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Memory Tasks

OrganizationPopular
basicmachines-co
memory-tasks

Task management via Basic Memory schemas: create, track, and resume structured tasks that survive context compaction. Uses BM's schema system for uniform notes queryable through the knowledge graph.

Overview

Publisherbasicmachines-co
Repositorybasic-memory
Skill namememory-tasks
Stars
4K
Forks
283
Bundled files
Instructions only
LicenseAGPL-3.0
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 basicmachines-co on GitHub. Read the source before you install it.

Installation

Install the Memory Tasks 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/basicmachines-co/basic-memory.git /tmp/basic-memory
mkdir -p .claude/skills
cp -r /tmp/basic-memory/skills/memory-tasks .claude/skills/memory-tasks
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Memory Tasks 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 Tasks 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 Tasks 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 Tasks

Manage work-in-progress using Basic Memory's schema system. Tasks are just notes with type: Task — they live in the knowledge graph, validate against a schema, and survive context compaction.

When to Use

  • Starting multi-step work (3+ steps, or anything that might outlast the context window)
  • After compaction/restart — search for active tasks to resume
  • Pre-compaction flush — update all active tasks with current state
  • On demand — user asks to create, check, or manage tasks

Task Schema

Tasks use the BM schema system (SPEC-SCHEMA). The schema note lives at memory/schema/Task.md:

yaml
---
title: Task
type: schema
entity: Task
version: 1
schema:
  description: string, what needs to be done
  status?(enum, current state): [active, blocked, done, abandoned]
  assigned_to?: string, who is working on this
  steps?(array): string, ordered steps to complete
  current_step?: integer, which step number we're on (1-indexed)
  context?: string, key context needed to resume after memory loss
  started?: string, when work began
  completed?: string, when work finished
  blockers?(array): string, what's preventing progress
  parent_task?: Task, parent task if this is a subtask
settings:
  validation: warn
---

Creating a Task

When work qualifies, create a task note. Use write_note with note_type="Task" and put queryable fields in metadata:

python
write_note(
  title="Descriptive task name",
  directory="tasks",
  note_type="Task",
  metadata={
    "status": "active",
    "priority": "high",
    "current_step": 1,
    "steps": ["First step", "Second step", "Third step"]
  },
  tags=["task"],
  content="""# Descriptive task name

## Observations
- [description] What needs to be done, concisely
- [status] active
- [assigned_to] claude
- [current_step] 1

## Steps
1. [ ] First concrete step
2. [ ] Second concrete step
3. [ ] Third concrete step

## Context
What future-you needs to pick up this work. Include:
- Key file paths and repos involved
- Decisions already made and why
- What was tried and what worked/didn't
- Where to look for related context"""
)

Why both frontmatter and observations? Fields in metadata (stored as frontmatter) power search_notes with metadata_filters. Fields as observations (- [status] active) power schema_validate. Include queryable fields in both places for full coverage.

Key Principles

  • Steps are concrete and checkable — "Implement X in file Y", not "figure out stuff"
  • Context is for post-amnesia resumption — Write it as if explaining to a smart person who knows nothing about what you've been doing
  • Relations link to other entitiesparent_task [[Other Task]], related_to [[Some Note]]
  • note_types is case-sensitivewrite_note(note_type="Task") stores the type as lowercase task in frontmatter. Use note_types=["task"] (lowercase) in search queries.

Resuming After Compaction

On session start or after compaction:

  1. Search for active tasks:

    python
    search_notes(note_types=["task"], status="active")
  2. Read the task note to get full context

  3. Resume from current_step using the context field

  4. Update as you progress — increment current_step, update context, check off steps

Updating Tasks

As work progresses, update the task note:

markdown
## Steps
1. [x] First step — done, resulted in X
2. [x] Second step — done, changed approach because Y
3. [ ] Third step — next up

## Context
Updated context reflecting current state...

Update frontmatter too:

yaml
current_step: 3

Completing Tasks

When done:

yaml
status: done
completed: YYYY-MM-DD

Add a brief summary of what was accomplished and any follow-up needed.

Pre-Compaction Flush

When a compaction event is imminent:

  1. Find all active tasks: search_notes(note_types=["task"], status="active")
  2. For each, update:
    • current_step to reflect actual progress
    • context with everything needed to resume
    • Step checkboxes to show what's done
  3. This is critical — context not written down is context lost

Querying Tasks

With BM's schema system, tasks are fully queryable:

QueryWhat it finds
search_notes(note_types=["task"])All tasks
search_notes(note_types=["task"], status="active")Active tasks
search_notes(note_types=["task"], status="blocked")Blocked tasks
search_notes(note_types=["task"], metadata_filters={"assigned_to": "claude"})My tasks
search_notes("blockers", note_types=["task"])Tasks with blockers
schema_validate(noteType="Task")Validate all tasks against schema
schema_diff(noteType="Task")Detect drift between schema and actual task notes

Guidelines

  • One task per unit of work — Don't cram multiple projects into one task
  • Externalize early — If you think "I should remember this", write it down NOW
  • Context > steps — Steps tell you what to do; context tells you why and how
  • Close finished tasks — Don't leave completed work as active
  • Link related tasks — Use parent_task [[X]] or relations to connect related work
  • Schema validation is your friend — Run schema_validate(noteType="Task") periodically to catch incomplete tasks

Frequently asked questions

What does the Memory Tasks AI skill do?

Task management via Basic Memory schemas: create, track, and resume structured tasks that survive context compaction. Uses BM's schema system for uniform notes queryable through the knowledge graph.

Why use Memory Tasks on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/basicmachines-co/basic-memory/tree/main/skills/memory-tasks. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Memory Tasks?

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 Tasks?

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

Is the Memory Tasks AI skill free?

Yes. It is published on GitHub by basicmachines-co under the AGPL-3.0 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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