Memory Design logo

Memory Design

Community
nahid-sparktales
memory-design

Decide what an agent should remember, which layer holds it, who it is scoped to, and how a stale or contradicted memory is detected and retired. Use when an agent forgets something across sessions, when a memory or persistent-context feature is being designed, or when stored memories have grown noisy, wrong, or are leaking between users. Not for retrieval over a document corpus, not for prompt or context-window packing, and not for conversation transcript storage.

Overview

Publishernahid-sparktales
Repositoryagent-dispatcher
Skill namememory-design
Stars
58
Forks
5
Bundled files
1
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.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by nahid-sparktales on GitHub. Read the source before you install it.

Installation

Install the Memory Design 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/nahid-sparktales/agent-dispatcher/tree/main/skills/ai/memory-design
  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/nahid-sparktales/agent-dispatcher.git /tmp/agent-dispatcher
mkdir -p .claude/skills
cp -r /tmp/agent-dispatcher/skills/ai/memory-design .claude/skills/memory-design
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Memory Design 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 Design 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 Design 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 design

Memory is the only part of an agent that keeps being wrong after you stop looking. A retrieval bug shows up in the next answer; a stale memory sits there being quietly confident for months. Design the retirement path before the write path.

When this fires

An agent needs a fact to survive past the current context — across turns, tasks or sessions — or an existing memory store has grown noisy, contradictory, or is surfacing one subject's data to another. It does not fire for retrieval over a document corpus (that is retrieval-rag), and not for keeping a transcript: a transcript is a log, not memory.

Procedure

  1. Name the repeat failure first. What did the agent forget, how often, and what did it cost — a re-asked question, a redone decision, a wrong assumption. Memory added without a named repeat failure is speculative storage, and storage you cannot justify is storage you will not prune.
  2. Classify what is actually worth keeping, because the class decides everything after it:
    • stable facts and preferences — this user's stack, conventions, how they want to be addressed
    • decisions and their rationale — true until explicitly reversed, and the rationale is the part that matters later
    • working state — what this task is mid-way through; it dies with the task
    • derived summaries — regenerable from the source; store only when regeneration is expensive Everything else is a log. Do not promote a log to memory.
  3. Put each class in the shallowest layer that holds it. Working state belongs in a task file or thread state, not a long-term store. Stable facts and decisions belong in a durable record. If the store is a vector index, it is a retrieval system and inherits the retrieval-rag procedure, including the requirement to measure recall — unevaluated vector memory is a wish.
  4. Scope every record explicitly at write time: the subject it is about (this user, this project, this repository, this organization) and who may read it. Default to the narrowest scope. Widening a memory's scope so it crosses users or tenants is a data boundary change — raise it and get an explicit decision, do not infer permission from convenience.
  5. Write memories as assertions with provenance, not as free prose: the claim, when it was learned, what it was learned from (session, message, file, commit), and what would make it false. A memory with no source cannot be retired safely, because nothing can be checked against the thing it came from.
  6. Choose the write trigger deliberately. Writing on every turn produces noise that buries the few records that matter. The triggers that earn a write: the user says to remember, a decision is settled, a correction is issued, or a fact was expensively discovered. A user correcting the agent is the highest-value write there is — never drop one.
  7. Refuse to store secrets and sensitive personal data. Credentials, tokens, keys, payment and identity data do not go into memory, even when they appeared in the conversation. If one is already stored, stop and tell the user rather than quietly acting on it.
  8. Check for staleness at read time, not only on a schedule. Three detectors, cheapest first: contradiction — a new statement conflicts with a stored one; expiry — the record was time-boxed at write (current sprint, active branch, "for now"); reference decay — it names a file, person, project or setting that no longer exists. Verify a load-bearing memory against the live source before acting on it.
  9. Supersede rather than silently overwrite. The replacing record points at what it replaced and says why. History of a reversed decision is often more useful than the decision.
  10. Treat deletion as destructive. Retiring or clearing memory the user gave you removes something they may be relying on. Propose the retirement with the list of what would go, and ask. Never bulk-delete a memory store to fix a formatting problem.
  11. Bound the store and measure usefulness. Set a cap and an eviction rule based on when a record was last read, not when it was written. Track what fraction of stored memories have been read at all in the last N sessions; a large unread tail is cost with no return, and it is the signal to tighten the write trigger from step 6.
  12. Exercise the full loop before calling it done: write a memory, start a genuinely fresh session, confirm it is retrieved and used. Then change the underlying fact and confirm the old record is superseded rather than duplicated, and that the new answer reflects the new fact.

Checklist

  • The repeat failure this memory exists to fix is named
  • Each stored class mapped to a layer, with working state kept out of the durable store
  • Every record carries subject scope and read visibility
  • Every record carries source, timestamp, and a falsification condition
  • Write triggers are explicit; corrections are always captured
  • Secrets and sensitive personal data excluded at the write path
  • Contradiction, expiry and reference-decay checks run before a memory is acted on
  • Supersession preserves what was replaced and why
  • Deletion is proposed to the user, never performed silently
  • Cross-session round trip exercised, including the changed-fact case

Failure handling

  • Two memories contradict each other — do not pick by recency alone. Check both against the live source; if it cannot be resolved, surface the conflict to the user rather than acting on a coin flip.
  • The agent recalls a fact that is no longer true — the defect is in the retirement path, not in retrieval. Fix the expiry or contradiction detector; deleting the one bad record leaves the next one to be found by the user.
  • A memory from one subject surfaced under another — stop, treat it as a data exposure, report it with the scope that was wrong. Do not continue the feature work first.
  • The store is large and nothing is measurably better — that is a result. Report the unread fraction and propose narrowing the write trigger.
  • No durable store is available in this environment — say so, keep the fact in the task's own working state, and do not describe behaviour as persistent when it is not.

Evidence to report

The classes stored and the layer each lives in; a sample record showing scope, source, timestamp and falsification condition; the write triggers as implemented; the staleness checks that run and where they run; the cross-session round trip actually performed, including the changed-fact case, with what was recalled; and the read-rate of the store if it has been running long enough to have one. "It remembers now" without a fresh-session round trip is a claim, not evidence.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Memory Design AI skill do?

Decide what an agent should remember, which layer holds it, who it is scoped to, and how a stale or contradicted memory is detected and retired. Use when an agent forgets something across sessions, when a memory or persistent-context feature is being designed, or when stored memories have grown noisy, wrong, or are leaking between users. Not for retrieval over a document corpus, not for prompt or context-window packing, and not for conversation transcript storage.

Why use Memory Design on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/nahid-sparktales/agent-dispatcher/tree/main/skills/ai/memory-design. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Memory Design?

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 Design?

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

Is the Memory Design AI skill free?

Yes. It is published on GitHub by nahid-sparktales 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 👇