In two years you'll have an AI that knows you better than your calendar. It'll know your tone, your methods, your clients, your history. Simple question: who will own it?
Four technical bricks, all built on open standards. No black magic, no proprietary code. Everything readable in a text editor.
Everything your AI knows about you lives in a plain .md file. One line per fact. You can read it, edit it, version it in git.
- Tone: consultative - ICP: SaaS B2B 50-200 emp. - Templates: 12 emails
Each magik is a Skill folder: SKILL.md + scripts + assets. Open convention. Forkable, versionable, runnable anywhere.
/skills/
prospect-brief/
SKILL.md
research.pyGmail, Notion, Slack, Drive… connected via Model Context Protocol. An open standard backed by Anthropic and several SaaS tools.
"mcpServers": {
"gmail": {...},
"notion": {...}
}Today: locked to Claude API. By design open : works with any LLM tomorrow, including local ones (Ollama, llama.cpp). Swap engines without rebuilding the lab.
provider: anthropic model: claude-sonnet-4-6 # soon: ollama, local
Structured data lives in CSV or JSON. Export at any time. Re-import in Excel, Airtable, anywhere that reads CSV.
leads.csv (247 rows) deals.csv ( 18 rows) runs.json (1.2 MB)
It's all text. All versionable in git. All zippable and downloadable.
One button. One file. Your entire personal AI system: config, magik, blocks, memory, data. Downloadable to your laptop. Re-importable elsewhere. Open format, plain readable text.
sales-ops-app/
├── memory.md
├── mcp.json
├── llm.config
├── skills/
│ ├── prospect-brief/
│ │ ├── SKILL.md
│ │ └── research.py
│ └── weekly-pipeline/
│ └── SKILL.md
└── data/
├── leads.csv (247 rows)
├── deals.csv ( 18 rows)
└── runs.json (1.2 MB)Every run, your AI learns your business a bit better. After six months, it finishes your sentences.
You're not alone figuring out how to use AI in your job. The community shows what others have already built. You copy, adapt, share back.





When one sales rep publishes their prospecting lab, twelve other reps copy and improve it. That's what learning AI as a team looks like: building domain knowledge together, without re-coding the same thing a hundred times.
Open standards. Portable data. Domain community. Start in five minutes.
Build a labNo credit card required.