
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.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/basicmachines-co/basic-memory --skill memory-tasks- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads memory-tasks automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
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. The schema note lives at schema/Task.md:
---
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 bm_write with note_type="Task" and put queryable fields in metadata:
bm_write(
title="Descriptive task name",
directory="tasks",
note_type="Task",
metadata={
"status": "active",
"priority": "high",
"assigned_to": "claude",
"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 bm_search with filters. Fields as observations (- [status] active) power bm_schema(command="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 entities —
parent_task [[Other Task]],related_to [[Some Note]] - Note types are normalized to snake_case —
bm_write(note_type="Task")storestype: task, andnote_types=["Task"]ornote_types=["task"]both match it.
Resuming After Compaction
On session start or after compaction:
-
Search for active tasks:
bm_search(note_types=["task"], filters={"status": "active"}) -
Read the task note to get full context
-
Resume from
current_stepusing thecontextfield -
Update as you progress — increment
current_step, update context, check off steps
Updating Tasks
As work progresses, update the task note:
## 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...
Read the task as JSON first and pass its checksum as expected_checksum on each
edit. Two sessions often update the same task, for example in a pre-compaction
flush; a revision conflict means the other one wrote first, so re-read before
writing.
Update frontmatter too, with command="set_metadata" and the metadata parameter (the body stays unchanged):
task = bm_read(identifier="tasks/descriptive-task-name", output_format="json")
bm_edit(
identifier="tasks/descriptive-task-name",
command="set_metadata",
metadata={"current_step": 3},
expected_checksum=task["checksum"]
)
Completing Tasks
When done:
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:
- Find all active tasks:
bm_search(note_types=["task"], filters={"status": "active"}) - For each, update:
current_stepto reflect actual progresscontextwith everything needed to resume- Step checkboxes to show what's done
- Context that isn't written down is lost at compaction
Querying Tasks
With BM's schema system, tasks are fully queryable:
| Query | What it finds |
|---|---|
bm_search(note_types=["task"]) | All tasks |
bm_search(note_types=["task"], filters={"status": "active"}) | Active tasks |
bm_search(note_types=["task"], filters={"status": "blocked"}) | Blocked tasks |
bm_search(note_types=["task"], filters={"assigned_to": "claude"}) | My tasks |
bm_search("blockers", note_types=["task"]) | Tasks with blockers |
bm_search(note_types=["task"], time_kind="due", valid_overlaps="[2026-10-14,2026-10-21)") | Tasks with a step due this week |
bm_schema(command="validate", note_type="Task") | Validate all tasks against schema |
bm_schema(command="diff", note_type="Task") | Detect drift between schema and actual task notes |
Due Dates
Give a step or the task a due date with a qualifier (see memory-notes), so "what's due" is one search:
- [deadline] @due:2026-10-20 Draft ready for review
A day-precision due date counts from that day onward, so a due-this-week search also returns overdue items.
Guidelines
- One task per unit of work — Don't cram multiple projects into one task
- Externalize early — write things down when you notice them
- 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
bm_schema(command="validate", note_type="Task")periodically to catch incomplete tasks
FAQ
What does the memory-tasks 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.
How do I install memory-tasks?
Run npx skills add https://github.com/basicmachines-co/basic-memory --skill memory-tasks in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/memory-tasks/ (personal) or .claude/skills/memory-tasks/ (project).
Which AI agents can use memory-tasks?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is memory-tasks safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains memory-tasks?
It is published by basicmachines-co in basicmachines-co/basic-memory. It has 1,226 recorded installs.
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Search Basic Memory notes by custom frontmatter fields with equality, range, array, and nested filters. Use when finding notes by status, priority, confidence, or any YAML field, not free text.
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