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memory-tiering

Organize AI memory into hot, warm, and cold tiers.

2|Updated Mar 31, 2026
Search Tags:#context#ai#memory#archiving#pruning#tiering
One-click install
npx skills add https://github.com/Wike-CHI/acquisition-agent --skill memory-tiering-wike-chi
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: memory-tiering
Source: https://github.com/Wike-CHI/acquisition-agent/tree/main/skills/memory-tiering
Command: npx skills add https://github.com/Wike-CHI/acquisition-agent --skill memory-tiering-wike-chi

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Managing AI context becomes challenging as conversations grow, leading to token limits and loss of important information. This skill introduces a structured, three‑tier memory system that automatically organizes, prunes, and archives data to keep the hot context lightweight while preserving valuable knowledge.

Core Features & Use Cases

  • Three‑Tier Architecture: HOT for immediate session data, WARM for stable user preferences and configurations, COLD for long‑term archives.
  • Organize‑Memory Workflow: Ingests all tiers, audits dead context, redistributes information, prunes details, and verifies integrity.
  • Use Case: After a lengthy sales outreach campaign, run the skill to move completed interaction details to COLD, keep current prospects in HOT, and store recurring client preferences in WARM.

Quick Start

Run memory tiering to reorganize the HOT, WARM, and COLD memory layers now.

Frequently Asked Questions about memory-tiering

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

FAQPage Schema
How do I manage AI context limits when long conversations exceed token limits?▼

To manage AI context limits, you can apply a tiered memory system that automatically prunes and archives older context into hot, warm, and cold tiers to keep the active session lightweight.

What is the best way to archive AI session context without losing important information?▼

The best way to archive AI session context is using a three-tier architecture that moves completed interaction details to cold long-term archives while preserving valuable knowledge in warm tiers.

How does tiered memory redistribution work for dynamic AI conversations?▼

Tiered memory redistribution works by ingesting all memory layers, auditing dead context, moving stable preferences to warm storage, and verifying data integrity to maintain an efficient hot context.

Can I safely handle credentials when pruning AI memory layers?▼

Yes, you can safely handle credentials during memory pruning by applying a tiered system designed to manage context redistribution while ensuring raw secrets are never exposed.

When do I need to run context summarization and tiered prioritization for AI?▼

You need to run context summarization and tiered prioritization when a lengthy session causes context to exceed limits, requiring dynamic redistribution across hot, warm, and cold memory tiers.

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