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Memory Search

Community
tashfeenahmed
memory_search

Search the user's long-term memory for stored facts and past context. Use when the user references something personal, past conversations, or people/projects by name. Do NOT use for current events, news, or real-time info — use bash with web-search for that.

Overview

Publishertashfeenahmed
Repositoryscallopbot
Skill namememory_search
Stars
56
Forks
9
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 tashfeenahmed on GitHub. Read the source before you install it.

Installation

Install the Memory Search 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/tashfeenahmed/scallopbot/tree/main/src/skills/bundled/memory_search
  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/tashfeenahmed/scallopbot.git /tmp/scallopbot
mkdir -p .claude/skills
cp -r /tmp/scallopbot/src/skills/bundled/memory_search .claude/skills/memory_search
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Memory Search 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 Search 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 Search 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 Search Skill

Search through stored memories using hybrid search combining BM25 keyword matching and semantic similarity. Returns ranked results with relevance scores.

When to Use

Use the memory_search skill for:

  • Finding user facts: Recall what you've learned about the user
  • Searching preferences: Find stored user preferences
  • Retrieving context: Get relevant past conversation context
  • Subject-specific queries: Find facts about specific people (user, friends, family)

Input Format

The skill accepts JSON arguments via the SKILL_ARGS environment variable:

json
{
  "query": "what does the user like to eat"
}

Parameters

ParameterTypeRequiredDescription
querystringYesSearch query - keywords, phrases, or natural language questions
typestringNoFilter by memory type: raw, fact, summary, preference, context, or all. Default: fact
subjectstringNoFilter to facts about specific person (e.g., "user", "Bob", "John")
limitnumberNoMaximum results to return (default: 10, max: 50)

Memory Types

  • fact (default): Extracted facts about the user or others
  • preference: User preferences and settings
  • raw: Unprocessed conversation data
  • summary: Condensed summaries
  • context: Processed contextual information
  • all: Search all memory types

Output Format

The skill returns JSON to stdout:

json
{
  "success": true,
  "output": "Found 3 memories:\n\n--- Result 1 (score: 0.850) ---\nContent: User enjoys Italian food...",
  "exitCode": 0
}

Response Fields

FieldTypeDescription
successbooleanTrue if search completed successfully
outputstringFormatted search results or error message
errorstringError message if search failed
exitCodenumberProcess exit code (0 = success)

Examples

Basic fact search

json
{ "query": "user's favorite food" }

Search with subject filter

json
{ "query": "birthday", "subject": "user" }

Search all memory types

json
{ "query": "conversation about work", "type": "all", "limit": 20 }

Search for preferences

json
{ "query": "notification settings", "type": "preference" }

Search Behavior

  • Hybrid search: Combines BM25 keyword matching with semantic similarity
  • Default type: Searches "fact" type by default (not raw logs)
  • Natural activation: Fresh memories surface easily; old memories fade but can return from a genuinely relevant topic/name without special history wording
  • Real reinforcement: User confirmation strengthens memory; automatic retrieval does not
  • User boost: Facts about the user get 1.5x score boost
  • Relevance scoring: Results sorted by combined relevance score

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 Search AI skill do?

Search the user's long-term memory for stored facts and past context. Use when the user references something personal, past conversations, or people/projects by name. Do NOT use for current events, news, or real-time info — use bash with web-search for that.

Why use Memory Search on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/tashfeenahmed/scallopbot/tree/main/src/skills/bundled/memory_search. 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 Search?

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 Search?

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

Is the Memory Search AI skill free?

Yes. It is published on GitHub by tashfeenahmed 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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