Claude Code Chief of Staff

How I turned Claude Code into a persistent AI chief of staff — the full architecture, from CLAUDE.md to scheduled background scripts.

Built with Claude Code by @loganinthefuture

Install it as a plugin (new)

The architecture below is now installable in one command. In Claude Code:

/plugin marketplace add loganhc-09/claude-chief-of-staff
/plugin install chief-of-staff@chief-of-staff

Restart Claude Code, then run /chief-of-staff:setup — it interviews you and scaffolds your own persistent memory + daily-brief system. No coding required. The plugin lives in plugins/chief-of-staff/; everything it creates is plain Markdown on your machine.

The Three Layers

The system has three layers. Most people are already at Layers 1 and 2. The unlock is Layer 3.

Layer 1 — Knowledge (Vault): A markdown vault Claude Code reads and writes. Start with the examples/CLAUDE.md template. That's the smallest viable version of this entire system — works the moment you drop it in a folder you claude from.

Layer 2 — Active (MacBook + Claude Code): You + Claude Code in the moment. Live work, live decisions. Already running if you have Claude Code installed.

Layer 3 — Autonomous (always-on machine): A second machine running scheduled scripts that pull email, calendar, transcripts, and post to Discord. Mine is an old laptop on my desk. Could be a Raspberry Pi, a Mac mini, or a cheap cloud VM. See architecture.md and examples/scripts/ for what runs there.

Cost: I run on Claude Max ($200/month). Most of this works on Max 5x ($100/month) too — see getting-started.md for plan-tier breakdown and cost-reduction habits.

What This Is

Most people use Claude Code for one-shot tasks. Write this email. Fix this bug. Explain this code. That's useful — but it's like hiring a brilliant contractor and only giving them 15-minute jobs.

This repo is the architecture for something different: a persistent operational layer that remembers your context, runs in the background, and compounds over time. Morning briefings generated before you wake up. Meeting transcripts processed into facts and follow-ups overnight. Commitments tracked and surfaced when they're overdue.

It's the difference between "I use AI sometimes" and "AI is part of how I operate."

Architecture Overview

DATA SOURCES
  Calendar · Email · Meeting Transcripts · Analytics · Notes
  Discord conversations · YouTube channels · Newsletters · RSS feeds
                          ↓
SCHEDULING LAYER
  launchd/cron jobs · Session hooks · Reminder scripts
  Discord bot (always-on) · Content scout (daily)
                          ↓
PROCESSING
  Transcript extraction · Email triage · Briefing generation
  Follow-up tracking · Content pipeline · Data analysis
  Reading curation · Signal extraction · Cross-source clustering
  Discord fact mining · Approval gate routing
                          ↓
KNOWLEDGE STORE
  SQLite memory (facts, knowledge profile, milestones)
  Semantic search (QMD) · Follow-up queue · Decision log
  Meeting archive · Position documents · Network map
  Reading preferences · Source trust scores · POV pillars
                          ↓
OUTPUTS
  Morning briefings · Draft messages · Reminders
  Content drafts · Intelligence briefs · Approval queues
  Discord channels · Reading recommendations

See architecture.md for the full system diagram, data flows, memory layout, and scheduling config.

The Handoff Model

The key design decision: not everything needs human involvement at every step. Three modes of work, explicit about where AI hands off to you.

Automated: Process transcripts, sync data, generate briefings, flag overdue items. AI owns it.

Approval-gated: Draft replies for you to review. Triage inbox for you to prioritize. Surface follow-ups for you to act on. AI preps, you decide.

Human only: Send messages, publish content, make judgment calls, strategic decisions. Never automated.

Most AI setups try to do everything or nothing. This one is explicit about where the handoff happens.

What's In Here

Core Architecture

File	What It Is
architecture.md	Full system diagram, data flows, memory layout, scheduling
getting-started.md	Progressive build guide — Day 1 to Month 3+
examples/CLAUDE.md	Template for your own chief of staff config
examples/scripts/briefing.py	Morning briefing generator
examples/scripts/meeting_processor.py	Transcript → facts + follow-ups pipeline

Deep Dives (new)

File	What It Is
memory-system.md	SQLite memory database, extraction protocol, effort tracking, semantic search
learning-loops.md	Reading system, content scout, discussion queue — feedback-driven learning
discord-system.md	Discord bot, approval gates, message memory, fact extraction

Working Components

This repo is the architecture. The code for two of the pieces lives in standalone repos you can fork independently:

Repo	What It Is	Where It Fits
task-tinder	Swipe-based task triage with metacognition capture	The Tier 2 interface. You decide, the system learns your methods.
reading-scout	Personal reading agent that learns from your conversations	The learning-loops layer. More here.

Daily Surface

Most of the system runs in the background, but day-to-day I open a local web dashboard at localhost:8091 (loopback only — bound to 127.0.0.1, not exposed to your LAN). It's a small Flask server that pulls from the SQLite memory and queue tables and renders three panels: Task Tinder (the swipe UI for triage), Intel (overnight signals + reading), and a day-prep panel.

The full dashboard is custom to my workflow and not in this repo (yet). The Task Tinder swipe UI is open source at task-tinder — fork that, point it at your own task store, and you have the most-used surface.

Getting Started

You don't build this in a weekend. You build it one layer at a time.

Day 0: Pick your plan + skim cost-reduction habits (details) — 5 minutes
Day 1: Create your CLAUDE.md (10 minutes)
Week 1: Add memory files for persistence across sessions
Week 2: Morning briefing routine (manual first, then scripted)
Month 1: Scheduled scripts + SQLite memory database
Month 2: Discord bot for mobile access + approval gates
Month 2: Learning loops — reading system + content scout
Month 3+: The full stack — everything connected, everything learning

See getting-started.md for the full walkthrough.

Design Principles

Clarity beats code. The bottleneck is never technical — it's knowing what you want.
Progressive complexity. Each layer works on its own before you add the next one.
Close loops, don't open them. Every automation should end with a verifiable outcome.
Human-in-the-loop by design. Prep is automated. Decisions are not.
Memory is architecture. If the AI doesn't remember yesterday, it can't help with tomorrow.
Learning compounds. Systems that get smarter from your behavior beat static configurations.
Filter aggressively. The value is in what you don't see — most content doesn't clear the bar.

About

Made by Logan Currie with Claude Code.

Part of my series on building personal AI operating systems — Captain's Log on Substack.

License

MIT — use it, adapt it, build on it.