auto-bean-memory

Name: auto-bean-memory
Availability: InStock
Author: SamArgt

Manage durable memory records in auto-bean fixed memory stores with structured metadata.

1|Updated Mar 28, 2026

Search Tags:#governance #audit-trail #memory-management #beancount #memory-records #import-workflow

Ready to run

npx skills add https://github.com/SamArgt/auto-bean --skill auto-bean-memory

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: auto-bean-memory
Source: https://github.com/SamArgt/auto-bean/tree/main/skill_sources/auto-bean-memory
Command: npx skills add https://github.com/SamArgt/auto-bean --skill auto-bean-memory

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Read this reference before acting: .agents/skills/shared/memory-access-rules.md Use this skill when the user asks to inspect, review, explain, correct, prune, reorganize, or persist reusable auto-bean memory.

Core Features & Use Cases

Memory is durable advisory context for future workflows. Keep it small, reviewable, and tied to evidence. Use judgment to decide whether the task is an inspection, cleanup, correction, or persistence request, and keep the response focused on the user's goal.

Reusable memory records across import, categorization, writing, and review workflows to guide future decisions.
Prune or reorganize memory while preserving provenance and audit context.
Persist reusable decisions to fixed memory files with structured metadata for auditability.

Quick Start

Inspect and manage governance memory for the current workspace by invoking the memory governance skill.

Frequently Asked Questions about auto-bean-memory

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I manage reusable beancount memory records across ledger workflows?▼

To manage reusable beancount memory records, inspect, review, and persist durable advisory context tied to import and categorization workflows. This skill stores updates with deterministic JSON formatting, preserving provenance and audit context for future ledger decisions.

What is the best way to add audit trail context to fixed memory stores?▼

Adding audit trail context to fixed memory stores requires persisting decisions with structured metadata fields like schema_version, memory_type, source, and review_state. This approach ensures governance and auditability across workspace memory records.

How do I prune memory records without losing provenance in my ledger workspace?▼

Pruning memory records without losing provenance involves reorganizing durable advisory context while preserving the audit fields and source history. This skill maintains structured metadata and deterministic formatting to keep memory small, reviewable, and tied to evidence.

What schema fields are required to persist reusable memory decisions for categorization?▼

Persisting reusable memory decisions for categorization requires satisfying schema_version, memory_type, source, decision, scope, confidence, review_state, created_at, updated_at, and audit fields. These fields ensure deterministic JSON formatting and provenance context.

When do I need to review fixed memory store records in auto-bean workflows?▼

Reviewing fixed memory store records is necessary when inspecting, correcting, or reorganizing durable advisory context created by import, categorization, naming conventions, and related governance actions across ledger workspaces.