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Memory

Organization
ovh
memory

Save, organize, and curate persistent memories (MEMORY.md index plus topic files) across sessions. Load before writing detailed notes, topic files, or when asked to remember context that should outlive this session.

Overview

Publisherovh
Repositoryshai
Skill namememory
Stars
627
Forks
60
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 ovh on GitHub. Read the source before you install it.

Installation

Install the Memory 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/ovh/shai/tree/main/shai-core/skills/memory
  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/ovh/shai.git /tmp/shai
mkdir -p .claude/skills
cp -r /tmp/shai/shai-core/skills/memory .claude/skills/memory
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Memory 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 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 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

Shai keeps persistent memory as plain markdown you manage with the standard file tools plus two index tools. Memories are loaded into every future session, so they must stay small, durable, and curated.

Layout

Two scopes exist, each with the same shape:

~/.config/shai/memory/        (global — personal, cross-project)
├── MEMORY.md                 # index: one line per memory
└── <topic>.md                # detail files, one topic each

.shai/memory/                 (project — knowledge shared for this repo)
├── MEMORY.md
└── <topic>.md
  • The index (MEMORY.md) is the only thing auto-injected into the prompt. One bullet per memory: - [YYYY-MM-DD HH:MM:SS] short fact, optionally ending with a pointer like (see testing.md).
  • Topic files hold detail. They are never auto-loaded; read them with the read tool when the index line is relevant.
  • Project scope = facts about this repo (conventions, decisions, architecture notes that are not derivable from the code). Global scope = facts about the user (preferences, working style) that apply everywhere.

What to save

Save when the information would be useful in a future session and cannot be derived from the codebase, git history, or AGENTS.md:

  • Corrections the user gives you ("no, we use pnpm here", "never force-push")
  • Confirmed preferences (answer style, workflow choices)
  • Decisions and their rationale ("chose X over Y because Z")
  • External references (issue tracker, dashboards, deploy targets)
  • Ongoing work context that is not in git yet

Do not save: anything readable from the code or AGENTS.md, file paths or architecture the repo already shows, one-off debugging fixes, or session task state (that belongs in the todo list).

How to save

  1. One-line fact → memory_write (add scope only for global facts). Keep it under ~120 characters. Deduplicate first: if the index already says it, do not write it again.
  2. Detailed note → create or update a topic file with the write/edit tools. Name it after the topic (testing.md, deploy.md, user_preferences.md). Then add or update a single index line pointing at it: memory_write {"content": "Testing conventions and commands (see testing.md)"}.
  3. Never write multi-paragraph content through memory_write — it rejects newlines by design.

Curation

The index is capped (200 lines / 25KB per scope). If memory_write reports the cap, or the injected index says it is truncated, curate before adding more:

  • Remove stale or superseded facts with memory_remove.
  • Merge near-duplicates: memory_remove the variants, memory_write one consolidated line.
  • Move growing detail out of index lines into topic files, leaving a pointer.
  • Topic files can be rewritten freely with edit — keep each focused on one topic and short enough to read on demand.

Prefer updating an existing memory over stacking a new one that contradicts it.

Examples

User says "we deploy with the cds pipeline, not kubectl":

json
{"name": "memory_write"}
{"content": "Deployment goes through CDS pipelines, not kubectl (see deploy.md)"}

then write the pipeline names/links into .shai/memory/deploy.md.

User says "stop adding emojis to commit messages" (personal preference):

json
{"name": "memory_write"}
{"content": "No emojis in commit messages or answers", "scope": "global"}

An index line is now wrong:

json
{"name": "memory_remove"}
{"content": "Deployment goes through CDS pipelines"}

Frequently asked questions

What does the Memory AI skill do?

Save, organize, and curate persistent memories (MEMORY.md index plus topic files) across sessions. Load before writing detailed notes, topic files, or when asked to remember context that should outlive this session.

Why use Memory on TypingMind?

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

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

Which AI models can use Memory?

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?

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

Is the Memory AI skill free?

Yes. It is published on GitHub by ovh 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.

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