Re0 Plan logo

Re0 Plan

CommunityPopular
LilMGenius
re0-plan

Open a paperthin iteration's casebook before re0-loop's first turn — seeded with real content the moment the folder exists, never an empty directory. User-invoked, paperthin-only: assumes the full skill package installed. Run when opening a new build cycle in this repo.

Overview

PublisherLilMGenius
Repositorypaperthin
Skill namere0-plan
Stars
1.3K
Forks
118
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by LilMGenius on GitHub. Read the source before you install it.

Installation

Install the Re0 Plan AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/LilMGenius/paperthin/tree/main/skills/coil/re0-plan
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/LilMGenius/paperthin.git /tmp/paperthin
mkdir -p .claude/skills
cp -r /tmp/paperthin/skills/coil/re0-plan .claude/skills/re0-plan
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Re0 Plan in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Re0 Plan on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Re0 Plan is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Open .re0/iteration/<version>-<workname>/ and write into it in the same motion, before a single line of re0-loop's cycle runs.

Goal

A cycle without a written thesis, gate list, and step plan drifts to whatever framing comes first — and a folder opened with nothing in it tells a resumed session nothing at all. re0-plan closes both gaps at once: the folder is never empty from the moment it's created, regardless of which weight the cycle turns out to be. It assumes the full paperthin package installed and names its sibling skills directly. It pairs with re0-release, which retires the folder, and re0-memo, which extends whatever this skill seeded instead of restarting fresh. It is not a portable planning method — the shape it writes is this repo's own convention.

Workflow

  1. Classify the cycle's weight: a fix or hardening pass with no real design surface is lightweight; a cycle with a direction genuinely worth arguing through is full.
  2. Create .re0/iteration/<version>-<workname>/ and, in the same step, write its first file — the weight decides which:
    • lightweight → RETRO.local.md: one paragraph naming the task and why it's lightweight. re0-memo extends this same file at the end; it never starts a fresh one. Stop here.
    • full → DESIGN.local.md: thesis, scope, and quality gates — the same content re0-loop's FRAME turn expects, and enough on its own to survive a session break. When the change touches shippable surface, those gates always include an AGENTS.md/README reflection gate (see Rules). Confirm the read with readchk, size the tier with modelchk; reach for macrothink or another judgment skill only when the direction is genuinely contestable.
  3. (full only) Write WORKFLOW.local.md: the numbered steps this specific cycle runs through re0-loop's turn order — specific to this build, not a restatement of re0-loop's generic stages.
  4. (full only) Write EVIDENCE.local.md: the gates from DESIGN.local.md, listed as proof surface still to fill — the bar, not results.
  5. Keep reference material flat as REF-<topic>.local.md; promote to a refs/ subfolder only once it multiplies past a couple of files.
  6. Hand off to re0-loop. Do not invoke re0-work here — a from-scratch restart is re0-loop's own mid-cycle call, surfaced by nba when warranted, never a setup step.

Rules

  • The folder is never empty at creation. Every path, lightweight or full, writes real content in the same motion the folder is made, so a session break at any point still leaves something to resume from.
  • Not portable, by design. Names other paperthin skills directly and assumes the full package installed — the opposite of the self-contained default every other skill here follows. It exists only to pair with re0-release and re0-memo, not to travel alone.
  • No padding. Lightweight gets no DESIGN/WORKFLOW/EVIDENCE, and its RETRO.local.md seed stays one paragraph — a task note, not a WORKFLOW.local.md in disguise. A judgment skill or re0-work never gets forced in "just in case."
  • The folder's <version> is provisional. Apply the same minor/patch discriminator re0-release uses — kind not size: is the change, against the artifact's own prior spec, a fix (patch) or a new capability a user newly reaches for (minor)? — and keep it contingent on any unresolved fork the cycle opens. Never pre-commit a firm bump the cycle hasn't earned; a fork that could add a skill leaves the bump undecided until it resolves.
  • Docs stay aligned, like re0-release at ship time. When the cycle's change touches shippable surface, its quality gates always include an AGENTS.md/README reflection gate, assigning each reflection to its register per AGENTS.md's docs-role split: internal mechanism/rules/anatomy → AGENTS.md (LLM + human, injected as instructions), user-facing catalog/experience → README (human-only, never injected). Never dump agent-instruction mechanism into README, and never skip README for a user-facing change. This is the plan-time complement to re0-release's ship-time README step.
  • negatives-as-corpus — nothing this skill or the cycle it opens produces gets deleted; a cycle that goes wrong is retired by re0-release, not discarded.
  • Every EVIDENCE.local.md gate traces back to a line in DESIGN.local.md; don't invent proof surface DESIGN never asked for.

Verification

Before finishing:

  1. The folder has real content from the moment it exists — no empty directory, on either path.
  2. Full path: DESIGN.local.md states a thesis, scope, and gates; WORKFLOW.local.md's steps are specific to this cycle; every EVIDENCE.local.md gate traces to DESIGN.local.md.
  3. Lightweight path: RETRO.local.md is seeded with the task and reason, one paragraph.
  4. Reference material stayed flat unless it earned a refs/ subfolder.
  5. Report which path was taken and why.

Frequently asked questions

What does the Re0 Plan AI skill do?

Open a paperthin iteration's casebook before re0-loop's first turn — seeded with real content the moment the folder exists, never an empty directory. User-invoked, paperthin-only: assumes the full skill package installed. Run when opening a new build cycle in this repo.

Why use Re0 Plan on TypingMind?

Because you install it once and use it with any model. Re0 Plan is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Re0 Plan in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/LilMGenius/paperthin/tree/main/skills/coil/re0-plan. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Re0 Plan?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Re0 Plan?

As many as you like. As long as a model supports skills, you can use Re0 Plan with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Re0 Plan AI skill free?

Yes. It is published on GitHub by LilMGenius under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇