Digital Brain

Digital Brain

A structured personal operating system for managing digital presence, knowledge, relationships, and goals with AI assistance. Designed for founders building in public, content creators growing their audience, and tech-savvy professionals seeking AI-assisted personal management.

Important: This skill uses progressive disclosure. Module-specific instructions are in each subdirectory's .md file. Only load what's needed for the current task.

When to Activate

Activate this skill when the user:

• Requests content creation (posts, threads, newsletters) - load identity/voice.md first
• Asks for help with personal brand or positioning
• Needs to look up or manage contacts/relationships
• Wants to capture or develop content ideas
• Requests meeting preparation or follow-up
• Asks for weekly reviews or goal tracking
• Needs to save or retrieve bookmarked resources
• Wants to organize research or learning materials

Trigger phrases: "write a post", "my voice", "content ideas", "who is [name]", "prepare for meeting", "weekly review", "save this", "my goals"

Core Concepts

Progressive Disclosure Architecture

The Digital Brain follows a three-level loading pattern:

File Format Strategy

Formats chosen for optimal agent parsing:

• JSONL (.jsonl): Append-only logs - ideas, posts, contacts, interactions
• YAML (.yaml): Structured configs - goals, values, circles
• Markdown (.md): Narrative content - voice, brand, calendar, todos
• XML (.xml): Complex prompts - content generation templates

Append-Only Data Integrity

JSONL files are append-only. Never delete entries:

• Mark as "status": "archived" instead of deleting
• Preserves history for pattern analysis
• Enables "what worked" retrospectives

Detailed Topics

Module Overview

digital-brain/
├── identity/     → Voice, brand, values (READ FIRST for content)
├── content/      → Ideas, drafts, posts, calendar
├── knowledge/    → Bookmarks, research, learning
├── network/      → Contacts, interactions, intros
├── operations/   → Todos, goals, meetings, metrics
└── agents/       → Automation scripts

Identity Module (Critical for Content)

Always read identity/voice.md before generating any content.

Contains:

• voice.md - Tone, style, vocabulary, patterns
• brand.md - Positioning, audience, content pillars
• values.yaml - Core beliefs and principles
• bio-variants.md - Platform-specific bios
• prompts/ - Reusable generation templates

Content Module

Pipeline: ideas.jsonl → drafts/ → posts.jsonl

• Capture ideas immediately to ideas.jsonl
• Develop in drafts/ using templates/
• Log published content to posts.jsonl with metrics
• Plan in calendar.md

Network Module

Personal CRM with relationship tiers:

• inner - Weekly touchpoints
• active - Bi-weekly touchpoints
• network - Monthly touchpoints
• dormant - Quarterly reactivation checks

Operations Module

Productivity system with priority levels:

• P0: Do today, blocking
• P1: This week, important
• P2: This month, valuable
• P3: Backlog, nice to have

Practical Guidance

Content Creation Workflow

1. Read identity/voice.md (REQUIRED)
2. Check identity/brand.md for topic alignment
3. Reference content/posts.jsonl for successful patterns
4. Use content/templates/ as starting structure
5. Draft matching voice attributes
6. Log to posts.jsonl after publishing

Pre-Meeting Preparation

1. Look up contact: network/contacts.jsonl
2. Get history: network/interactions.jsonl
3. Check pending: operations/todos.md
4. Generate brief with context

Weekly Review Process

1. Run: python agents/scripts/weekly_review.py
2. Review metrics in operations/metrics.jsonl
3. Check stale contacts: agents/scripts/stale_contacts.py
4. Update goals progress in operations/goals.yaml
5. Plan next week in content/calendar.md

Examples

Example: Writing an X Post

Input: "Help me write a post about AI agents"

Process:

1. Read identity/voice.md → Extract voice attributes
2. Check identity/brand.md → Confirm "ai_agents" is a content pillar
3. Reference content/posts.jsonl → Find similar successful posts
4. Draft post matching voice patterns
5. Suggest adding to content/ideas.jsonl if not publishing immediately

Output: Post draft in user's authentic voice with platform-appropriate format.

Example: Contact Lookup

Input: "Prepare me for my call with Sarah Chen"

Process:

1. Search network/contacts.jsonl for "Sarah Chen"
2. Get recent entries from network/interactions.jsonl
3. Check operations/todos.md for pending items with Sarah
4. Compile brief: role, context, last discussed, follow-ups

Output: Pre-meeting brief with relationship context.

Guidelines

1. Voice First: Always read identity/voice.md before any content generation
2. Append Only: Never delete from JSONL files - archive instead
3. Update Timestamps: Set updated field when modifying tracked data
4. Cross-Reference: Knowledge informs content, network informs operations
5. Log Interactions: Always log meetings/calls to interactions.jsonl
6. Preserve History: Past content in posts.jsonl informs future performance

Integration

This skill integrates context engineering principles:

• context-fundamentals - Progressive disclosure, attention budget management
• memory-systems - JSONL for persistent memory, structured recall
• tool-design - Scripts in agents/scripts/ follow tool design principles
• context-optimization - Module separation prevents context bloat

References

Internal references:

• Identity Module - Voice and brand details
• Content Module - Content pipeline docs
• Network Module - CRM documentation
• Operations Module - Productivity system
• Agent Scripts - Automation documentation

External resources:

• Agent Skills for Context Engineering
• Anthropic Context Engineering Guide

Skill Metadata

Created: 2024-12-29 Last Updated: 2024-12-29 Author: Murat Can Koylan Version: 1.0.0

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