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How to Automate Business Operations with a Centralized AI 'Second Brain' in GitHub

Organize reusable business context, instructions, and small automations in a private GitHub repository so different AI tools can work from the same governed source without mixing client data, secrets, or unrelated tasks.

How to Automate Business Operations with a Centralized AI 'Second Brain' in GitHub

Source episode · 42:00

How I AI: Hamel Husain's Guide to Debugging AI Products & Writing Evals

with Claire Vo

Hamel shows separate Claude projects for proposals, course operations, and writing, a Gemini workflow that turns videos and slides into annotated notes, and the private GitHub monorepo that gives multiple coding agents shared context.

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Before you start

What you need

  • A private repository with access controls and secret scanning
  • An inventory of recurring business tasks and their approved source material
  • Clear ownership, confidentiality, retention, and licensing rules
  • Task-specific instructions, examples, and output locations
  • Claude Code, Cursor, or another approved agent with least-privileged repository access

What you’ll make

A versioned business-operations repository where each task has scoped context, instructions, examples, and outputs that a permitted AI agent can use without searching the entire business history.

Tools used

Step by step

The workflow

Follow the sequence once, then adapt the prompts, checks, and handoffs to your own setup.

4 steps

Step01

Create Dedicated AI Projects for Business Functions

List the recurring functions that benefit from dedicated context, such as proposals, course support, writing, or legal issue spotting. Keep legal advice and other high-risk decisions with qualified professionals.

Example prompt
You are an expert consultant specializing in [your expertise, e.g., AI product strategy]. Your task is to draft a consulting proposal based on a client call transcript.

CONTEXT:
- My skills include: [list your key skills, e.g., "partner of Palantir's, expert generative AI"].
- My style guide: [list style rules, e.g., "get to the point, writing short sentences," "avoid jargon"].
- Past proposal examples: [paste 1-2 successful proposal examples].

TASK:
Read the following client call transcript and draft a concise, compelling proposal that addresses the client's needs and highlights my relevant skills.

TRANSCRIPT:
[paste client call transcript]
Step02

Establish a GitHub Monorepo as a Central 'Second Brain'

Create a private repository with one scoped directory per function. Add a data inventory, owners, source dates, retention rules, output paths, ignore rules, secret scanning, and access controls before loading business material.

Step03

Provide AI Access and Contextual Rules

Give the agent a short repository guide that maps tasks to allowed source and output paths. Add task-specific instructions and strong examples, and require the agent to cite inputs and stop when current context is missing.

Example prompt
For task [task], read only [allowed paths]. Use [canonical sources] and write a draft to [output path]. Cite source files and dates. Do not access other client or business directories, invent missing facts, expose secrets, or overwrite a canonical document.

This monorepo approach prevents vendor lock-in, as you can point any new AI tool to the same centralized source of context, maintaining consistency across your workflows.

Step04

Automate Content Repurposing with Multimodal Models

For a permitted video, store the source URL, transcript, slides, and usage rights. Generate an annotated draft with timestamps, then compare sampled claims and images with the source before any public use.

Example prompt
Turn this authorized video package into an annotated draft. For each section, include the source timestamp, slide or image reference, concise explanation, and any uncertainty. Do not add claims absent from the transcript or imply endorsement. Write a draft only to [path].

What good looks like

  • Client, legal, financial, and credential data stay in explicitly permitted locations with the right access and retention.
  • Each automation cites its inputs and writes drafts to a predictable path without overwriting canonical records.
  • The same task can run with another supported agent because context and instructions live in the repository.
  • Generated proposals and public content preserve source facts, rights, and accountable editorial judgment.

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After the steps

Runbook notes

How to recover when the loop fails and where human judgment helps.

Recover

If it goes sideways

A broad repository lets one client or business function see another one's confidential context
Separate high-risk domains into access-controlled repositories or directories and constrain each agent to the task-specific paths it needs.
API keys, contracts, customer data, or private transcripts enter Git history or model context
Use secret scanning and ignore rules, minimize stored data, remove sensitive files before commits, and rotate any exposed credential.
The agent uses an old proposal, policy, price, or course detail as current truth
Record source dates and owners, mark superseded material, and require current canonical references for time-sensitive output.
Video repurposing invents context or uses material without permission
Use authorized media, keep timestamps and attribution, compare claims with the transcript and slides, and route public copy through editorial review.

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