# Self Actualization

> Comprehensive cognitive self-assessment — honest evaluation of architecture health, growth, and optimization opportunities

- Skill: `fabioc-aloha/self-actualization` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add fabioc-aloha/self-actualization`
- Raw SKILL.md: https://api.skillmd.com/api/skills/fabioc-aloha/self-actualization/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Marketing & Growth
- Author: fabioc-aloha (https://skillmd.com/u/fabioc-aloha)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/fabioc-aloha/self-actualization

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# Self-Actualization Skill

> 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

1. **Inventory** — Count all files by type (skills, instructions, prompts, episodic, agents)
2. **Structural audit** — Run brain-qa or dream for baseline metrics
3. **Depth sampling** — Read 5-10 smallest skills, assess for shallow content
4. **Balance calculation** — Compute P:E:D ratio, compare to maturity target
5. **Network analysis** — Check connection density, find orphans and hubs
6. **Growth review** — Compare to last self-actualization (what changed?)
7. **Priority list** — Rank top 3-5 improvements by impact
8. **Action plan** — Assign each to a session type (quick fix, deep-dive, trifecta build)

## Scoring Guide

| Dimension | Weight | Score 1-5 |
| --------- | ------ | --------- |
| Structural Integrity | 20% | 5=perfect, 1=broken links everywhere |
| Memory Balance | 15% | 5=ideal ratio, 1=severely skewed |
| Knowledge Depth | 25% | 5=all deep, 1=mostly shallow |
| Connection Density | 15% | 5=well-connected, 1=isolated islands |
| Trifecta Completeness | 10% | 5=appropriate coverage, 1=skills only |
| Growth Trajectory | 15% | 5=healthy momentum, 1=stagnant |

**Overall**: Weighted average. > 4.0 = healthy. 3.0-4.0 = needs attention. < 3.0 = urgent.

## Relationship to Other Rituals

Meditation is the foundational ritual. Self-Actualize is a deep meditation variant.

| Ritual | Frequency | Depth | Relationship |
| ------- | --------- | ----- | ------- |
| **Meditation** | Per-session | Moderate | Foundational — the primary ritual |
| Dream | On-demand | Structural | Diagnostic — chains after meditation sometimes |
| **Self-Actualize** | **Monthly** | **Deep** | **Deep meditation; dream is an optional diagnostic input (D8)** |

### Session Flow

1. (Optional) Dream for structural baseline → 2. Run snapshot muscle → 3. 6-dimension assessment → 4. Meditation 5 R's (persist findings as a chronicle)

## Drift Remediation Protocol

Self-actualization detects documentation drift — when implementation evolves faster than docs.

| Drift Type | Detection | Fix |
|------------|-----------|-----|
| Version references | Scan for outdated version strings in `.github/` | grep + multi-replace |
| Documentation counts | Compare `copilot-instructions.md` counts vs actual file counts | Update counts |
| Memory balance | P:E:D ratio outside maturity target | Consolidate episodic, enrich skills, prune redundant instructions |

**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.

### Schema (`.github/quality/self-actualization-snapshot.json`)

| Field | Meaning |
|---|---|
| `schemaVersion` | Snapshot schema version (current: 1) |
| `generatedAt` | ISO timestamp |
| `recency.lastSelfActualization` | Date of newest `self-actualization-YYYY-MM-DD.md` chronicle (D7-pattern) |
| `recency.source` | `chronicle-filename` \| `cogConfig` \| `none` |
| `recency.daysSinceLastSelfActualization` | Days since last chronicle |
| `recency.stale` | `true` if `≥ 30` days (D9) |
| `dimensions.structuralIntegrity.dreamAvailable` | `false` if `dream-report.json` missing (D8 — dim 1 then N/A) |
| `dimensions.structuralIntegrity.brokenRefs` / `trifectaIssues` / `health` | Pulled from dream report when present |
| `dimensions.memoryBalance.ratio.{P,E,D}` | Procedural / Episodic / Domain mix |
| `dimensions.knowledgeDepth.samples[]` | Smallest N skills for LLM-judged depth pass (D16: dim 3 LLM-judged) |
| `dimensions.connectionDensity.{avgPatternsPerSkill, orphanSkills, hubSkills}` | applyTo network shape |
| `dimensions.trifectaCompleteness.trifectaIssueCount` | From dream report (or null if dream unavailable) |
| `dimensions.growthTrajectory.diff` | Per-artifact-type delta vs prior snapshot, or `priorAvailable: false` on first run |

### What's mechanical vs what's LLM-judged (D16)

| Dimension | Producer |
|---|---|
| 1. Structural Integrity | Mechanical (from dream report) |
| 2. Memory Balance | Mechanical counts; LLM interprets ratio against maturity target |
| 3. Knowledge Depth | Muscle picks deterministic sample; **LLM judges** Deep / Adequate / Shallow / Empty-shell |
| 4. Connection Density | 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.