fabioc-aloha Global Knowledge Skill
Your past solved problems, accessible from any AI surface.
Architecture: AI-Memory
Global knowledge lives in a single shared file: AI-Memory/global-knowledge.md
This replaces the previous GitHub-based system (~/.alex/global-knowledge/, GK-/GI- files, index.json). The AI-Memory folder is accessible across all surfaces:
- M365 Copilot: Reads via OneDriveAndSharePoint capability
- VS Code: Reads via local OneDrive sync path. Falls back to
~/.alex/AI-Memory/when OneDrive is unavailable - Agent Builder agents: Reads via OneDriveAndSharePoint capability
AI-Memory Files
| File | Purpose |
|---|---|
profile.md |
User identity, preferences, expertise |
notes.md |
Session notes, reminders, observations |
learning-goals.md |
Active/completed goals with progress tracking |
global-knowledge.md |
Cross-project insights, patterns, and learnings |
Templates: platforms/m365-shared/onedrive-templates/AI-Memory/
Check Before You Create
Always search global-knowledge.md before creating new skills, instructions, or prompts. This prevents duplicate work and ensures institutional knowledge is reused.
Lookup Process
- Search
AI-Memory/global-knowledge.mdfor existing content - Also search local
.github/skills/(the answer may already be a skill) - Also search Copilot Memory (
/memories/) for VS Code-specific notes - Evaluate the result:
| Result | Action |
|---|---|
| Exact match | Adopt directly; don't recreate |
| Similar match | Adapt as starting point |
| No match | Create new, then add to global-knowledge.md |
This check is mandatory before skill creation (Phase 1 of skill-building procedure).
Knowledge Organization in global-knowledge.md
Insights are organized by category with a consistent format:
### [Topic Title]
- **Source**: [Project or context where learned]
- **Insight**: [The key learning]
- **Date**: [When captured]
Categories emerge organically: PowerShell Patterns, Azure Patterns, Frontend Patterns, AI Image Generation, Design Patterns, Workflow Patterns, etc.
What Goes in global-knowledge.md
| Content | Example |
|---|---|
| Cross-project patterns | "SWA EasyAuth handles login; domain gating via fetch" |
| Hard-won gotchas | "PowerShell -replace corrupts multi-byte content" |
| Architecture decisions | "Cosmos liveness probes cause container kill loops" |
| Integration patterns | "MS Graph MCP: call suggest_queries first" |
| Tool-specific learnings | "Flux 1.1 Pro max width 1440px" |
What Does NOT Go in global-knowledge.md
| Content | Where It Belongs |
|---|---|
| Personal preferences | AI-Memory/profile.md |
| Session reminders | AI-Memory/notes.md |
| Learning goals | AI-Memory/learning-goals.md |
| Project-specific config | Project's own .github/ or docs |
| Temporary workarounds | Project notes (will become stale) |
Memory System Differentiation
Alex uses three complementary memory systems:
1. AI-Memory (Cloud Storage): Cross-Platform Persistent Memory
Shared across all AI surfaces via cloud storage sync. Use for:
- User identity and structured preferences (user-profile.json)
- Narrative profile and expertise (profile.md)
- Session notes and observations (notes.md)
- Learning goals with progress tracking (learning-goals.md)
- Cross-project insights and patterns (global-knowledge.md)
2. Copilot Memory (/memories/): VS Code Workflow Memory
GitHub-cloud-synced notes for VS Code workflows. Use for:
- VS Code-specific workflow tips (terse, actionable rules)
- Tool-specific workarounds (npm, CLI, Mermaid palette, etc.)
- Cross-workspace tool access patterns (MS Graph MCP, etc.)
- Session-scoped working state (/memories/session/)
3. Project Memory (.github/): Local Architecture
Project-specific cognitive architecture. Use for:
- Skills, instructions, muscles (the trifecta), plus prompts
- Episodic memories from meditation sessions
- Connections between skills (via
applyTopatterns) - Project persona (.github/config/project-persona.json)
Decision Matrix
| Data Type | AI-Memory | /memories/ | .github/ |
|---|---|---|---|
| Name, role, identity | user-profile.json | ||
| Formality, detail level, humor | user-profile.json | ||
| Learning style, explanation style | user-profile.json | ||
| Technologies, expertise areas | user-profile.json | ||
| Writing style rules (em dash ban) | /memories/ | ||
| Tool workarounds (npm, CLI) | /memories/ | ||
| Mermaid palette, diagram prefs | /memories/ | ||
| Cross-workspace access (MS Graph) | /memories/ | ||
| Cross-project insight | global-knowledge.md | ||
| Learning goals with progress | learning-goals.md | ||
| Session notes | notes.md | ||
| Project persona (detected) | project-persona.json | ||
| Project architecture | .github/skills/ | ||
| Meditation output | .github/episodic/ |
Conflict Prevention Rules
AI-Memory/user-profile.json is the source of truth for user identity and preferences. Copilot Memory (/memories/) supplements with VS Code-specific workflow tips. They do not overlap.
| Rule | Rationale |
|---|---|
| Never store name, role, or identity in /memories/ | AI-Memory owns identity |
| Never store formality, detailLevel, explanationStyle in /memories/ | AI-Memory owns structured preferences |
| Never store technologies or expertise in /memories/ | AI-Memory owns the profile |
| VS Code workflow tips (em dash ban, palette, CLI workarounds) go in /memories/ only | These are terse, actionable, VS Code-specific |
| If a preference exists in BOTH systems, AI-Memory wins | Single source of truth |
Integration Workflow
User learns something
Is it about who they are? -> AI-Memory/user-profile.json
Is it a cross-project pattern? -> AI-Memory/global-knowledge.md
Is it a VS Code workflow tip? -> /memories/ (terse, 3-workspace test)
Is it project-specific? -> .github/skills/ or .github/episodic/
No duplication: Each piece of information lives in ONE system.
Saving Insights
From VS Code
During a session, when you learn something cross-project:
- Open
%OneDrive%/AI-Memory/global-knowledge.md(synced locally) - Add entry under the appropriate category
- Use the standard format: Topic, Source, Insight, Date
- Save the file (OneDrive syncs automatically)
Or say "consolidate" to trigger the cognitive protocols consolidation workflow, which prompts you to capture insights.
From M365 Copilot / Agent Builder
- Say "consolidate" or "save this insight"
- Alex generates the content update in a code block
- You save it to OneDrive > AI-Memory > global-knowledge.md
Promotion from Project to Global
When a project-specific learning proves cross-project:
- Identify the insight in .github/episodic/ or session notes
- Abstract it (remove project-specific details)
- Add to AI-Memory/global-knowledge.md under the right category
- Verify it reads well without project context
Promotion Signals
Strong Promotion Signals
| Pattern | Description |
|---|---|
| Cross-project applicability | Would 3+ other projects benefit? |
| Resolution pattern | Solved problem with concrete solution |
| Hard-won gotchas | Prevents repeat mistakes |
| Architecture with rationale | ADR-style decisions with "why" |
| Integration patterns | How systems connect |
Anti-Promotion Signals
| Signal | Why Skip |
|---|---|
| Project-specific config | Only works in one context |
| Temporary workarounds | Will be obsolete |
| Personal preferences | Goes in profile.md |
| Incomplete/draft content | Needs validation |
Migration from Legacy GK System
The previous system used ~/.alex/global-knowledge/ with GK-/GI- files and index.json. All valuable content has been migrated to AI-Memory/global-knowledge.md. The legacy system is deprecated:
| Legacy | Replacement |
|---|---|
~/.alex/global-knowledge/ |
OneDrive/AI-Memory/ |
patterns/GK-*.md |
Categories in global-knowledge.md |
insights/GI-*.md |
Entries in global-knowledge.md |
index.json |
Not needed (single file, searchable) |
Alex-Global-Knowledge GitHub repo |
OneDrive sync (automatic) |
skill-registry.json |
Master Alex .github/skills/ is canonical |
Triggers
- "save insight", "promote to global", "consolidate"
- "search knowledge", "have I solved this before?"
- "global knowledge", "cross-project"
$patterns = (Get-ChildItem patterns\GK-*.md).Count
$insights = (Get-ChildItem insights\GI-*.md).Count
Sync During Dream
Automated sync during dream/meditation cycles: check for uncommitted changes, pull latest, regenerate KNOWLEDGE-INDEX.md, report sync status.
Skill Pull-Sync (For Heirs)
Discover and pull new skills from GK via skills/skill-registry.json. Commands: /knowledge checkskills (discover), /knowledge pullskill <id> (pull on demand).
Quality Gates
✅ Knowledge sync reports 0 orphaned, 0 unindexed
✅ Heir contributions documented as insights (if any)
✅ Counts in copilot-instructions.md match disk
✅ git status clean (or changes committed)