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Hidden-History
Hidden-HistoryCOMMUNITY
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aim-freshness-report

Scan code-pattern collections against GitHub blob data to detect stale AI memories.

41|5|Updated Jan 15, 2026
Search Tags:#ai memory#memory management#code patterns#code freshness#github sync#stale data
One-click install
npx skills add https://github.com/Hidden-History/ai-memory --skill aim-freshness-report
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: aim-freshness-report
Source: https://github.com/Hidden-History/ai-memory/tree/main/.claude/skills/aim-freshness-report
Command: npx skills add https://github.com/Hidden-History/ai-memory --skill aim-freshness-report

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This Skill addresses the issue of "AI amnesia" by identifying outdated or irrelevant information stored in the AI's memory, ensuring the AI operates with the most current and accurate context.

Core Features & Use Cases

  • Staleness Detection: Compares stored code patterns against the latest GitHub code blobs to identify memories that may no longer be accurate due to code changes.
  • Tiered Reporting: Categorizes memories into 'Fresh', 'Aging', 'Stale', and 'Expired' based on commit activity and content changes.
  • Actionable Insights: Provides specific recommendations for updating or re-capturing stale memories.
  • Use Case: A developer can run this skill to quickly identify which of their AI-assisted code snippets or architectural decisions are no longer aligned with the current codebase, preventing the AI from acting on outdated information.

Quick Start

Run the aim-freshness-report skill to scan all projects for stale memories.

Frequently Asked Questions about aim-freshness-report

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

FAQPage Schema
How do I detect stale AI memories from outdated code patterns?▼

Stale AI memories are detected by scanning stored code patterns against current GitHub code blobs to identify mismatches caused by recent code modifications. This categorizes outdated memories by freshness tiers and provides actionable update recommendations.

What do the freshness tiers for AI memory management mean?▼

Freshness tiers categorize AI memory accuracy into 'Fresh', 'Aging', 'Stale', and 'Expired' based on GitHub commit activity and content changes. This tiered reporting identifies outdated memories and provides actionable recommendations for updates.

Do I need GitHub sync enabled to check code pattern freshness?▼

Yes, GitHub synchronization must be enabled and configured to check code pattern freshness. The detection mechanism relies on comparing stored memories against the latest GitHub code blob data to identify staleness from recent code modifications.

How do I prevent AI amnesia when my codebase changes?▼

Prevent AI amnesia by scanning stored code patterns against GitHub code blobs to detect stale data after codebase changes. This identifies outdated architectural decisions and provides actionable recommendations to update the AI's context.

What is the best way to find outdated AI-assisted code snippets?▼

The best way to find outdated AI-assisted code snippets is scanning stored code patterns against current GitHub code blob data. This categorizes memories into freshness tiers and provides actionable insights to update or re-capture stale information.

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