Memory Discipline logo

Memory Discipline

CommunityPopular
rohitg00
memory-discipline

The session loop that makes agentmemory pay off, recall before starting work, save at decision points, learn from corrections. Use when starting a nontrivial task, after settling a decision or debugging a gotcha, or whenever deciding if something belongs in memory.

Overview

Publisherrohitg00
Repositoryagentmemory
Skill namememory-discipline
Stars
28.6K
Forks
2.5K
Bundled files
Instructions only
LicenseApache-2.0
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 rohitg00 on GitHub. Read the source before you install it.

Installation

Install the Memory Discipline 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.
  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/rohitg00/agentmemory.git /tmp/agentmemory
mkdir -p .claude/skills
cp -r /tmp/agentmemory/plugin/skills/memory-discipline .claude/skills/memory-discipline
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Memory Discipline 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 Memory Discipline 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 Memory Discipline 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.

Memory only pays off when reads happen before the work and writes happen at decision points. This loop is the skill; every tool call in it is mechanical.

Quick start

json
memory_smart_search { "query": "auth refresh flow", "project": "myrepo", "limit": 5 }

at task start, then at each settled decision:

json
memory_save { "content": "Chose cursor pagination over offset; offset scans broke past 100k rows in db/list.ts.", "concepts": "cursor-pagination, offset-scan-limit", "files": "src/db/list.ts" }

Why

Hooks capture what happened automatically. What they cannot capture is judgment: which fact mattered, which decision was settled, which correction should change future behavior. That judgment applied at the right moments is this discipline.

Workflow

  1. Task start, before reading code for any nontrivial task: memory_smart_search with the task topic and the project name. Spend the first tool call here; a hit saves rediscovery, a miss costs one call.
  2. Mid-task, the moment a decision settles or a gotcha resolves: memory_save with the decision AND the reason, 2-5 specific concepts, real file paths. Save at the moment of resolution; end-of-session batch saves lose the reasons.
  3. On user correction of your approach: save a lesson instead of a memory (the lesson skill). Lessons carry confidence and resurface before similar work; memories carry facts.
  4. Before repeating a task type you have been corrected on: memory_lesson_recall with the task type as query.
  5. Session end: stop. Hooks summarize and consolidate; a manual recap save duplicates them.

What qualifies

Save: settled decisions with reasons, non-obvious constraints discovered by debugging, environment facts not derivable from the repo. Skip: anything readable from the code, transient state, secrets, and step-by-step narration (hooks already captured it).

Anti-patterns

WRONG: finish implementing, then search memory to double-check, and batch-save a summary of everything done.

RIGHT: search first, save each decision as it settles, let hooks own the summary.

Checklist

  • First tool call on a nontrivial task was a project-scoped search.
  • Every save carries the reason, not just the conclusion.
  • Corrections became lessons, not memories.
  • Nothing saved that the repo or hooks already record.

See also

  • recall, remember: the user-invoked forms of the read and write sides.
  • lesson: the correction loop this discipline hands off to.

Troubleshooting

See ../_shared/TROUBLESHOOTING.md if memory_smart_search or memory_save is not available.

Frequently asked questions

What does the Memory Discipline AI skill do?

The session loop that makes agentmemory pay off, recall before starting work, save at decision points, learn from corrections. Use when starting a nontrivial task, after settling a decision or debugging a gotcha, or whenever deciding if something belongs in memory.

Why use Memory Discipline on TypingMind?

Because you install it once and use it with any model. Memory Discipline 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 Memory Discipline in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/rohitg00/agentmemory/tree/main/plugin/skills/memory-discipline. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Memory Discipline?

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 Memory Discipline?

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

Is the Memory Discipline AI skill free?

Yes. It is published on GitHub by rohitg00 under the Apache-2.0 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 👇