti-patterns
None needed
Adequate
Has structure and some detail, missing edge cases
Enrich when convenient
Shallow
Capabilities list, one-liner descriptions, no examples
Rewrite priority
Empty shell
Only frontmatter and a description line
Consider removing
The capabilities-list anti-pattern: "Expert in X. Capabilities: validate, detect, assess..." — this adds zero value because an LLM already knows these things generically. Skills must encode specific knowledge.
4. Connection Density (Link Network)
Metric
How to Measure
Healthy
Concern
Avg connections per skill
Total applyTo links / total skills
3-6
< 2 (isolated) or > 10 (over-connected)
Orphan skills
Skills with no applyTo patterns
0
Any
Hub skills
Skills with > 8 connections
1-2 core hubs
> 4 (over-centralized)
Bidirectional coverage
% of connections that are reciprocated
> 80%
< 60%
5. Trifecta Completeness
Component
Purpose
What's Missing If Absent
SKILL.md
What to know (declarative)
No reference knowledge
.instructions.md
How to do it (procedural)
No step-by-step process
.prompt.md
Interactive workflow (episodic)
No guided conversation
Trifecta priority: Not every skill needs a trifecta. Prioritize trifectas for skills that are:
Used frequently (high activation count)
Complex (multi-step processes)
Error-prone (common mistakes without guidance)
6. Growth Trajectory
Metric
How to Assess
Good Sign
Warning Sign
Skills added this month
Count new SKILL.md files
1-5 new skills
0 (stagnant) or > 10 (unfocused)
Skills deepened
Skills edited to add depth
Active enrichment
Only new, never deepened
Global knowledge growth
New GI-* and GK-* entries
Synthesis happening
No global entries (isolated learning)
Trifecta progression
New instructions/prompts
Capability maturing
Skills without procedures
Self-Actualization Session Flow
Inventory — Count all files by type (skills, instructions, prompts, episodic, agents)
Structural audit — Run brain-qa or dream for baseline metrics
Drift is not always bad — growth causes natural ratio shifts. Remediate only when the shift indicates debt (e.g., unconsolidated sessions, stagnant skills, overlapping instructions).
Snapshot Muscle (self-actualization-snapshot.cjs)
Mechanical inputs are produced by .github/muscles/self-actualization-snapshot.cjs. The snapshot is gitignored (D13) — it's regenerable working state. Run before each self-actualization session and re-run after writing the chronicle.
Mechanical (avg / orphans / hubs); LLM interprets distribution
5. Trifecta Completeness
Mechanical (count from dream); LLM picks build priority
6. Growth Trajectory
Mechanical diff; LLM interprets as healthy growth or consolidation debt
Cadence and recency tracking
cogConfig.lastSelfActualization is the fallback field name (D11/D12). The snapshot derives lastSelfActualization from chronicle filenames first; cogConfig only matters when no chronicle exists yet (e.g., heir bootstrap).
session-start.cjs warns when daysSinceLastSelfActualization ≥ 30 (D9).
Always re-run the muscle after authoring a new chronicle so recency.daysSinceLastSelfActualization resets.
