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sync-memory

Sync new and modified marketing learnings into persistent memory for an active brand.

Updated May 18, 2026
Search Tags:#vector database#insights#checkpointing#marketing operations#memory sync#content hashing#campaign learnings
One-click install
npx skills add https://github.com/ajayatwal1105-emerson/digital-marketing-pro --skill sync-memory-ajayatwal1105-emerson
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: sync-memory
Source: https://github.com/ajayatwal1105-emerson/digital-marketing-pro/tree/main/skills/sync-memory
Command: npx skills add https://github.com/ajayatwal1105-emerson/digital-marketing-pro --skill sync-memory-ajayatwal1105-emerson

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires memory-manager.py.

What problem does it solve?

This Skill prevents valuable session insights from being lost by syncing new and changed marketing learnings into the persistent memory layer for your active brand.

Core Features & Use Cases

  • Incremental, idempotent syncing: Only stores items that are new or modified since the last sync checkpoint, avoiding duplicates.
  • Structured payload preparation: Auto-classifies content types (e.g., performance insights, campaign learnings, guidelines) and applies tags for traceability.
  • Robust batch storage with recovery: Stores items with per-item failure handling, updates sync state after completion, and queues failed hashes for retry.
  • Use case: Before closing a workday, sync performance insights plus campaign retrospective learnings so future brand engagements start with the most recent, auditable history.

Quick Start

Run "/digital-marketing-pro:sync-memory" to batch sync all new and modified insights and campaign learnings into persistent memory for the active brand.

Frequently Asked Questions about sync-memory

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

FAQPage Schema
How do I persist marketing campaign learnings across sessions without creating duplicates?▼

You can persist campaign learnings by using incremental, idempotent syncing that computes SHA-256 diffs to store only new or modified items since the last checkpoint, preventing duplicate entries.

What is the best way to checkpoint performance insights into a vector database at the end of a workday?▼

Batch syncing performance insights and campaign data into a connected vector memory service updates your active brand's persistent layer, ensuring future engagements start with recent, auditable history.

Can I selectively sync only campaign data instead of all session insights?▼

Yes, you can selectively sync insights-only or campaigns-only data during ongoing workflows, preparing typed storage payloads that auto-classify content types like performance insights and guidelines.

How does content hashing handle failures when syncing marketing operations data?▼

Robust batch storage handles per-item failures during marketing operations syncing by updating sync-state.json with detailed reporting and queuing failed hashes for retry.

Do I need a memory manager dependency to sync session insights?▼

Yes, syncing session insights requires loading the memory-manager.py dependency to establish active brand context and sync state before writing to the vector memory service.

When should I not use incremental sync for campaign learnings?▼

Avoid incremental sync if your connected vector memory service is unavailable, as the process requires writing typed storage payloads and updating sync-state.json to successfully checkpoint data.

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