القائمة
Comprehensive cognitive self-assessment — honest evaluation of architecture health, growth, and optimization opportunities
يُحدَّد Prompt الذي يراجع المصدر أولًا بشكل افتراضي. يمكنك التبديل إلى أمر مباشر أو تنزيل نسخة محلية.
اقرأ SKILL.md وأي ملفات مرافقة يعرضها SkillsMP قبل أن تقرر التثبيت.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
يتجاوز الأمر المباشر Prompt المخصّص للمراجعة. افحص المصدر قبل تشغيله.
npx skills add https://github.com/fabioc-aloha/ChessCoach --skill self-actualizationيبقى الأمر في سطر واحد. مرّر أفقيًا لمراجعته كاملًا قبل النسخ.
تفضّل نسخة محلية؟ نزّل الملفات المتاحة حاليًا لدى SkillsMP.
End-to-end academic paper drafting for CHI, HBR, journals, and conferences with venue-specific templates, drafting workflows, and revision strategies.
Detect, create, and manage the AI-Memory fleet communication channel. Fires on bootstrap, session start (announcements), and feedback writes.
Generate on-brand Alex — ACT Edition SVG banners for documents (READMEs, plans, notes, release artifacts)
استنادا إلى تصنيف SOC المهني
عرض SKILL.md
| type | skill |
| lifecycle | stable |
| inheritance | inheritable |
| name | self-actualization |
| description | Comprehensive cognitive self-assessment — honest evaluation of architecture health, growth, and optimization opportunities |
| tier | standard |
| applyTo | **/*actuali*,**/*assessment*,**/*self-assess*,**/*health* |
| disable-model-invocation | true |
| currency | 2026-04-22T00:00:00.000Z |
The full physical exam. Not "am I sick?" but "how fit am I, and where should I train next?"
| 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) |
| 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 |
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:
| 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.
| 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% |
| 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:
| 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 |
| 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.
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) |
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).
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.
.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 |
| 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 |
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).recency.daysSinceLastSelfActualization resets.