Re0 Memo logo

Re0 Memo

CommunityPopular
LilMGenius
re0-memo

Turn a finished, failed, or disappointing work cycle into portable lessons, anti-patterns, quality gates, and next-cycle vocabulary. Use after a build, QA pass, demo, user complaint, or abandoned attempt when the useful output is what the next pass must learn rather than the code itself.

Overview

PublisherLilMGenius
Repositorypaperthin
Skill namere0-memo
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 Memo 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-memo
  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-memo .claude/skills/re0-memo
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Re0 Memo 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 Memo 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 Memo 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.

Turn a completed cycle into lessons the next cycle can actually use.

Goal

re0-memo extracts durable learning from a work cycle without defending the artifact that produced it. A cycle can run, pass tests, and still be the wrong thing. The output is not a changelog or therapy note: it is a local, evidence-backed record of what worked, what misled the build, and what gate the next pass must clear.

Use re0-memo when the artifact is done, failed, disappointing, or ambiguous enough that the next agent needs the cycle's lessons more than its momentum.

Workflow

  1. Read the original objective, final artifact, QA evidence, user complaints, and any local planning notes.
  2. Separate working assets from misleading progress: contracts, schemas, tests, services, vocabulary, and examples that earned reuse vs. UI, panels, scaffolds, or abstractions that only looked productive.
  3. Generalize before recording: when feedback arrives as many specific complaints, cluster them upward into the shared pattern before writing anything down — many specifics against many items usually share one root gap. Record the root and its pattern family as the lesson; keep the specifics under it as evidence, not as lessons themselves.
  4. Name each failure as an anti-pattern, not a mood.
  5. Convert repeated or high-impact failures into quality gates for the next pass.
  6. Convert vague user direction into architecture vocabulary a fresh agent can use.
  7. Write or refresh local docs for the cycle: a re0-memo for lessons and a plan for next-cycle contracts, gates, and vocabulary.
  8. Verify that a from-scratch agent could avoid the same failure from those docs alone.

Rules

  • Do not defend the artifact. If it missed the product, say what missed.
  • Do not write a changelog. File lists and effort summaries are not lessons.
  • Do not transcribe a specific complaint as a lesson. A lesson that only fixes the reported case keeps the system human-in-the-loop: the same class of failure returns anywhere a person didn't point. Escalate every specific to the pattern that catches its whole class — the reporter's exact words are evidence, not the gate.
  • Preserve negative corpus. Failed paths are training data.
  • Prefer hard gates over advice.
  • Cite evidence from the cycle: objective, file facts, QA output, screenshots, transcripts, diffs, or user feedback.
  • If the next agent cannot act on it, it is not a lesson yet.
  • Keep provenance local; shipped artifacts should not narrate their scars.

Verification

Before finishing:

  1. Every lesson traces to observed cycle evidence.
  2. Every anti-pattern names a concrete failure mode and the gate that catches it.
  3. No lesson is a single reported case in disguise: each covers a class, with the specifics that raised it sitting under it as evidence.
  4. A fresh agent can tell what to preserve, what to discard, and what to test first without reading the whole old session.

Frequently asked questions

What does the Re0 Memo AI skill do?

Turn a finished, failed, or disappointing work cycle into portable lessons, anti-patterns, quality gates, and next-cycle vocabulary. Use after a build, QA pass, demo, user complaint, or abandoned attempt when the useful output is what the next pass must learn rather than the code itself.

Why use Re0 Memo on TypingMind?

Because you install it once and use it with any model. Re0 Memo 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 Memo in TypingMind?

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

Which AI models can use Re0 Memo?

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 Memo?

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

Is the Re0 Memo 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 👇