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Memory Curation

OrganizationPopular
Prismer-AI
memory-curation

When you have read / processed a workspace asset in this session and learned something durable about it, write a memory page so future sessions benefit. Maintain the workspace wiki's hierarchical structure as it grows.

Overview

PublisherPrismer-AI
RepositoryPrismerCloud
Skill namememory-curation
Stars
1.6K
Forks
14
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 Prismer-AI on GitHub. Read the source before you install it.

Installation

Install the Memory Curation 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/Prismer-AI/PrismerCloud.git /tmp/PrismerCloud
mkdir -p .claude/skills
cp -r /tmp/PrismerCloud/sdk/prismer-cloud/built-in-skills/memory-curation .claude/skills/memory-curation
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Memory Curation 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 Curation 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 Curation 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 Curation Skill

You have access to a workspace memory layer. Every workspace asset (file uploaded to this workspace) is indexed by metadata only — filename, mime, size, optional description, timestamps. The system does not pre-process file contents.

To inspect assets, use bounded asset tools:

  • prismer.asset.search to find candidate files
  • prismer.asset.describe before reading bytes
  • prismer.asset.read for explicit byte ranges only

Do not claim to have read an asset unless one of these tool calls succeeded.

Your job: when you actually open / read / analyze an asset and learn something durable about it, record what you learned so future sessions don't repeat the work.

When to write a memory page

Write a memory page after handling an asset if and only if all of these are true:

  1. You actually read the asset content (not just listed it).
  2. You produced an analysis, decision, or summary that is non-trivial — not "this file is a CSV with 1000 rows" but "this CSV is Q4 sales data with revenue collapse in region APAC".
  3. The conclusion is durable — it will still matter weeks from now.
  4. The conclusion is not derivable from the asset metadata alone or from re-reading the asset directly.

Do not write a memory page for:

  • Files you only listed / saw in a directory listing
  • Trivial summaries ("this is a PDF about marketing")
  • Conversation context that belongs in the session, not in long-term memory
  • Process artifacts: build logs, temporary downloads, cache files, scratch outputs
  • Anything the user explicitly asked you not to remember

What to write

Format: short Markdown page (typical 200-1000 words). Required structure:

markdown
# <Short descriptive title>

**Source:** [original-filename.ext](prismer://workspace/<wid>/asset/<contentHash>)

**Why this matters:** one sentence explaining the durable conclusion.

<body — your actual analysis / decision / summary>

## Provenance
- Session: <session id or date>
- Tools used: <which tools/skills you ran on this asset>

Use prismer:// URIs for any cross-reference to other assets, memory pages, tasks, etc. This is the wiki link form — agents in future sessions navigate by following these.

Where to put it

Path convention:

memory/<topic>/<source-slug>.md         ← leaf page about one source
memory/<topic>/<source-slug>/<aspect>.md ← finer-grained aspect of one source

<topic> is your judgment: datasets, customer-research, architecture, etc. If you are uncertain, default to the asset's content kind (datasets/ for tabular, documents/ for text-heavy, etc.). The Dream consolidation phase will reorganize sub-optimal placements.

Index hygiene (important)

The workspace has a top-level INDEX.md that points to memory pages. Do not write directly into INDEX.md — the system maintains it. Just write your page at the right path and the indexer will pick it up.

If you notice the top-level INDEX already has >200 entries, do not create a fresh top-level page. Instead nest under an existing sub-index (e.g. memory/datasets/INDEX.md if it exists). The Dream phase will rebalance the hierarchy when it ticks.

Hub merging

If you write a page about a topic and there is already a hub page for the same topic (check via memory_search before writing), append to or update the existing hub rather than creating a sibling. Hub merging at runtime keeps the wiki coherent and reduces work for the Dream phase later.

Deduplication

Before writing, run memory_search with terms drawn from the asset content. If a memory page already contains the same conclusion, do nothing. If a memory page contains a different conclusion about the same source, do not silently overwrite — append a note or update with an explicit reason.

What the Dream phase will do for you

You do not need to be perfect. The Dream phase runs periodically (typically idle ≥ 5 minutes or session boundaries) and will:

  • Merge duplicates you missed
  • Cluster N pages about one topic into a new sub-hub
  • Mark stale pages (when the source asset was deleted or the conclusion contradicts a newer page)
  • Prune low-signal pages that were never recalled and never linked
  • Enforce top-level INDEX size invariants

This means: if you are uncertain whether a memory is worth writing, err on the side of not writing. Dream cannot reconstruct missing knowledge, but Dream can prune excess writes.

Anti-patterns

  • ❌ Writing one memory page per asset by default. Most assets are noise; only a fraction yield durable knowledge.
  • ❌ Writing memory pages with content equal to a paste of the asset itself. Memory is for conclusions, not raw content (the raw is already in the asset).
  • ❌ Writing memory pages for files the user said to ignore.
  • ❌ Editing INDEX.md directly.
  • ❌ Creating new top-level INDEX entries when one already exists for the topic.

Anti-pattern detector (rhetorical check)

Before saving, ask yourself: "If I encounter this memory page 6 months from now in a different session, will it tell me something that re-reading the source asset would not?" If the answer is no, do not save.

Frequently asked questions

What does the Memory Curation AI skill do?

When you have read / processed a workspace asset in this session and learned something durable about it, write a memory page so future sessions benefit. Maintain the workspace wiki's hierarchical structure as it grows.

Why use Memory Curation on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Prismer-AI/PrismerCloud/tree/main/sdk/prismer-cloud/built-in-skills/memory-curation. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Memory Curation?

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 Curation?

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

Is the Memory Curation AI skill free?

Yes. It is published on GitHub by Prismer-AI 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.

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