LLM-GTD

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SUMMARY

GTD (Getting Things Done) agent harness for Claude Code, Cursor & Codex — one source of truth, one markdown trusted-system, three platform front-ends. AI auto-runs capture→clarify and mechanical organize; you keep engage & review.

README.md

LLM-GTD

中文 | English

A portable GTD harness for LLM agents.

Not a todo app. Not another productivity prompt. LLM-GTD is a trusted external system that lets
Claude Code, Cursor, and Codex help you capture, clarify, organize, choose, and review your work
without turning your life into a pile of half-processed tasks.

The installable Codex plugin is named llm-gtd.
The legacy skill package remains gtd-harness for Cursor and manual installs.

See it in one example

You don't hand it a task — you hand it an intention:

You: I want to get my daughter into table tennis
LLM-GTD: Makes it a project; first action → "talk with her for 10 min — does she want to try it, current level, sessions/week…". It does not enroll, schedule, or contact a coach.

A while later you report back:

You: Talked with her — she's really into it, two sessions/week, ~1h each
LLM-GTD: Checks the talk off; advances the project to "shortlist 2-3 classes — slots, price, distance, trial options"; logs the facts as support material; flags your "~1h" as needs-confirmation.

A project without a current next action is a stalled promise. LLM-GTD keeps the promise live across
days
— you supply judgment and report reality; it does the bookkeeping.
full walkthrough

The Short Version

Most AI productivity workflows fail for the same reason most human GTD systems fail: the inbox fills,
the next action is vague, the project has no current move, and the weekly review gets skipped.

LLM-GTD gives the agent a harness:

  • plain Markdown state in memory/gtd/
  • a full GTD workflow, not just inbox triage
  • one /gtd router plus six workflow commands: init, capture, clarify, organize, engage, review
  • one shared trusted system across Claude Code, Cursor, and Codex
  • automatic Google Calendar writes for complete schedule items, with fail-closed reporting

The model does what it is good at: drafting next actions, cleaning structure, spotting stale items,
and preparing reviews.

You keep what should stay human: commitment, priority, reflection, and final choice.

Why This Exists

Public agent skills around GTD tend to fall into two buckets:

  1. Single workflow skills such as inbox processing or weekly review.
  2. Broad Life OS / second-brain systems that include GTD as one part of a larger personal OS.

Those are useful, but they often miss the hardest part: a durable trusted system that an agent can
maintain every day without scattering state across tools, chats, and half-written notes.

LLM-GTD is narrower and deeper. It turns GTD into a reusable agent harness:

LLM = judgment, language, drafting
Harness = state, workflow, cadence, adapter, safety boundary

David Allen gave us the operating system for commitments. LLM-GTD makes that operating system
agent-native.

What It Does

You want to... Command What happens
set up the trusted system gtd-init creates the eight GTD lists and checks wiring; legacy installs also refresh Codex slash prompts
capture a thought or task gtd-capture writes it to inbox first, then auto-clarifies small inputs
process inbox items gtd-clarify turns vague "stuff" into next actions, projects, waiting-for, reference, or someday
clean the system gtd-organize fixes mechanical drift: orphan actions, stalled projects, bad contexts, duplicates
decide what to do now gtd-engage suggests 3-5 context-fit next actions based on context, time, energy, and priority
run the weekly review gtd-review generates a read-only prep package, cleans mechanical drift, then reviews inbox/calendar/waiting/projects/horizons

The important design choice: capture, clarify, and mechanical organize can be mostly automated;
engage and review stay human-led.

The Trusted State

LLM-GTD stores its operating state in eight plain Markdown files:

File GTD list Purpose
memory/gtd/inbox.md Inbox zero-friction capture sink
memory/gtd/next-actions.md Next Actions concrete single-step actions grouped by context
memory/gtd/projects.md Projects outcomes that require more than one action, each with a current next action
memory/gtd/waiting-for.md Waiting For delegated or pending items, with person and agreement
memory/gtd/someday-maybe.md Someday/Maybe things you do not commit to now but do not want to lose
memory/gtd/calendar.md Calendar fallback hard landscape only, used only when the real calendar is unavailable
memory/gtd/reference.md Reference non-actionable support material
memory/gtd/horizons.md Horizons purpose, vision, goals, areas, projects, and runway

No database. No hidden app state. No vendor lock-in. A file is a file.

How It Works

LLM-GTD has four layers:

LLM
  does judgment, interpretation, next-action drafting

Harness
  State      memory/gtd/*.md
  Logic      src/skill/SKILL.md + sub-skills
  Adapter    Claude Code commands, Cursor skill rules, Codex prompts
  Cadence    weekly review workflow and optional reminders

The same skill package and the same memory/gtd/ state can be used from multiple agent surfaces:

Platform Front end State
Claude Code plugin llm-gtd (skill + /gtd + /gtd-*); or .claude/commands/gtd*.md (manual) same memory/gtd/
Cursor .cursor/skills/gtd-harness/ plus keyword rules same memory/gtd/
Codex Codex plugin llm-gtd; legacy ~/.codex/prompts/gtd*.md also works same memory/gtd/

Why It Is Different

It is a complete GTD loop, not an inbox prompt.
Capture, clarify, organize, engage, and review are all first-class.

It treats GTD as a harness, not a chatbot personality.
The agent can be replaced. The state and workflow remain.

It keeps knowledge and action separate.
Actions go to GTD. Ideas and notes should go to your note system, such as a Zettelkasten.

It uses AI where AI actually helps.
Drafting a concrete next action, finding stale projects, and cleaning list structure are good AI jobs.
Choosing what you value and what you commit to are not.

It fails closed around calendar writes.
If Google Calendar is connected, it is the only hard landscape. Complete schedule items are written
to Google Calendar automatically; missing date/time/title fields are clarified first. If the tool
fails, LLM-GTD does not pretend anything happened.

Install

Install as a Claude Code plugin

This repo is also a Claude Code plugin marketplace. From Claude Code:

/plugin marketplace add mikonos/LLM-GTD
/plugin install llm-gtd@llm-gtd

The bundled gtd-harness skill auto-activates on GTD phrasing, and the /gtd router plus /gtd-* commands
(/gtd, /gtd-init, /gtd-capture, /gtd-clarify, /gtd-organize, /gtd-engage, /gtd-review) are added.
State is written to your current workspace's memory/gtd/ — never bundled with the plugin
(${CLAUDE_PLUGIN_ROOT} holds the read-only skill; your lists live in your project). Run /gtd-init
(or just ask) in the workspace where you want your GTD lists to live.

Install as a Codex plugin

LLM-GTD now includes a repo-scoped Codex plugin package:

.agents/plugins/marketplace.json
plugins/llm-gtd/

Add this repository as a Codex plugin marketplace, then install llm-gtd from the Codex
plugin directory:

codex plugin marketplace add https://github.com/mikonos/LLM-GTD.git
codex plugin add llm-gtd@llm-gtd

After installing the plugin, start Codex in the workspace where you want your GTD state to live and
ask it to use LLM-GTD:

Set up my GTD trusted system
Capture and clarify this task: renew passport before summer
Run my weekly GTD review

The plugin writes user state only under that workspace's memory/gtd/. It does not bundle any
personal GTD state, and it does not include Google Calendar as an app or MCP server. If your Codex
environment already has Google Calendar available, LLM-GTD can use it as the real hard landscape;
otherwise it falls back to memory/gtd/calendar.md.

Install with the legacy multi-surface installer

git clone <your-fork-url> LLM-GTD
cd LLM-GTD
./install.sh /path/to/your/vault

If you omit the vault path, the installer uses the current directory:

./install.sh

The installer copies:

  • src/skill/ to <vault>/.cursor/skills/gtd-harness/
  • Claude Code commands to <vault>/.claude/commands/
  • Codex prompts to ~/.codex/prompts/
  • the Codex orchestrator to <vault>/.codex/agents/
  • the initial GTD state to <vault>/memory/gtd/

It also prints two optional manual wiring steps:

  • merge snippets/cursor-skill-rules.json into your Cursor skill rules
  • merge snippets/AGENTS.routing.md into your workspace AGENTS.md

Requirements

  • Bash
  • Python 3 for status/dashboard helpers
  • Claude Code, Cursor, or Codex, depending on which surface you use
  • Optional: Google Calendar access if you want real calendar reads and automatic writes

Quick Start

Initialize:

./install.sh /path/to/your/vault

Then try one of these from your agent:

/gtd-capture Renew passport before the summer trip
/gtd-clarify
/gtd-engage
/gtd-review

Natural-language triggers are supported by the skill prompts, for example:

Help me clear my head.
Help me sort through these pending items.
I have 30 minutes right now. What should I do?
Run a weekly review.

You can also use /gtd as a general entry point. You do not have to pick a specific sub-command:

/gtd Schedule coffee with Jack tomorrow afternoon at the Starbucks near my home.

Check the system state:

bash .cursor/skills/gtd-harness/scripts/gtd_status.sh

Example Flow

You say:

/gtd-capture Ask Mei about the school form, renew passport, maybe learn piano, save the tax PDF

LLM-GTD first captures everything, then clarifies what can be safely inferred:

  • Ask Mei about the school form becomes a concrete next action or waiting-for item.
  • renew passport becomes a project if it needs multiple steps.
  • maybe learn piano goes to someday/maybe unless you commit to it.
  • save the tax PDF goes to reference unless it implies an action.

If the agent cannot safely infer your commitment, it asks instead of pretending.

→ Full five-step walkthrough (capture → clarify → engage → review): docs/demo.md.

Repository Layout

src/skill/            core gtd-harness skill package
plugins/llm-gtd/      Codex plugin package generated from src/skill/
.agents/plugins/      repo-scoped Codex marketplace
scripts/              repository maintenance scripts
src/claude-commands/  Claude Code slash commands
src/codex-prompts/    Codex slash prompts
src/codex-agents/     Codex orchestrator agent
snippets/             optional routing snippets for Cursor and AGENTS.md
docs/design.md        architecture and design notes
docs/demo.md          a day-with-LLM-GTD walkthrough (the five steps)
install.sh            installer
CHANGELOG.md          project changelog

Design Boundaries

  • The inbox is not the system. It is only the capture sink.
  • A next action must be physical and concrete. "Handle taxes" is not a next action. "Email CPA the W-2 PDF" is.
  • Projects must have a current next action. A project without a next action is a stalled promise.
  • Calendar is sacred. Only time-specific commitments belong there.
  • Weekly review is not optional. Without review, GTD decays into a task pile.
  • No hidden writes. Calendar writes and other high-consequence actions need confirmation.
  • Knowledge is not action. Notes, insights, and research belong in your knowledge system, not in next-actions.md.

Related Work

LLM-GTD was shaped by looking at existing public agent-skill patterns:

  • natea/ExoMind includes Life OS skills such as inbox processing, email inbox processing, and weekly review.
  • huytieu/COG-second-brain is a broader agentic second-brain system with capture and weekly check-in workflows.
  • openai/skills shows the current Codex skill packaging pattern.

LLM-GTD is deliberately smaller than a Life OS and more complete than a single inbox skill. It is the
GTD commitment loop, packaged as a portable harness.

Language

The README is in English for open-source discovery.

The skill prompts are currently written in Chinese because the original operating environment is Chinese.
The methodology is David Allen's GTD; the implementation language can be localized.

License

MIT. See LICENSE.

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