muratcankoylan__agent-skills-for-context-engineering/examples/digital-brain-skill

Digital Brain

A personal operating system for founders, creators, and builders. Part of the Agent Skills for Context Engineering collection.

Overview

Digital Brain is a structured knowledge management system designed for AI-assisted personal productivity. It provides a complete folder-based architecture for managing:

• Personal Brand - Voice, positioning, values
• Content Creation - Ideas, drafts, publishing pipeline
• Knowledge Base - Bookmarks, research, learning
• Network - Contacts, relationships, introductions
• Operations - Goals, tasks, meetings, metrics

The system follows context engineering principles: progressive disclosure, append-only data, and module separation to optimize for AI agent interactions.

Architecture

digital-brain/
├── SKILL.md                 # Main skill definition (Claude Code compatible)
├── SKILLS-MAPPING.md        # How context engineering skills apply
│
├── identity/                # Personal brand & voice
│   ├── IDENTITY.md          # Module instructions
│   ├── voice.md             # Tone, style, patterns
│   ├── brand.md             # Positioning, audience
│   ├── values.yaml          # Core principles
│   ├── bio-variants.md      # Platform bios
│   └── prompts/             # Generation templates
│
├── content/                 # Content creation hub
│   ├── CONTENT.md           # Module instructions
│   ├── ideas.jsonl          # Content ideas (append-only)
│   ├── posts.jsonl          # Published content log
│   ├── calendar.md          # Content schedule
│   ├── engagement.jsonl     # Saved inspiration
│   ├── drafts/              # Work in progress
│   └── templates/           # Thread, newsletter, post templates
│
├── knowledge/               # Personal knowledge base
│   ├── KNOWLEDGE.md         # Module instructions
│   ├── bookmarks.jsonl      # Saved resources
│   ├── learning.yaml        # Skills & goals
│   ├── competitors.md       # Market landscape
│   ├── research/            # Deep-dive notes
│   └── notes/               # Quick captures
│
├── network/                 # Relationship management
│   ├── NETWORK.md           # Module instructions
│   ├── contacts.jsonl       # People database
│   ├── interactions.jsonl   # Meeting log
│   ├── circles.yaml         # Relationship tiers
│   └── intros.md            # Introduction tracker
│
├── operations/              # Productivity system
│   ├── OPERATIONS.md        # Module instructions
│   ├── todos.md             # Task list (P0-P3)
│   ├── goals.yaml           # OKRs
│   ├── meetings.jsonl       # Meeting notes
│   ├── metrics.jsonl        # Key metrics
│   └── reviews/             # Weekly reviews
│
├── agents/                  # Automation
│   ├── AGENTS.md            # Script documentation
│   └── scripts/
│       ├── weekly_review.py
│       ├── content_ideas.py
│       ├── stale_contacts.py
│       └── idea_to_draft.py
│
├── references/              # Detailed documentation
│   └── file-formats.md
│
└── examples/                # Usage workflows
    ├── content-workflow.md
    └── meeting-prep.md

Skills Integration

This example demonstrates these context engineering skills:

Skill	Application
context-fundamentals	Progressive disclosure, attention budget
memory-systems	JSONL append-only logs, structured recall
tool-design	Self-contained automation scripts
context-optimization	Module separation, just-in-time loading

See SKILLS-MAPPING.md for detailed mapping of how each skill informs the design.

Installation

As a Claude Code Skill

# User-wide installation
git clone https://github.com/muratcankoylan/digital-brain-skill.git \
  ~/.claude/skills/digital-brain

# Or project-specific
git clone https://github.com/muratcankoylan/digital-brain-skill.git \
  .claude/skills/digital-brain

As a Standalone Template

git clone https://github.com/muratcankoylan/digital-brain-skill.git ~/digital-brain
cd ~/digital-brain

Quick Start

1. Define your voice - Fill out identity/voice.md with your tone and style
2. Set your positioning - Complete identity/brand.md with audience and pillars
3. Add contacts - Populate network/contacts.jsonl with key relationships
4. Set goals - Define OKRs in operations/goals.yaml
5. Start creating - Ask AI to "write a post" and watch it use your voice

File Format Conventions

Format	Use Case	Why
.jsonl	Append-only logs	Agent-friendly, preserves history
.yaml	Structured config	Human-readable hierarchies
.md	Narrative content	Editable, rich formatting
.xml	Complex prompts	Clear structure for agents

Usage Examples

Content Creation

User: "Help me write a X thread about AI agents"

Agent Process:
1. Reads identity/voice.md for tone patterns
2. Checks identity/brand.md - confirms "ai_agents" is a pillar
3. References content/posts.jsonl for successful formats
4. Drafts thread matching voice attributes

Meeting Preparation

User: "Prepare me for my call with Sarah"

Agent Process:
1. Searches network/contacts.jsonl for Sarah
2. Gets history from network/interactions.jsonl
3. Checks operations/todos.md for pending items
4. Generates pre-meeting brief

Weekly Review

User: "Run my weekly review"

Agent Process:
1. Executes agents/scripts/weekly_review.py
2. Compiles metrics from operations/metrics.jsonl
3. Runs agents/scripts/stale_contacts.py
4. Presents summary with action items

Automation Scripts

Script	Purpose	Run Frequency
weekly_review.py	Generate review from data	Weekly
content_ideas.py	Suggest content from knowledge	On-demand
stale_contacts.py	Find neglected relationships	Weekly
idea_to_draft.py	Expand idea to draft scaffold	On-demand

# Run directly
python agents/scripts/weekly_review.py

# Or with arguments
python agents/scripts/content_ideas.py --pillar ai_agents --count 5

Design Principles

1. Progressive Disclosure - Load only what's needed for the current task
2. Append-Only Data - Never delete, preserve history for pattern analysis
3. Module Separation - Each domain is independent, no cross-contamination
4. Voice First - Always read voice.md before any content generation
5. Platform Agnostic - Works with Claude Code, Cursor, any AI assistant

Contributing

This is part of the Agent Skills for Context Engineering collection.

Contributions welcome:

• New content templates
• Additional automation scripts
• Module enhancements
• Documentation improvements

License

MIT - Use freely, attribution appreciated.

---

Author: Muratcan Koylan Version: 1.0.0 Last Updated: 2025-12-29