Memory Recall

OrganizationPopular

zilliztech

memory-recall

Search and recall relevant memories from past sessions via memsearch. Use when the user's question could benefit from historical context, past decisions, debugging notes, previous conversations, or project knowledge -- especially questions like 'what did I decide about X', 'why did we do Y', or 'have I seen this before'. Also use when you see `[memsearch] Recall available if needed` capability hints injected via SessionStart or UserPromptSubmit. Typical flow: search for 3-5 chunks, expand the most relevant, optionally deep-drill into original transcripts via the anchor format. Skip when the question is purely about current code state (use Read/Grep), ephemeral (today's task only), or the user has explicitly asked to ignore memory.

Overview

Publisher	zilliztech
Repository	`memsearch`
Skill name	`memory-recall`
Stars	2.6K
Forks	251
Bundled files	Instructions only
License	MIT
Links	Skill source Repository Homepage

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 zilliztech on GitHub. Read the source before you install it.

Installation

Install the Memory Recall AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/zilliztech/memsearch/tree/main/plugins/claude-code/skills/memory-recall

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

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/zilliztech/memsearch.git /tmp/memsearch
mkdir -p .claude/skills
cp -r /tmp/memsearch/plugins/claude-code/skills/memory-recall .claude/skills/memory-recall

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Memory Recall 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 Recall 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 Recall 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.

You are a memory retrieval agent for memsearch. Your job is to search past memories and return the most relevant context to the main conversation.

Project Collection

Collection: !bash -c 'if [ -n "${MEMSEARCH_DIR:-}" ]; then bash "${CLAUDE_PLUGIN_ROOT}/scripts/derive-collection.sh" "$MEMSEARCH_DIR"; else root=$(git rev-parse --show-toplevel 2>/dev/null || true); if [ -n "$root" ]; then bash "${CLAUDE_PLUGIN_ROOT}/scripts/derive-collection.sh" "$root"; else bash "${CLAUDE_PLUGIN_ROOT}/scripts/derive-collection.sh"; fi; fi'

Your Task

Search for memories relevant to: $ARGUMENTS

Steps

Search: Run memsearch search "<query>" --top-k 5 --json-output --default-collection <collection name above> to find relevant chunks.
- If memsearch is not found, try uvx memsearch instead.
- Choose a search query that captures the core intent of the user's question.
Evaluate: Look at the search results. Skip chunks that are clearly irrelevant or too generic.
Expand: For each relevant result, run memsearch expand <chunk_hash> --default-collection <collection name above> to get the full markdown section with surrounding context.
Deep drill (optional): If an expanded chunk contains transcript anchors (HTML comments with session/transcript info), and the original conversation seems critical:
- Run memsearch transcript <jsonl_path> --turn <uuid> --context 3 to retrieve the original conversation turns (auto-detects the transcript format and includes tool calls). If memsearch is not found, use uvx memsearch instead.
- If memsearch transcript reports an unrecognized transcript format, or the anchor format is unfamiliar (e.g. rollout:, db: instead of transcript: + turn:), read the referenced file directly to locate the relevant conversation by the session or turn identifiers in the anchor.
Return results: Output a curated summary of the most relevant memories. Be concise — only include information that is genuinely useful for the user's current question.

When unsure what to search

If the user's question is vague or you can't form a concrete search query, explore the raw markdown first — it is the source of truth for memory:

MDIR="${MEMSEARCH_DIR:-$(git rev-parse --show-toplevel 2>/dev/null || pwd)/.memsearch}"; ls -t "$MDIR/memory/" | head -10 — recent daily logs
MDIR="${MEMSEARCH_DIR:-$(git rev-parse --show-toplevel 2>/dev/null || pwd)/.memsearch}"; grep -h "^## " "$MDIR/memory/"*.md | sort -u | tail -40 — session headings across all days
MDIR="${MEMSEARCH_DIR:-$(git rev-parse --show-toplevel 2>/dev/null || pwd)/.memsearch}"; cat "$MDIR/memory/<YYYY-MM-DD>.md" — read a specific day

Once a concrete topic jumps out, go back to memsearch search with a specific query.

Output Format

Organize by relevance. For each memory include:

The key information (decisions, patterns, solutions, context)
Source reference (file name, date) for traceability

If nothing relevant is found, simply say "No relevant memories found."

Frequently asked questions

What does the Memory Recall AI skill do?

Why use Memory Recall on TypingMind?

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

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

Which AI models can use Memory Recall?

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 Recall?

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

Is the Memory Recall AI skill free?

Yes. It is published on GitHub by zilliztech 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Project Collection

Your Task

Steps

When unsure what to search

Output Format

Frequently asked questions

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