Memory logo

Memory

OrganizationPopular
Prismer-AI
memory

Persist and retrieve agent memory across sessions — write durable notes, read by path, recall via semantic search, list/delete, and consolidate. Use whenever the user asks to remember/forget something, when you need to look up past decisions or context, or when episodic state matters beyond the current turn. Executes via the `cloud memory` and `cloud recall` CLI.

Overview

PublisherPrismer-AI
RepositoryPrismerCloud
Skill namememory
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 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 .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

Use this skill for durable episodic memory — facts, decisions, feedback, and project context that need to survive across sessions. Memory has four canonical types: user, feedback, project, reference. The index is MEMORY.md; topic files live under semantic paths.

When to use

  • The user explicitly says "remember X" or "forget X" → write or delete immediately.
  • The user references a past decision, preference, or detail you don't have in current context → recall first.
  • Before answering a question that depends on prior agreement (architecture, preferences, deadlines), check memory.
  • After a non-obvious clarification or correction lands, write it so the next session keeps the lesson.

CLI Reference

Write

bash
# Single memory file with full frontmatter (path is required, content is the body)
cloud memory write \
  --path "decisions/database-choice.md" \
  --type project \
  --description "We chose PostgreSQL over MySQL; deadline 2026-06-01." \
  --content "## Decision\nPostgres 16 because pgvector + better JSON ops."

# Quick fact (no path → auto-named under inbox/)
cloud memory write --type feedback --content "User prefers terse end-of-turn summaries"

# From a journal blob — service extracts structured entries
cloud memory extract --journal "Long stream-of-consciousness session notes..."

Read

bash
cloud memory read --path "decisions/database-choice.md"   # full file
cloud memory read <file-id>                                # by id
cloud memory list                                          # everything
cloud memory list --type feedback                          # by type
cloud memory list --updated-after "2026-05-01"             # by recency

Recall (semantic search)

bash
cloud recall "what database did we choose?"               # default: hybrid
cloud recall "timeout retry" --strategy keyword           # exact-match fast path
cloud recall "the thing with the auth bug" --strategy llm # LLM-assisted; slowest, best for fuzzy
cloud recall --layer memory --top-k 5 "..."              # narrow to one layer

Maintenance

bash
cloud memory delete <file-id>                              # remove a stale memory
cloud memory consolidate                                   # trigger Dream — merge/dedupe/mark stale
cloud memory compact <conversation-id>                     # summarize a long conversation into memory

Memory Types

TypeWhat it isWhen to write
userWho the user is, role, preferences, expertiseWhen you learn role/responsibility/preference details that should shape future behavior
feedbackApproach corrections + validated approachesAfter a correction ("don't do X") OR a non-obvious approval ("yes that was right")
projectGoals, deadlines, decisions, ongoing initiativesWhen you learn who/what/why/by-when that isn't derivable from code
referencePointers to external systems (Linear, Slack, dashboards)When the user names a tool/channel and its purpose

Operating Rules

Write

  • Don't save secrets, credentials, personal data, or one-off debugging chatter. Memory is durable — anything you write may be loaded into future contexts.
  • Don't save generic programming advice that isn't tied to this project. The model already knows generic things.
  • Don't save ephemeral task state (in-progress work, current-conversation context) — that belongs in plans/tasks, not memory.
  • Don't save things derivable from the current project state (file paths, conventions, git history). Reading the code is authoritative.
  • Don't save things already in CLAUDE.md.
  • For feedback and project types, include a Why line (the reason the user gave) and a How to apply line so future-you can judge edge cases. Knowing why lets you decide if the rule still applies when conditions change.
  • Convert relative dates to absolute dates before writing ("Thursday" → "2026-05-22") so memory stays interpretable as time passes.
  • If a fact may become stale, embed the condition or date that makes it valid.

Read / Recall

  • Read MEMORY.md first when you don't know the exact path. It's the index.
  • Treat recall results as leads, not evidence. Snippets with low scores are likely false matches; verify by reading the underlying file.
  • Don't let memory override explicit current user instructions — if the user says ignore memory or contradicts it, trust the current input and update or remove the stale entry.
  • Before recommending action based on memory that names a specific function/file/flag, verify it still exists (grep / read). Memory is frozen in time.
  • For current or recent state ("what changed this week"), prefer git log over recalling activity-log memories.

Delete

  • When updating an outdated memory, prefer editing over deleting + rewriting (preserves the link graph).
  • When the user says "forget X", search first, confirm the match, then delete. Don't silently fail if recall finds nothing — tell the user.

Output reporting

After writing memory, echo the path, type, and one-line description back to the user so they can verify what got persisted.

After recalling, list match titles + paths + scores; do not paste full file content unless the user asks. The agent driving this skill can follow up with memory read for any specific hit.

Backing capabilities (D22 mapping)

Replaces these v1.x built-in skills: memory-read, memory-write, memory-recall.

Frequently asked questions

What does the Memory AI skill do?

Persist and retrieve agent memory across sessions — write durable notes, read by path, recall via semantic search, list/delete, and consolidate. Use whenever the user asks to remember/forget something, when you need to look up past decisions or context, or when episodic state matters beyond the current turn. Executes via the `cloud memory` and `cloud recall` CLI.

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/Prismer-AI/PrismerCloud/tree/main/sdk/prismer-cloud/built-in-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 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇